buttonscli.com - buttons, AI help and automation, AI control / AI can create new tabs, type commands, use multiple at the same time, full user interface, doesn't force you to memorize lots of key combos just to do simple things

i'm just testing this new blog system

Providers are hiking token prices while slashing free credits and allowances—GitHub Copilot being a prime example, where users now burn through entire monthly quotas on single tasks that previously consumed a fraction.

Luckly, there are still plenty left.

Here is some more free APIs and more coding agents (that seem actually good)

✅ FREE Coding Agents, APIs

Poolside

What: Coding agent + API
Link: poolside.ai/get-started
Status: Currently free

I Use their CLI agent to update docs automatically while I worked on other things. Solid so far.

Mistral Vibe CLI

What: CLI coding assistant
Link: mistral.ai - look for the Vibe CLI, it has higher rate limits vs the other stuff

Install:

curl -LsSf https://mistral.ai/vibe/install.sh | bash

Status: Free up to a request/time cap

Common knock on Mistral: "it sucks." My experience: replies instantly, handles terminal commands, automations, and scripting just fine. Perfectly adequate for lightweight tasks.

Nvidia NIM

What: Free hosted models with rate limits
Link: build.nvidia.com/nvidia
Status: Free (rate-limited)

Haven't stress-tested the limits yet.

Tool I built: An endpoint liveness checker — paste an OpenAI-compatible /v1/models URL (optional key), and it pings every model to log which ones respond and when. Useful for figuring out if a "free" resource is actually reliable enough to use. (Buggy right now, fix coming soon — don't use real keys yet.)

🔗 extra.wuu73.org/chu5

Opencode Zen & Go models: Some may work without an API key. If not, one key covers both Zen and Go — free models, zero cost. Opencode Go is a coding plan/subscription for $5/$10, I used up my entire alotment in like one week though.. with lite use

💵 CHEAP - All close to or less than $1/M

Minimax (M3 / 2.7 / 2.5) — API is extremely reliable. When I had a sub, even the lowest tier let me run tons of subagents without hitting limits. Prices may have increased; re-evaluating API vs. subscription.

Deepseek v4 — Free flash models using Opencode Zen's free models and some other ways like thru Cline, Kilo Code endpoints. Cheap pro/flash. Reasonix CLI agent works well! I am using it a lot.

StepFun Flash 3.7 — Inexpensive, strong at tool-use and agentic workflows.

Arcee AI Trinity -- inexpensive models, USA based. All less than a dollar input/output per M tokens. Good with agentic tools.

🗂️ Coding Plan Picks

• Minimax → ⭐ Best option (if pricing/limits haven't changed)
• Opencode Go → Ran out in ~1 week. Raw API + free models is probably cheaper.

🤖 New Agent Harnesses

• Reasonix — specifically for Deepseek v4 — it is good! Been using it a lot
• Mistral Vibe — higher rate limits for free, haven't ran into limits yet
• Poolside Pool — totally free to use right now! Works good, might not be the most intelligent, but great for creating docs and doing tasks

🧰 Misc Tools

Kilo Code

VS Code extension with plenty of free models available all the time. You can even use its API endpoints in other tools/apps.

CLIProxyAPI

A tool that lets you stack providers for fallbacks and convert between OpenAI and Anthropic style endpoints, so you can use any model in Claude Code. I tried and tested this thing well — no issues with formatting the API correctly, no errors in Claude Code.

Buttons CLI

buttonscli.com — This terminal app can be controlled by AI coding agents via MCP or CLI, letting agents control multiple terminal tabs while you watch what they're doing (which is hard when using them in terminals since it only shows short summaries). Has agent stalling detection and nudging to get back to working.

This blog audits the code that is actually present in coding-agents\: how each agent wires models, exposes tools, runs shell commands, speaks MCP or ACP, and where Claude Code and Hermes feel fundamentally different from the rest of the field — plus Oh My Pi's hashline-heavy Bun + Rust fork of Pi Mono, OpenCode's client-server runtime, Reasonix's exact-match edit gate, ADK-Rust's feature-gated Rust framework stack, CheetahClaws's Python-native daemon-plus-kernel architecture, and OpenAI's own Codex CLI, a Rust-native agent with platform-specific sandboxes and bidirectional MCP support.

Shameless PlugWhile we're deep-diving into complex CLI tools... let's be honest. Are you tired of pretending you enjoy memorizing 400 arcane tmux key combos? Do you lie awake at night dreading the moment you have to edit a cryptic dotfile just to change a font size? Same. It's the AI age, we shouldn't be computing like it's 1995.ButtonsCLI: The terminal for

(Alright, ad over. Back to the serious technical analysis.)

What this deep dive covers

The interesting part of coding agents is not the marketing layer. It is the runtime beneath it: whether the tool layer is generic or bespoke, whether shell access is tightly guarded or casually wrapped, whether model support is truly abstracted or just superficially multiplexed, and whether the repo reads like a productized operating environment or a fast-moving integration shell.

I read the local repositories for Pochi, Neovate Code, Mux, Crush, Kimi CLI, Qwen Code, OpenHands, Claude Code, DeerFlow, Hermes Agent (by Nous Research), Pi Mono, Oh My Pi, OpenCode, Codex CLI (by OpenAI), Wintermolt, Zaica, Goose, Dirac, Reasonix, CodeWhale, CheetahClaws, and Open Claude Code 2.0 (a clean-room implementation via AI decompilation), then mapped them against the same questions on every page of this site.

🔍

Methodology note

This is a local-only repo study. I did not use outside documentation beyond what is already checked into the worktree. That matters most for OpenHands, where the repo itself says the newer V1 agent core now lives elsewhere.

Repos audited

• Pochi - TypeScript monorepo with vendor-specific model adapters and built-in agents.
• Neovate Code - TypeScript CLI with AI SDK providers, MCP, and hardened bash tooling.
• Mux - large TypeScript desktop/browser agent platform with workspaces and provider routing.
• Crush - Go-based terminal product with custom tools, permissions, and provider metadata plumbing.
• Kimi CLI - Python terminal agent focused on Kimi platform flows plus ACP and MCP bridges.
• Qwen Code - Gemini CLI descendant with strong config resolution, declarative tools, and MCP lifecycle management.
• OpenHands - platform/runtime repo with sandbox and legacy CodeAct agent pieces still present locally.
• Claude Code - deeply integrated Bun + React/Ink runtime centered on Anthropic models and a huge internal tool surface.
• DeerFlow - LangGraph-based super-agent harness with middleware, subagents, SSE streaming, and config-driven model factories.
• Hermes Agent (Nous Research) - Python agent with persistent skill learning, 14+ messaging platform gateways (Telegram, Discord, Slack, WhatsApp, Signal, WeChat, Matrix, Mattermost, Feishu, DingTalk, Email, SMS, HomeAssistant, Webhook), MoA synthesis, 6 execution backends, and RL training infrastructure.
• Codex CLI (OpenAI) - Rust-native coding agent (3,805 files, 70+ crates) with macOS Seatbelt, Linux bubblewrap/Landlock, Windows sandbox, MCP client and server, multi-agent spawning, OpenAI Responses API plumbing, configurable TOML config, IDE extensions, and a Ratatui TUI.
• Pi Mono - TypeScript minimalist harness (874 files) with tree-structured JSONL v3 sessions, differential TUI rendering, Pi Packages (shareable bundles via npm/git), 26 providers across 10 APIs, parallel tool execution, file mutation queue, 4 run modes, MIT license, author Mario Zechner.
• Oh My Pi - Bun + TypeScript + Rust fork of Pi Mono (v14.7.8, MIT) with default hashline editing, a Rust native engine, MCP and plugin discovery, LSP, Python, browser tooling, task/subagent execution, and a dedicated edit-benchmark package.
• OpenCode - TypeScript/Bun client-server coding runtime (4,531 files) with a SolidJS opentui terminal UI, web and desktop clients, apply_patch plus exact-string editing, wildcard permissions, worktrees, MCP, ACP, skills, and an SDK.
• ADK-Rust - Rust framework workspace (v0.8.0, Apache-2.0, 34 workspace members) with feature-tiered crates, a minimal LLM trait, provider-native Anthropic/OpenAI tool wrappers, graph workflows, MCP integration, A2A APIs, AWP deployment support, and optional browser/realtime/sandbox modules.
• Wintermolt - Zig 0.15 native binary (3 MB, zero runtime) with 6 AI backends, 16 tools, cron scheduling, Tailscale mesh, camera vision, browser automation, MCP bidirectional, chat bridges to 4 platforms, and a macOS menu bar app.
• Zaica - Zig 0.15 focused coding agent (~9,100 lines, zero runtime) with multi-provider LLM support, chain-mode structured workflows, parallel sub-agent dispatch, reactive state management (zefx), and Wyhash-based loop detection.
• Goose (AAIF/Linux Foundation) - Rust-native AI agent (v1.32.0, Apache-2.0) with 15+ providers, 5-layer security inspector stack, LLM-based AdversaryInspector, 4 GooseModes (Auto/Approve/SmartApprove/Chat), extension system, recipe framework, and MOIM injection.
• Open Claude Code 2.0 - Clean-room implementation of Claude Code via AI-powered decompilation (1,581 tests, async generator architecture, multi-agent teams, git worktree isolation, 7-type hook system).
• Dirac - TypeScript fork of Cline with hash-anchored parallel edits, AST-native precision, multi-file batching, 64.8% cost reduction, no MCP, hook system, git checkpoints, state mutex, and 40+ provider support. 8/8 on TerminalBench 2.0 evals.

The landscape in one screen

🧠

Bespoke runtime products

Claude Code and Crush feel like full terminal operating environments, not thin wrappers. Their tool, permission, and UX layers are part of the product, not just adapters around a chat loop.

Most opinionated🔌

Provider multiplexers

Mux, Neovate, and Qwen Code all build serious provider catalogs and shared abstractions. They want broad model reach more than a single model-native identity.

Most configurable🧩

Protocol and adapter layers

Pochi and Kimi CLI stand out for their ecosystem bridges. Pochi ships vendor-specific packages for Codex, Qwen, Copilot, and others. Kimi invests heavily in ACP and in translating internal tool output into protocol-friendly shapes.

Most bridge-heavy🏗️

Client-server coding runtimes

OpenCode feels less like a single CLI and more like a shared runtime: one backend powering TUI, web, desktop, SDK, MCP, ACP, skills, worktrees, and a permission bus. It is the clearest open-source example here of a terminal agent growing into a platform.

Most runtime-shaped🧱

Frameworks and execution harnesses

DeerFlow, OpenHands, and ADK-Rust are less about one CLI persona and more about reusable orchestration environments. DeerFlow and OpenHands lean into services and sandboxes. ADK-Rust pushes furthest toward a publishable crate ecosystem with feature tiers, graph workflows, provider-native tools, A2A, AWP, and payment adapters.

Most framework-shaped

The Pi lineage: kernel to power-tool fork

Pi Mono is still the sharp minimalist kernel: tree-structured JSONL sessions, differential TUI rendering, Pi Packages, and a tight extension-first philosophy. Oh My Pi takes that lineage in the opposite direction with Bun workspaces, Rust native modules, default hashline edits, MCP, plugins, browser tooling, a task tool, and an edit benchmark lab.

Most visible fork split

Self-improving multi-platform agents

Hermes Agent (Nous Research) is in a category of its own: a persistent skill-learning loop, six remote execution backends, 14+ messaging platform gateways (Telegram, Discord, Slack, WhatsApp, Signal, WeChat, Matrix, Mattermost, Feishu, DingTalk, Email, SMS, HomeAssistant, Webhook), MoA synthesis, and RL training infrastructure.

Most functionally unique

Zero-runtime native binaries

Wintermolt and Zaica are both written entirely in Zig 0.15 — no Node.js, no Python, no garbage collector. They compile to single native binaries (Wintermolt is 3 MB) with cross-compilation to any Zig target including ARM boards. Wintermolt goes wide (7 modes, cron, Tailscale, camera, browser, MCP, chat bridges). Zaica goes deep (chain-mode workflows, reactive state, Wyhash loop detection).

Most portable⚡

OpenAI's own coding agent

Codex CLI is OpenAI's production coding agent — a Rust workspace of 70+ crates with platform-specific sandboxes (Seatbelt, bubblewrap/Landlock, Windows restricted tokens), bidirectional MCP (client and server), multi-agent job execution, and a configurable provider system that supports Ollama and LM Studio.

Most sandboxed

Extension-first security advocates

Goose (AAIF at Linux Foundation) takes a unique approach to security: it uses an LLM-based AdversaryInspector that fires a second LLM call to review tool calls against user-defined rules from ~/.config/goose/adversary.md. This is defense-in-depth for multi-agent setups where parent agents delegate to sub-agents. With 15+ providers, 4 GooseModes (Auto/Approve/SmartApprove/Chat), and a recipe framework, Goose is Rust-native with extensive feature gates (local-inference, aws-providers, otel).

Most LLM-secured

Fast takeaways

Approximate codebase size by file count

File count is not the same thing as quality, but it does reveal where the implementation surface is broadest.

ADK-Rust is intentionally omitted here: its 34-member framework workspace and extracted companion repos make raw file-count comparison less useful than with single-product CLIs.

OpenCode

4531 files

Codex CLI

3805 files

OpenHands

2774 files

Mux

2226 files

Claude Code

2137 files

Qwen Code

2038 files

Pochi

1315 files

Kimi CLI

899 files

DeerFlow

810 files

Crush

799 files

Neovate

582 files

Hermes

~450 files

Pi Mono

874 files

Wintermolt

51 Zig files (~18,400 lines)

Zaica

~13 files (~9,100 lines)

Open Claude Code 2.0

61 files (~8,300 lines)

Goose

Rust Cargo workspace (~6+ crates)

Dirac

TypeScript monorepo (fork of Cline)

My high-level verdict

Best designed, if you value a coherent product runtime

Claude Code is the standout. It is not the most provider-flexible repo, but it is the clearest example of an agent built as its own operating model: tool schemas, permissioning, commands, tasking, worktrees, UI, feature flags, and retry logic all sit inside one deliberate runtime.

Best designed, if you value clean systems engineering

Crush is the nicest surprise. The Go codebase feels disciplined, modular, and product-minded without being bloated. Its provider plumbing, permissions, and TUI organization are easier to reason about than many faster-moving TypeScript peers.

Best multi-model architecture

Mux and Qwen Code lead here. Mux has a broad provider routing layer with desktop app ambitions, while Qwen Code has a particularly strong configuration and runtime model-resolution story.

Most extensible framework shape

DeerFlow wins on composability. It feels more like a harness for building agent systems than a single agent persona, which makes it powerful but also less opinionated than Claude Code or Crush.

Most publishable framework ecosystem

ADK-Rust. The repo is built as a crate ecosystem, not just a runnable app: minimal-by-default packaging, typed tools, workflow agents, graph orchestration, A2A/AWP surfaces, and an honest stability file that separates mature crates from frontier modules.

Most functionally unique

Hermes Agent by Nous Research. The self-improving skill loop, 14+ messaging platform gateways, MoA tool (4 frontier models in parallel), and RL training infrastructure are not features in any other repo here. It is the only agent that explicitly tries to get better at your tasks over time.

Most portable — zero runtime, one binary

Wintermolt and Zaica are the only agents here that compile to a single native binary with zero runtime dependency. Wintermolt (3 MB, 18,400 lines) is the most ambitious agent in any language. Zaica (~9,100 lines) is the most focused coding specialist with chain-mode workflows and best-in-class loop detection.

Most extension-friendly kernel

Pi Mono by Mario Zechner. A tight 874-file TypeScript kernel with tree-structured JSONL v3 sessions, differential TUI rendering, 26 providers across 10 APIs, Pi Packages (shareable bundles via npm/git), parallel tool execution, a file mutation queue, and 4 run modes. MIT licensed and deliberately minimal so you can build MCP, permissions, or sub-agents yourself.

Most serious edit-lab fork

Oh My Pi. It starts from Pi Mono's terminal-agent kernel and then turns editing into a research surface: default hashline mode, prompt/runtime anchor helpers, a native engine, MCP and plugin plumbing, built-in task execution, and benchmark infrastructure dedicated to edit variants.

Most complete open-source runtime

OpenCode. The repo combines a terminal UI, browser console, desktop shell, SDK, MCP, ACP, worktrees, skills, and a permission bus behind one backend runtime. It is the clearest open-source "agent platform" in this snapshot.

Most security-conscious sandboxing

Codex CLI by OpenAI. Three platform-specific sandbox implementations (macOS Seatbelt, Linux bubblewrap/Landlock, Windows restricted tokens), split-filesystem awareness, an execution policy engine with a rule DSL, bidirectional MCP (client and server), and a strict clippy lint policy that bans unwrap_used and expect_used across 70+ crates.

How to read the rest of this site

1Architecture

Compare tool schemas, shell execution, MCP support, and recovery patterns.

↓2Agents

Read per-repo profiles, strengths, weaknesses, and fit.

↓3Models

See who is genuinely provider-neutral and who writes model-specific logic.

↓4Claude Code

The dedicated page on why Claude Code feels like a category of its own.

↓5DeerFlow

The LangGraph-based super agent harness with 14-layer middleware, skill evolution, and sub-agent orchestration.

↓6OpenHands

The platform-shaped agent with Docker sandboxing and ingenious temperature-bumping retry logic.

↓7Security

Deep dive into shell injection defense, prompt injection scanning, permissions, sandboxing, and loop detection.

↓8Protocols

MCP and ACP implementation compared — transports, OAuth, lifecycle, and deferred tool loading.

↓9Subagents

How agents delegate work, isolate children, enforce concurrency limits, and collect results.

↓10Hermes Agent

The completely separate deep dive on the most unusual agent in the set — self-improving, multi-platform, and RL-augmented.

↓11Pi Mono

The minimalist kernel — 874 files, tree-structured JSONL v3 sessions, differential TUI, Pi Packages, 26 providers across 10 APIs, parallel tool execution, file mutation queue, 4 run modes, MIT licensed.

↓12OpenCode

The client-server runtime — SolidJS terminal UI, web and desktop clients, apply_patch, wildcard permissions, ACP, MCP, worktrees, and skills.

↓13ADK-Rust

The Rust framework workspace: 34 member crates, feature tiers, provider-native tool wrappers, graph workflows, A2A, AWP, and optional sandbox/browser/realtime modules.

↓14Codex CLI

OpenAI's production agent: 3,805 files, 70+ Rust crates, three platform-specific sandboxes, bidirectional MCP, multi-agent jobs, and IDE extensions.

↓15Wintermolt

The 3 MB everything-agent: 6 backends, 16 tools, cron, Tailscale, camera, browser, MCP, chat bridges, and a macOS menu bar app.

↓16Zaica

The focused specialist: chain-mode workflows, reactive state management, Wyhash loop detection, and a hand-crafted terminal REPL.

↓17Goose

The extension-first Rust agent: LLM-based AdversaryInspector, 4 GooseModes, 15+ providers, recipe framework, and MOIM injection.

↓18Zig Agents

Head-to-head comparison: two agents, one language, opposite philosophies — platform vs. specialist, 18,400 lines vs. ~9,100.

↓19Open Claude Code

Clean-room rebuild of Claude Code v2.1.91 via ruDevolution decompilation: async generator loop, 25 tools, 5 providers, nightly releases.

→20Dirac

Hash-anchored parallel edits, AST-native precision, 64.8% cost reduction vs competitors, no MCP, 8-type hook system, git checkpoints.

→21Reasonix

DeepSeek-native coding agent with byte-exact SEARCH/REPLACE, edit-gate review, repair stages, and strict sandbox enforcement.

→22CodeWhale

DeepSeek-first Rust agent with a constitution prompt, durable task manager, persistent subagents, runtime APIs, and a transactional edit stack.

→23CheetahClaws

Python-native multi-provider agent with a plugin registry, daemon server, capability-gated kernel, MCP plumbing, and a mixed direct-write plus diff editing stack.

How do AI coding assistants actually edit your files? Discover the strategies, matching algorithms, fallback mechanisms, and provider-native, exact-match, capability-gated, or anchor-native editing contracts behind Cline, Codex, OpenCode, ADK-Rust, Reasonix, CodeWhale, CheetahClaws, Oh My Pi, Aider, Crush, DeerFlow, Dirac, Goose, OpenHands, Pochi, Qwen Code, and more.

Why Does This Matter?

When you ask an AI assistant to "add a new function" or "fix this bug," there's a critical gap between the LLM's text generation and your actual filesystem. The AI outputs text—but how does that become a working code change?

Every AI coding agent solves this differently. Some use surgical search-and-replace, others apply unified diffs, some just overwrite entire files, and a small but important set use hash-backed anchors to survive drift. Dirac does it with cryptographic word anchors; Oh My Pi does it with compact line-plus-hash markers. Understanding these approaches matters if you're:

• Building your own AI coding tool
• Debugging why an AI edit failed
• Choosing between different AI assistants
• Curious about the engineering behind these tools

The Core Challenge

💡

LLMs Are Non-Deterministic

Even with the same prompt, an AI might output slightly different whitespace, comments, or formatting each time. A file editing system must handle these variations gracefully—or fail constantly.

Consider what can go wrong:

• Whitespace mismatches: The AI uses 2 spaces, but your file uses tabs
• Hallucinated content: The AI "remembers" code that doesn't exist
• Partial context: The AI only saw 50 lines but the file has 500
• Race conditions: You edited the file while the AI was generating

The Landscape at a Glance

We analyzed how the agents in repos/ handle file editing, plus a few adjacent reference agents. Each takes a different philosophical approach:

🎯

Cline

The Precision Specialist

Uses search/replace with a 4-tier matching strategy. Doesn't trust AI whitespace—implements heavy fallback logic.

4-Tier Fallback🔧

Codex / Claude Code

The Patch Master

Uses custom patch syntax with *** Begin Patch markers. Treats file editing like version control.

Custom Patch Format🛠️

OpenCode

The Fallback King

Implements 9 different matching algorithms tried sequentially. Maximum redundancy approach.

9-Layer Fallback⚡

Aider

The Format Flexible

Supports three formats: SEARCH/REPLACE, whole file, and unified diff. Model-specific defaults.

3 Edit Formats🚀

Grok CLI

The Dual-Mode Agent

Traditional text editor + Morph AI-powered fast editing. User confirmation with diff previews.

4,500+ tok/sec💎

Crush / Neovate

The Redundant Matchers

JSON tool schemas with edit/write/multiedit tools and layered string-matching fallbacks for stubborn whitespace and escaping.

Layered Replacement📌

Dirac

The Hash-Anchored Editor

Edits target stable line hashes instead of line numbers, then batch multi-file changes in reverse order to avoid drift.

Hash Anchors🧪

Oh My Pi

The Hashline Lab

Makes hashline the default edit mode: compact line-plus-hash anchors, multi-section preflight checks, same-path merge logic, nearby anchor rebasing, and benchmark-driven iteration.

Hashline Default🧩

OpenHands / Claude-style

The Standard Editor Interface

Uses str_replace_editor-style commands: view, create, str_replace, insert, and undo, usually inside a sandbox.

Editor Command API🏗️

ADK-Rust

The Provider Delegate

Wraps provider-native editor contracts instead of building a giant local matcher. Anthropic tools execute strict exact-match edits; OpenAI apply_patch is surfaced as a native built-in.

Native Tool Contracts⚙️

Pi / Pochi / Qwen

The Exact-Match Pragmatists

Prefer explicit old/new content with uniqueness checks, preview diffs, encoding preservation, and clear failure messages.

Exact + Verified🔌

Goose / DeerFlow / Hermes

The Tool-Host Agents

File mutation often lives in extensions, sandbox tools, or MCP-like tool registries rather than a single built-in editor primitive.

Extension/Sandbox Tools

The Six Core Editing Methods

Across all agents we analyzed, file editing boils down to these six fundamental approaches:

The "Secret Sauce": Fallback Cascades

The top-performing agents don't rely on a single method. They implement cascading fallbacks—if one approach fails, they automatically try the next.

1Exact Match

Try byte-for-byte string matching. Fastest, most reliable when it works.

↓2Whitespace Flexible

Normalize spaces/tabs, trim lines. Handles indentation differences.

↓3Anchor Matching

Match first/last lines of a block, fuzzy-match the middle content.

↓4Diff/Patch Application

Use diff-match-patch or git cherry-pick algorithms.

↓5Full Overwrite

Nuclear option. Works 100% but expensive and risky for large files.

↔️

Not every agent wants a fallback ladder

ADK-Rust is one counterexample. It prefers provider-native tool contracts and sharp exact-match failures over adding more local heuristics. Oh My Pi is another: instead of endlessly extending fuzzy matching, it changes the address space with hashline anchors.

Key Insights for Tool Builders

✅

Don't Force JSON

For file editing, custom formats or XML reduce escaping errors significantly. Cline and Codex both avoid passing code inside JSON strings.

✅

LSP Integration

OpenCode checks for syntax errors immediately after every edit using Language Server Protocol. Bad edits get caught and reported back to the AI.

✅

User Confirmation

Grok CLI shows diff previews before every write. Users can approve, modify, or skip. This prevents catastrophic mistakes.

✅

1-Indexed Line Numbers

Codex, OpenCode, and ADK-Rust all use 1-based line numbers when communicating with LLMs. It matches how humans count lines in editors.

Explore the Playbook

📚 Editing MethodsDeep dive into each editing approach: whole file, search/replace, unified diff, anchors, and multi-edit.🤖 Agent ComparisonsDetailed analysis of Cline, Codex, OpenCode, Aider, Grok CLI, Crush, Dirac, OpenHands, Pi, Pochi, Qwen Code, Goose, and more.💬 Prompts & InstructionsHow agents tell AI models to use their tools. System prompts, tool definitions, and error handling.🏗️ ADK-Rust Deep DiveShort focused write-up on provider-native editing, strict exact matches, Anthropic editor wrappers, and OpenAI patch declarations.🎯 Reasonix Deep DiveByte-exact SEARCH/REPLACE, edit-gate review, repair stages, snapshotting, and strict sandbox enforcement.🐋 CodeWhale Deep DiveDirect writes, exact-first replace with bounded fuzzy fallback, transactional patch rollback, approval gating, and diagnostic feedback from touched files.🐆 CheetahClaws Deep DiveMixed-mode editing with direct Read/Write/Edit tools, capability-gated kernel built-ins, notebook mutation, and unified diff feedback.🧪 Oh My Pi Deep DiveFocused analysis of hashline editing, prompt helpers, nearby rebasing, multi-section preflight, and how it compares to Dirac and Pi Mono.🛠️ Build Your OwnPractical guide with code examples. Implement fallback cascades, matching algorithms, and LSP integration.

Quick Comparison Chart

A visual overview of how different agents prioritize various aspects:

Token Efficiency

Lower is betterCodex★★★★★Aider★★★★☆Cline★★★☆☆OpenCode★★★☆☆

Fallback Depth

More is more robustOpenCode9 layersAider5 layersCline4 layersCodex3 layers

LSP Integration

Syntax checkingOpenCodeFullClinePartialCodexMinimalAiderMinimal

User Approval

Confirmation flowGrok CLIFull diff previewCodexApproval req.ClineConfigurableAiderAuto-apply

All of the top companies are starting to raise prices, limit access. I kept going back to Claude Code but with cheaper chinese models instead of real Claude, which works okay, but every time they release a new Claude Code, it starts annoying me with something. Last time, even when I ran it with dangerously skip permissions, it just kept asking me permission for every single thing it did. It has done that a few times, so I said screw it, i'll make my own agentic coding tool!

But then I found Oh My Pi - its Pi Mono, the agent used in OpenClaw, but its got all the good stuff you typically want. The developer added a lot of stuff I was thinking about in my head that I would have to implement if i tried to make my own (since I couldn't find anything decent!).

When you install it, it'll pick up information from other coding agents, like api keys, etc, that have already been installed/used, so its very easy to just install and use it.

Its doing a lot of things right, Its just an agent that works, and has an efficient hash based file edit mode that seems to work great and is more efficient (and works with all models).

It has been perfect so far, no complaints with Minimax M2.7, Deepseek v4.

Minimax coding plan or whatever they call it, token plan or w/e, is $10, and its a good model for constant use, its fast, mostly accurate but not always. But since, i've found some more better plans that include it. Opencode Go $5 first month, $10 after - has several models, Qwen3.6 models, Minimax, Deepseek, some other ones. I am about to try it, but seems good - someone complained about a model having only 250k context instead of 1m but mostly people seem happy. You can use it with things without using Opencode at all (Opencode sucks, sorry to say, glitchy mess, i would avoid especially if on a Windows machine)

🚀 Github Copilot won't be so great with the tokens coming next month

Github Copilot, Claude Code, Pi this is the last month (May) to get the crazy lenient usage out of it before they switch it to something much worse. I have no idea how that will work out... but I will find out since I have copilot.

Claude Code + Oh My Pi

Some people seem to think that it is only the model intelligence that matters, and the agent harness / coding tool around the models aren't a big deal. This is sooooo wrong.

I recommend Oh My Pi at this point since Claude Code is trying hard to kick everyone off or annoy them if they aren't using 100% Claude models.

Deepseek v4 Flash and Pro

Right now its crazy discounted/cheap Deepseek v4 and/or Minimax M2.7 via Opencode Go and/or GLM 5.1 (GLM seems to have lots of availability issues...), compared to using Claude Opus 4.5 in some tools like Opencode. Qwen3.5/3.6 are very good as well.

I am only paying 28-40 cents per million tokens if I use the official Deepseek Anthropic-compatible API endpoint with Claude Code.

I read that if you had bought it when it was at $3, it might still let you pay $10 or something, but this is unconfirmed. I got rid of mine and am just using CliProxyAPIPlus to route Openrouter models to Claude Code.

But... I found this:

Rust redo of Claude Code, when the src code was leaked

Haven't tried it yet, and it might not have every detail yet from C.C. but it looks good, will try.

Will post new guide more like the older ones soon.

old info starts here

Alibaba Coding Plan: $3/Month for Claude Code

I had to post about this cheap Coding Plan I found — Alibaba Coding Plan, $3/month right now, and you get all these models plus some other ones:

Plan Details

Supported Models

Recommended:

• qwen3.5-plus (vision)
• kimi-k2.5 (vision)
• glm-5
• MiniMax-M2.5

More models:

• qwen3-max-2026-01-23
• qwen3-coder-next
• qwen3-coder-plus
• glm-4.7

You can use it directly in Claude Code, OpenClaw, and others.

I still use Claude Code and GitHub Copilot. I tried Goose... on Windows 11... errors and more errors! I don't have time for that so I got rid of it. I have noticed a lot of these things are created by Mac people, and Windows/Linux are 2nd class citizens — more of an afterthought.

Claude Code Setup for Alibaba / Dashscope

🔧 Click to expand: Bash config for Claude Code with Alibaba models (add to your ~/.bashrc)Copy and paste this at the end of your `~/.bashrc` file if using bash in Linux, or vibe code something similar for easy model switching.# --- ALIBABA / DASHSCOPE CLAUDE CODE CONFIGURATION ---

# PASTE YOUR API KEY HERE:
export ALIBABA_API_KEY="sk-sp-xxxxxxx"

# Internal helper function to launch Claude with Alibaba settings
_claude_ali_launch() {
local model_name=$1
shift
(
export ANTHROPIC_BASE_URL="https://coding-intl.dashscope.aliyuncs.com/apps/anthropic"
export ANTHROPIC_AUTH_TOKEN="$ALIBABA_API_KEY"
export ANTHROPIC_API_KEY="" # Clear this to avoid conflicts
export ANTHROPIC_MODEL="$model_name"
export ANTHROPIC_DEFAULT_HAIKU_MODEL="$model_name"
export ANTHROPIC_DEFAULT_OPUS_MODEL="$model_name"
export ANTHROPIC_DEFAULT_SONNET_MODEL="$model_name"
export ANTHROPIC_REASONING_MODEL="$model_name"

# Performance/Token settings
# export CLAUDE_CODE_MAX_OUTPUT_TOKENS=30000
export API_TIMEOUT_MS=3000000
export CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC=1
export CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1

claude --dangerously-skip-permissions --model "$model_name" "$@"
)
}

# Model Commands
alias claude-qwen='_claude_ali_launch qwen3.5-plus'
alias claude-kimi='_claude_ali_launch kimi-k2.5'
alias claude-glm5='_claude_ali_launch glm-5'
alias claude-mm='_claude_ali_launch MiniMax-M2.5'

# --- MENU DISPLAY ---
claude-menu() {
echo -e "\n\033[1;36m====================================================\033[0m"
echo -e "\033[1;32m CLAUDE CODE - ALIBABA (DASHSCOPE) ENDPOINTS \033[0m"
echo -e "\033[1;36m====================================================\033[0m"
echo -e " \033[1;33mclaude-qwen\033[0m - Use Qwen 3.5 Plus"
echo -e " \033[1;33mclaude-kimi\033[0m - Use Kimi K2.5"
echo -e " \033[1;33mclaude-glm5\033[0m - Use GLM-5"
echo -e " \033[1;33mclaude-mm\033[0m - Use MiniMax M2.5"
echo -e "\033[1;36m----------------------------------------------------\033[0m"
echo -e " Keys are pre-configured with skip-permissions."
echo -e "\033[1;36m====================================================\033[0m\n"
}

# Run the menu automatically on startup
claude-menu

Which Model to Use?

I have found that running Claude Code set to kimi-k2.5 will create very elaborate, complex plans — but it is not as fast as the others. MiniMax M2.5 is quickest, which is often the best just for that reason.

Some tactics you can use:

• Run these different model commands, even at the same time, to all create plans simultaneously.
• Then have one of the smarter ones like Kimi go over all the plans and give opinions — explain why any one is better than the others.
• Generate a best-of-N plan. Use MiniMax to implement.

I haven't used the new Qwen 3.5 models enough yet to have a strong opinion, but they seem good. Same with GLM 5.

The Coding Plan's main page gives instructions for all of the main coding agentic tools, and they include OpenClaw. Save some money! Be careful with security if messing with OpenClaw. 🔐

Gemini 3.1 Pro came out too! It's very very good. The USA AI companies are still the absolute best ones. It still makes sense to use something like my tool, aicodeprep-gui, to plan things out on all the different web chat's (see the older guides from last year... July, Sept etc for how-to's) and just use that tool to go back and forth when bug fixing or in the planning stages of a project.

I use Gemini 3 Flash a lot for bug fixing, the speed is sometimes better than intelligence since a faster model can fail 3 times before fixing something, while the smarter model can figure it out on first pass or two, but it takes longer...

Help AI models by giving them the WHY behind your instructions.

Fancy title but simple idea I have been doing that helps keep coding agents on track:

After I give some instructions (anywhere in the process of coding with them) if I think something might be hard to understand or I suspect that it might go off track or forget, I add a sentence or two about the purpose of the instructions.

Like:

Prompt/instructions:
Have the app check to see if powershell 7 is installed before defaulting to the built in powershell, on app start.
**The purpose of this is to** help the user save time so they don't have to manually change it to the newer powershell (it seems logical to assume they will probably want to use the latest one, if they installed it).

Usage tips:

• Keep the purpose concise (one or two lines) and place it immediately after the instruction it clarifies.
• Use the word Purpose or say "The Point of this is..." so agents can understand. Basically, its like if it was a person and you are trying to teach the person something and when they understand the WHY behind something, its easier to learn or do it!
• Consider visually styling or marking purpose lines (highlight, bold, or a prefixed token) if your toolchain supports it — that makes them easier to extract.

AI is moving so fast it's nearly impossible to keep up with it or have enough time in the day! I am having a blast though.

🚀 Current Workflow Lately

Claude Code and Github Copilot most of the time (Copilot is not as good as Claude Code but they do try to catch up fast and they have the most bang for your buck when it comes to access to the best models: Claude Opus 4.5, GPT 5.2, etc).

Claude Code is just the best!!

Some people seem to think that it is only the model intelligence that matters, and the agent harness / coding tool around the models aren't a big deal. This is sooooo wrong.

Why Claude Code + Cheap Models > Expensive Models + Bad Tools

I can get such a better, smoother experience using Claude Code with Deepseek v3.2 and/or Minimax M2.1 and/or GLM 4.7, compared to using Claude Opus 4.5 in some tools like Opencode.

I am only paying 28-40 cents per million tokens if I use the official Deepseek Anthropic-compatible API endpoint with Claude Code.

I use Claude Code in Windows often in WSL (Linux sort of "inside" Windows, a lot of things just work better in WSL) and have my ~/.bashrc file set up with custom commands to run Claude Code with either Deepseek or Minimax. GLM 4.7 is the cheapest option here... $3/month still I believe!

🔧 Click to expand: Add custom bash commands to run Claude Code with Deepseek API, GLM 4.7, Minimax M2.1Add custom bash commands to run Claude Code with **Deepseek API**, **GLM 4.7**, **Minimax M2.1**, or anything that supports `v1/messages` type API. This makes it simple to switch back and forth when you want normal Claude or Deepseek Claude, M2 Claude, etc. ### Tested in Ubuntu WSL2 You can ask AI to modify it for Mac, Windows Powershell, etc.## Claude Code Custom Commands
CLAUDE_BIN="$HOME/.local/bin/claude"
CLAUDE_SETTINGS="$HOME/.claude/settings.json"

### Helper function to swap settings temporarily
### There are better/other ways to do it, but I know this works
### I haven't tested other versions but you can

_claude_with_config() {
local json_content="$1"
shift

# Ensure .claude directory exists
mkdir -p "$HOME/.claude"

# Backup original settings
[ -f "$CLAUDE_SETTINGS" ] && cp "$CLAUDE_SETTINGS" "$CLAUDE_SETTINGS.backup"

# Write JSON directly - no variables to expand
echo "$json_content" > "$CLAUDE_SETTINGS"

# Run claude
"$CLAUDE_BIN" "$@"
local exit_code=$?

# Restore original settings
[ -f "$CLAUDE_SETTINGS.backup" ] && mv "$CLAUDE_SETTINGS.backup" "$CLAUDE_SETTINGS"

return $exit_code
}

## DeepSeek Configuration
claude-ds() {
local config='{
"env": {
"ANTHROPIC_AUTH_TOKEN": "sk-examplekey",
"ANTHROPIC_BASE_URL": "https://api.deepseek.com/anthropic",
"ANTHROPIC_API_KEY": "",
"ANTHROPIC_DEFAULT_HAIKU_MODEL": "DeepSeek-V3.2",
"ANTHROPIC_DEFAULT_OPUS_MODEL": "DeepSeek-V3.2",
"ANTHROPIC_DEFAULT_SONNET_MODEL": "DeepSeek-V3.2",
"ANTHROPIC_MODEL": "DeepSeek-V3.2",
"ANTHROPIC_REASONING_MODEL": "DeepSeek-V3.2-Speciale",
"API_TIMEOUT_MS": 600000
},
"includeCoAuthoredBy": false
}'
_claude_with_config "$config" "$@"
}

## This version of the Deepseek command adds the skip permissions flag
claude-ds2() {
local config='{
"env": {
"ANTHROPIC_AUTH_TOKEN": "sk-examplekey",
"ANTHROPIC_BASE_URL": "https://api.deepseek.com/anthropic",
"ANTHROPIC_API_KEY": "",
"ANTHROPIC_DEFAULT_HAIKU_MODEL": "DeepSeek-V3.2",
"ANTHROPIC_DEFAULT_OPUS_MODEL": "DeepSeek-V3.2",
"ANTHROPIC_DEFAULT_SONNET_MODEL": "DeepSeek-V3.2",
"ANTHROPIC_MODEL": "DeepSeek-V3.2",
"ANTHROPIC_REASONING_MODEL": "DeepSeek-V3.2-Speciale",
"API_TIMEOUT_MS": 600000
},
"includeCoAuthoredBy": false
}'
_claude_with_config "$config" --dangerously-skip-permissions "$@"
}

## MiniMax Configuration
claude-mm() {
local config='{
"env": {
"ANTHROPIC_AUTH_TOKEN": "eyJexamplekeyFJP5CLrCE1f-sxKKg",
"ANTHROPIC_BASE_URL": "https://api.minimax.io/anthropic",
"ANTHROPIC_API_KEY": "",
"ANTHROPIC_DEFAULT_HAIKU_MODEL": "MiniMax-M2.1",
"ANTHROPIC_DEFAULT_OPUS_MODEL": "MiniMax-M2.1",
"ANTHROPIC_DEFAULT_SONNET_MODEL": "MiniMax-M2.1",
"ANTHROPIC_MODEL": "MiniMax-M2.1",
"API_TIMEOUT_MS": 3000000,
"CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC": "1"
},
"includeCoAuthoredBy": false
}'
_claude_with_config "$config" "$@"
}

claude-mm2() {
local config='{
"env": {
"ANTHROPIC_AUTH_TOKEN": "eyJhbGcfakeexamplekeyNpfWyandBFJP5CLr9999CE1f-sxKKg",
"ANTHROPIC_BASE_URL": "https://api.minimax.io/anthropic",
"ANTHROPIC_API_KEY": "",
"ANTHROPIC_DEFAULT_HAIKU_MODEL": "MiniMax-M2.1",
"ANTHROPIC_DEFAULT_OPUS_MODEL": "MiniMax-M2.1",
"ANTHROPIC_DEFAULT_SONNET_MODEL": "MiniMax-M2.1",
"ANTHROPIC_MODEL": "MiniMax-M2.1",
"API_TIMEOUT_MS": 3000000,
"CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC": "1"
},
"includeCoAuthoredBy": false
}'
_claude_with_config "$config" --dangerously-skip-permissions "$@"
}

## Claude Menu Command
claude-menu() {
echo "Available Claude Custom Commands:"
echo " claude-ds - Run Claude with DeepSeek"
echo " claude-ds2 - Run Claude with DeepSeek (skip permissions)"
echo " claude-mm - Run Claude with MiniMax"
echo " claude-mm2 - Run Claude with MiniMax (skip permissions)"
}

🎯 Why Claude Code Stays Ahead

All of those coding tools are lagging behind Claude Code, and it seems like the main developer on that is just way smarter (and faster) so it's unlikely to change anytime soon. He's the one coming up with all the novel methods to save context or split context, he knows how to delegate tasks with the right context to subagents, etc.

For example, the Skills feature is genius. Most people thought it was a gimmick - not me! It's immediately obvious how smart it is with the ability to not shove the entire amount of text at the model. It can choose to load more progressively.

I can see a lot of other things that could possibly use a similar feature/addition like context pruning... with a dedicated model that does that in the background. Might be a good thing.

### 💡 Key Insight: Skills Feature The **Skills feature** in Claude Code allows: - Progressive context loading (not dumping everything at once) - Smart selection of relevant information - Reduced token usage - Better model performance This is what separates amateur tools from professional ones.

💰 Use Smartest Models for Planning, Cheap Models for Doing

You don't have to use the pricey Claude 4.5 Opus and Sonnet the entire time - I see that the internet is still full of angry people when they can't do much with a $20 Claude subscription. Do they upgrade to MAX for $200? People tend to use Opus for everything or think they need at least Sonnet.

### My Philosophy (Still True After All This Time) **You don't need superintelligent models to use tools and MCP servers.** I think the best coding tool could be one that always uses at least two models: 1. **One for planning** (the smart one) 2. **One for execution** (well, more like one main smart one, and many smaller/less intelligent models that run subagents to do things) One model for tool use (multiple instances of it at the same time usually) that just hand back the information to the smart model when it needs to do smart stuff like fix bugs or plan out a whole app at a high level.

The Strategy:

1. Planning Stage: Use Claude Opus 4.5, GPT 5.2, Deepseek v3.2, or Gemini 3 Pro
2. Execution Stage: Use GLM 4.7, Minimax M2.1, or Deepseek v3.2
3. Tool Usage: Cheap models work just fine for file operations, searches, etc.

📝 Spend Longest Time in the Planning Stages

Figure out details so it won't guess

If you pay for Claude, use it for planning stages, spend much more time here than feels comfortable. Or just use Deepseek v3.2 here (it is so good, I am not sure why I ignored it for a while!).

Look up SPEC.md methods or different PLAN approaches. I like this plugin called Superpowers. Github has some spec command you can download that is probably good although it seemed too time consuming. I am still trying to find the perfect plan tool or workflow to plan really good to where it can one-shot a complex project.

### 🧪 Experimental Multi-Model Planning Workflow A workflow I have been thinking about but haven't tried yet: 1. Let **Minimax/GLM 4.7** plan with Superpowers plan plugin 2. Run it through **GPT 5.2**, **Claude Opus 4.5**, **Gemini 3 Pro**, and a couple Chinese models 3. Have them look it over and make suggestions 4. Synthesize the best ideas from all models You can use this tool to help send prompts to many models at the same time (I'm working on making that part a separate thing, and making it into an Agent Skill/plugin): **[AI Code Prep GUI](https://wuu73.org/aicp)** Then switch to a faster model (Deepseek is a bit slow!) like **GLM 4.7** - I believe it is still $3/month, for execution.

There are easy ways around this, even better than what I had discovered before... read on!

⚡ GLM 4.7

It's a good "do-er" model, not as smart as Claude or Deepseek v3.2, but it is fast and cheap. Only $3/month!

🚀 You've been invited to join the GLM Coding Plan! Enjoy full support for Claude Code, Cline, and 10+ top coding tools — starting at just $3/month. Subscribe now and grab the limited-time deal! Link: https://z.ai/subscribe?ic=LQIZTM2EP4

🏆 Claude Code Is Always Several Steps Ahead of the Rest

I am currently using Claude Code with Deepseek v3.2 which only costs:

I also use it a TON with Minimax M2.

My Current Workflow:

1. Plan with aicodeprep-gui using multiple models (and the new Flow Studio)
2. Get everything fine-tuned
3. Get a good plan and possibly a speckit going (if it's a big enough software project)
4. Load Claude Code up
5. Give it the markdown plan and just let it run
6. Give it some MCP servers to help automate testing

Works so well.

🤖 Gemini CLI

It is getting better! I have not used it that much but I am going to see what it can do vs Claude Code. It is still free!

I use Gemini 3 Flash and Pro in Copilot for debugging... its great, better than Claude usually for that.

You can get the 3-month $300 Google Cloud credit, which allows you to use Nano Banana Pro (REALLY amazing) in the CLI/API.

🌌 Google Anti Gravity IDE

It's good! It's free to use (rate limited but usable) and you can use Opus 4.5 + Gemini 3 Pro free.

It is a VS Code Clone.

Link: https://antigravity.google

🌍 Updates to aicodeprep-gui: Now Multilingual!

I made it multilingual as some people asked about it - originally I made it for myself which is why I didn't bother with any of that before. Now you can switch language.

I noticed a lot of visitors to this site from China, Korea, India... so this should help!

💡 Quick Tips & Best Practices

### Model Selection Matrix **When to use EXPENSIVE models (Claude Opus 4.5, GPT 5.2):** - Initial project architecture decisions - Complex debugging that cheaper models can't solve - Novel algorithm design - Critical security reviews **When to use CHEAP models (GLM 4.7, Deepseek v3.2):** - File operations - Repetitive code generation - Following established patterns - MCP server tool usage - Test writing **When to use FREE models (Gemini 3 Pro on Antigravity):** - Free doesn't mean bad, its good for everything - Experiments - Learning new technologies - Debugging

🎓 Lessons Learned So Far in 2026

1. The tool matters MORE than the model - Claude Code with Deepseek > Bad tool with Claude Opus
2. Planning is worth the investment - Spend 3x longer planning vs coding
3. Multi-model workflows work - Different models excel at different things
4. Context curation is king - Don't dump everything, be strategic
5. Cheap models are getting REALLY good - GLM 4.7 and Deepseek v3.2 rival expensive models for many tasks

📊 Cost Comparison: February 2026

Here's what I'm actually spending monthly for unlimited AI coding:

Compare this to:

• Claude Pro MAX: $200/month (and you still hit limits!)
• Cursor Pro: $40/month
• Other premium IDEs: $30-100/month

🚀 What's Next?

I'm experimenting with:

• Multi-model review workflows
• Automated spec generation
• Context pruning with dedicated models
• MCP server automation strategies

Check back next month for updates!

## Final Thoughts **AI coding in 2026 is not about having the most expensive subscription.** It's about understanding which model to use for which task, how to structure your prompts, and leveraging the right tools. Claude Code + cheap models + good planning = infinite coding at 5% the cost.

Last updated: February 4, 2026

Current Workflow

I am currently using Claude Code a TON with Minimax M2. Usually I plan with aicodeprep-gui using multiple models (and the new Flow Studio), get everything fine tuned, get a good plan and possibly a speckit going (if it's a big enough software project) and then load Claude Code up, give it the markdown plan and just let it run. Give it some MCP servers to help automate testing.. works so well.

Model Updates

Minimax M2

Was free for a period of time in November, works with Claude Code (as well as GLM 4.5/4.6) very very good!! Works in Cline, etc. Better/smarter vs GLM models. Also, I got $50 API credits free just by signing up for the developer deal - their API is very cheap, I hope the model quality stays great! They have a coding plan similar to the GLM / Z.ai coding plan (but $10/mo vs $3).

I just noticed Cline is giving away free usage of Minimax M2 right now.

🔥 Claude Code + Chinese Models = Less Hallucinations!

Both Minimax M2 and GLM 4.5/4.6 work exceptionally well with Claude Code. Here's the surprising thing: they hallucinate LESS than real Claude 4.5 Opus! Opus tends to stray off into its own ideas a lot, going on tangents or "improving" things you didn't ask it to improve. These Chinese models stay focused on the task, follow instructions better, and just get the job done. Especially when you let Claude Code run without user input - M2 and GLM just keep chugging along reliably.

Get 10% off Z.AI's coding plan: https://z.ai/subscribe?ic=RV42B8COHB

Worth Looking Into

Have not used these enough to have an opinion but looks possibly good:

• Ling 1T
• Ring 1T

Longcat.chat

Has some new models, with 0.5-5.0 mil tokens free daily on the website. Also looks like it might work in Claude Code.

Kimi K2 Thinking

I am sure this is excellent, have not tried, but heard good things. Non-thinking Kimi K2 is already excellent for writing.

Grok 4

I tried using it for something (one of the 5 LLMs I will sometimes use for brainstorming or fixing a bug - 5 models, each from a different AI company, then a 6th model to analyze all 5 results to generate a best of all of them result usually provides a superior response to just one model Learn more here - and it hallucinated pretty bad. It was actually funny, is this what people see when they claim AI is always hallucinating? I don't usually get much of that. I don't trust people claiming these models are always the best - some people (not mentioning names) will lie a ton, and i'm supposed to just believe them when they claim their models are always the best. Everytime I try to use it, its meh, not terrible but not great. Its free though - but I don't trust that it will always tell me the truth and is not programmed to say something different.

Interesting Finds

These are currently (either totally or partial) free, and interesting enough to use:

Google's Antigravity IDE / Code Editor

https://antigravity.google/?ref=wuu73.org

VS Code clone with Gemini 3 Pro, Claude Sonnet 4.5... FREE! Its got a multi-agent system that can do many things at the same time. I am using it today and so far it is okay but NOT as good as Claude Code or Github Copilot in VS Code (which is actually very good now compared to how it used to be!). I also got rate limited, but its free and i'm sure they will improve it with regular updates. Can't beat free usage of both those models!

When it started rate limiting Gemini 3, I could switch to Claude 4.5 and get plenty of use, so it appears the limits to free are for each model rather than combined.

https://www.kimi.com

Besides normal AI web chat, there are things on here for:

• Generating slides automatically
• A Manus-like thing that has AI agents (powered by Kimi K2) inside a virtual machine that can code and create apps or do things, research etc for you

https://agent.minimax.io

Looks interesting, Minimax M2 model is fantastic so I'm sure this is good, and appears to be free as of the time I am typing this.

cc-switch

https://github.com/farion1231/cc-switch

Looks useful for switching config/MCP servers on/off between Claude Code and Codex. I downloaded the repo and scanned it through several LLMs for a security check, looks good. It's very small size due to using Rust and Tauri.

GitHub Spec-Kit from Microsoft

https://github.com/github/spec-kit

This is free and will keep your AI coding agent on task. It seems good but I have not experimented enough to know how good it works. It seems a bit overkill for smaller projects but for super important, larger software, it seems like it would be very helpful.

Async / Web Based Tools

Google Jules

Google Jules

Currently free and seems reliable. You give it permission to work with your repos on GitHub, it will make changes, work inside a VM where it is able to test in a loop.

GitHub Spark & Copilot Agent

https://github.com/features/spark?ref=wuu73.org

Both similar and work great! The copilot agent only uses one premium request for the whole task.

**About GitHub Copilot:** It used to suck, but now it is good. I pay the $10/month and then use the unlimited GPT 4.1 and limited Claude 4.5. It has come a long way, and the **copilot agent mode is fantastic**. So I currently pay $10 for Copilot (and the Claude credits go a long way.. you can just keep on going in the same chat as long as you don't keep opening fresh ones, it barely uses any premium credits even after many hours).

My Current Subscriptions

• $10 for Github Copilot Pro
• $3 for Chutes.ai
• $3 for Z.AI GLM Coding Plan - Get 10% off with that link! (but.. I think Minimax M2 is also excellent! Very cheap API, both work amazingly in Claude Code)

I have used both of these more than once, to run async jobs (you send it off, and it gets to work without you interacting much or needing to, then you come to look at it later).

🤝 A Note About "Deals" on This Guide

I am not going to 'sell out' ever and start going down the enshittification road to crappyness. I will never put crap on this guide. So when you see a 'deal' like 10% off Z.AI's coding plan, it's because it's genuinely great! It's pocket change credit they would give me that I don't really care about.

2025-2026 is the era of no shame where people will try to sell you the crappiest crap, snake oil, etc. because they get a dollar. NOT ME! Promise!

These Chinese models (GLM, Minimax M2) are GREAT in Claude Code, especially when letting it run without user input. They hallucinate less than real Claude Opus 4.5 which tends to stray off into its own ideas constantly.

Random study I found

https://arxiv.org/abs/2407.01489

@@@ make this section stand out with a yellow background, with dark black text. Try to make it a rounded box with soft edges or a fade to yellow then fade back to normal. The yellow shouldn't be full brightness.. more like 30-40% grey still.

Agentless: Demystifying LLM-based Software Engineering Agents

Recent advancements in large language models (LLMs) have significantly advanced the automation of software development tasks, including code synthesis, program repair, and test generation. More recently, researchers and industry practitioners have developed various autonomous LLM agents to perform end-to-end software development tasks. These agents are equipped with the ability to use tools, run commands, observe feedback from the environment, and plan for future actions. However, the complexity of these agent-based approaches, together with the limited abilities of current LLMs, raises the following question: Do we really have to employ complex autonomous software agents? To attempt to answer this question, we build Agentless -- an agentless approach to automatically solve software development problems. Compared to the verbose and complex setup of agent-based approaches, Agentless employs a simplistic three-phase process of localization, repair, and patch validation, without letting the LLM decide future actions or operate with complex tools. Our results on the popular SWE-bench Lite benchmark show that surprisingly the simplistic Agentless is able to achieve both the highest performance (32.00%, 96 correct fixes) and low cost ($0.70) compared with all existing open-source software agents! Furthermore, we manually classified the problems in SWE-bench Lite and found problems with exact ground truth patch or insufficient/misleading issue descriptions. As such, we construct SWE-bench Lite-S by excluding such problematic issues to perform more rigorous evaluation and comparison. Our work highlights the current overlooked potential of a simple, interpretable technique in autonomous software development. We hope Agentless will help reset the baseline, starting point, and horizon for autonomous software agents, and inspire future work along this crucial direction. @@@@

Testing New Feature: Flow Studio

Testing new feature in aicodeprep-gui/pro - Has a Flow Studio now where you can have it auto-send your context/code/project, send your problems or tasks etc (prompt) to as many LLMs as you want at the same time. You can design complex logic. See more by going here: https://wuu73.org/aicp/flowc/flow_studio_help.html

I am currently splitting that so it is a separate app and MCP server, so any app or model can directly call for help (if you use 5 different companies AI models... GPT 5.. Claude 4.5.. Gemini.. Kimi.. etc and maybe 3 instances each of those... compare all outputs or have AI do it.. and design a best of N or better than all solution - that's the idea and it works well on harder problems or big planning stages.

My Browser Setup: The Free AI Buffet

First things first, I have a browser open loaded with tabs pointing to the free tiers of powerful AI models. Why stick to one when you can get multiple perspectives for free? My typical lineup includes:

• At least 2-3 tabs of z.ai's GLM 4.5 – free on web, and seems as good or better than Claude 4! no joke. Get 10% off Z.AI's coding plan if you want API access for Claude Code (works amazingly well, less hallucinations than real Claude Opus!).
• At least one tab but more like three of Google Gemini AI Studio (Gemini 2.5 Pro/Flash are often free and unlimited here).
  Also, try Google Gemini 2.5 Pro (different than AI Studio, has better image generation and deep research; I always have a couple tabs of this along with a couple tabs of AI Studio).
• Couple tabs of Poe.com usually set to Claude 4 or o4-mini for its free daily credits on premium models.
• Several tabs of OpenRouter, set to several models, some free models, some not.
• At least one tab for ChatGPT (the free version is still useful).
• At least one tab for Perplexity AI, especially good for research-heavy questions.
• At least one tab for Deepseek (v3 and r1 are free on their web interface, though watch the context limit).
• One tab for Grok.com. Good, free and seemingly unlimited for general use and deep research/image editing. I mainly use the deep research feature, similar to perplexity.
• Phind is another free one, it tries to show you flowcharts/diagram visuals.
• lmarena.ai offers free access to Claude Opus 4 and Sonnet 4 and others. Free Opus 4 is so good.

Claude.ai - Free but sometimes so limited it's annoying to use, so I use other sites/ways to access Claude like Cody extension, Copilot, etc.

### ⚠️ Important Disclaimer about Grok Grok offers free compute and uncensored image generation, which can be useful when other models' safety systems interfere with legitimate tasks. However, reports indicate that Grok has been instructed to lie about about some things. While the misinformation appears to be mostly on X, if you keep in mind to restrict usage to coding or be cautious knowing it might be programmed with questionable motives, it can occasionally be useful. It was never that great though but its sometimes okay.

A smarter, cheaper workflow: Focused Context

When you use AI in web chat's (the chat interfaces like AI Studio, ChatGPT, Openrouter, instead of thru an IDE or agent framework) are almost always better at solving problems, and coming up with solutions compared to the agents like Cline, Trae, Copilot.. Not always, but usually.

When you use things like Cursor, Cline, Roo Code for everything, they are sending tons of text to the AI about how to use their tools, how to use or activate MCP server stuff, edit files, etc it "dumbs it down" too much. It gets confused. People end up paying for the most expensive best models to do everything and even that isn't enough to get over the dumbing down effect from the AIs getting tons of unneeded information unrelated to your problem.

So when that happens, I use my tool to generate the right context to solve my problem. Then I paste it into one of the many AI web chat's (sometimes more than one, since they sometimes give different answers) and just ask it questions or ask it to code review, to try to figure out why x is happening when y is happening...etc then when it figures out a solution.. i have it write a prompt for Cline or another agent type thing to do the actual file edits. GPT 4.1 can handle this just fine and I have unlimited. No reason to be wasting Claude credits to edit files. No reason to be sending Claude a bunch of crap it doesn't need making it dumb. I can use Claude to plan out anything or fix really hard problems, cheap, using Openrouter web chat then just paste it back in Cline and let it run.

After doing this for a while, you really get a feel for which models excel at which types of tasks.

**How AI Code Prep Helps (Example Prompt Structure):** _Can you help me figure out why my program does x instead of y?_ Then, [AI Code Prep GUI](https://wuu73.org/aicp) (for Windows, Mac, Linux, and web) steps in. It recursively scans your project folder (subfolders, sub-subfolders, you name it) and grabs the code, formatting it nicely for AI like this: The context block generated by AI Code Prep looks like this:Can you help me figure out why my program does x instead of y?

fileName.js:
<code>
... the contents of the file..
</code>

nextFile.py:
<code>
import example
...etc
</code>

Can you help me figure out why my program does x instead of y?
It writes it twice if you have that option enabled, which helps get the AI to focus better on your question/prompt. You can choose to have it on top, bottom, or both. OpenAI claims this helps, I haven't really tested to see if that's true but it seems logical. On Windows, you just right-click somewhere inside your project folder (or on the folder itself) and select "AI Code Prep GUI" from the context menu (look at the screenshots on the site). A GUI window pops up, usually with the right code files pre-selected. It smartly tries to skip things you likely don't need, like `node_modules`, `.git`, etc. If its guess isn't perfect, you can easily check or uncheck files. This is super useful when your project is huge and blows past an AI's context limit. You can manually curate exactly what the AI needs to see. The problem with many coding agents like [Cline](https://cline.bot/), Github Copilot, Cursor, Windsurf, etc., is that they often send either WAY too much context or WAY too little. This is why they can seem dumb or ineffective sometimes. Sometimes, you just gotta do things yourself, use a tool like mine to select the files yourself, but it helps auto-select the code files while skipping the stuff you probably don't need (but still have the option to add what you want with the checkboxes) and then dump that curated context into several AIs (especially the free web ones!). There are other context-generating tools, but many are command-line only, or need a public GitHub repo link. What if your code is private? What if you want to keep it local? What if you prefer checkboxes on a GUI? For something like this a GUI makes sense. It has some other things I haven't seen anywhere else like the preset buttons, dual placement of problem/prompt, per-project saves. 

Model Strategy: Picking the Right Brain for the Job

Model Strategy: Picking the Right Brain for the Job

Since many great models are free to use via web interfaces (like Gemini in AI Studio, Grok, Deepseek), I prioritize these. Poe.com also gives free daily credits for top models like Claude and the new o4 series.

Gemini 2.5 Pro (via AI Studio) is great for debugging, great for planning, and also finding it the best at lots of things now. For really thorny issues, I might try the new o4-mini (available via OpenRouter or Poe). It surprisingly fixed a persistent bug for me right away, though I'm still figuring out its best use cases. It's notably cheaper via API than the previous top dogs like Claude 3.5/3.7/4.

I usually try Claude 3.7 or 4 at some point, via Poe or API (OpenRouter makes this easy), or github Copilot chat (you can get some free usage from that if you don't pay) but it's pricier for frequent use. Think of Claude 3.7 and 4 as Claude on Adderall – brilliant, sometimes verbose, maybe a bit 'psychotic' like Hunter S. Thompson. Lots of great output, but you might need a calmer model like Claude 3.5 to refine it or do the actual coding.

For really hard problems, try using OpenAI's o3 or GLM 4.5, Qwen3 Coder 480b. You can get lots of free daily tokens if you set your account to allow sharing of your data to help train models. Go to the Open AI Playground page, click the settings icon in the upper right, then click Data Controls on the left sidebar, then Sharing on the displayed page, there you can change the "Share inputs and outputs with OpenAI" setting to Enabled, which will give you:

• Up to 250 thousand tokens per day across gpt-5, gpt-4.1, gpt-4o, o1 and o3
• Up to 2.5 million tokens per day across gpt-4.1-mini, gpt-4.1-nano, gpt-4o-mini, o1-mini, o3-mini, o4-mini, and codex-mini-latest

This is really awesome, o3 and GPT 4.5 seem super genius! Sometimes in the OpenAI Playground, I have it set up to use o3 and o4-mini side-by-side, to compare them. This helps me get a feel for which ones are best for which types of problems.

Claude 4 and 3.7 is always a good option to try and fix hard problems quickly, it is just harder to access it for cheap or free. But it is often the best out of them all. When you really need to fix something fast, use it. Poe has free tokens for all models, daily. OpenRouter has all models paid and/or free. Claude 3.7 is Claude on caffeine – brilliant, sometimes verbose, maybe a bit 'psychotic' like Hunter S. Thompson. Lots of great output, but you might need a calmer model like Claude 3.5 / 4 to refine it or do the actual coding.

The Hybrid Approach: Premium Planning + Budget Execution

After extensive testing with various models, I've developed a hybrid strategy that maximizes both quality and cost-effectiveness. The key insight is that different models excel at different parts of the development process.

My "smart juice" theory of model intelligence

How Models become stupid under certain circumstances: AI models are usually smarter the less text you send to them. Think of each model as having a fixed amount of "intelligence" or "smart juice" available for every question or problem you ask. When you send a simple, focused prompt, nearly 100% of that intelligence is available to solve your problem. But the more complex your input—long agentic instructions about how to use tools, lots of context unrelated to your specific problem, or multiple pages of code—the more of that "smart juice" gets used up just processing the unrelated stuff like how it can use tools in your IDE, leaving less intelligence energy available for your actual problem.

This is why tools like Cursor, Cline, and other agentic systems can sometimes seem less effective: if they send five giant pages of instructions and context before even getting to your real question, the model's available intelligence for your specific problem drops. The more "stuff" you send, the more diluted the model's focus becomes. For best results, keep your prompts as concise and targeted as possible—curate the context so the model can use its full intelligence on what matters most.

When you have a hard problem or bug, you will usually save time by using AI Code Prep to dump it into a web chat (as discussed on page 1 of this guide). It cuts out all the extra instructions and stuff that get sent in agentic IDEs/apps. I noticed that this works better even if you give the AI ALL of the files from your project. The agentic instructions/stuff/bloat that is unrelated to your actual problem is the content that seems to make the AI dumber/run out of juice.

My Workflow is something like this when starting a new project:

1. Plan & Brainstorm: Use the smarter/free web models (Gemini 2.5, o4-mini, Claude 3.7, 4, o3, etc) to figure out the approach, plan the steps, identify libraries, etc.
2. Generate Agent Prompt: Ask one of these smart models: "Write a detailed-enough prompt for Cline, my AI coding agent, to complete the following tasks: [describe tasks]". Sometimes, I'll copy this generated prompt and paste it into another free AI good at rewriting (like ChatGPT) to refine it further.
3. Execute with Cline: Paste the step-by-step task list into Cline, configured to use a stable and efficient model like GPT 4.1 or Claude 3.5 (or Claude 4 if it is doing really complicated things). The 4.1's have been trained to follow instructions well.
4. Fallback: If GPT 4.1 struggles, switch Cline to use Claude 3.5 via API. It seems to be the next best for reliable execution. Deepseek v3 or R1 is really great at following instructions as well.

Essentially: Use expensive/smart models (and the excellent free Gemini 2.5 Pro) to strategize and plan. Validate the plan by pasting it into 2-3 other free models (Deepseek R1, Claude on Poe if context allows) and ask "Is this good? Can you improve it or find flaws?". Then, use a stable workhorse like GPT 4.1 or Claude 3.5 within Cline to do the heavy lifting (coding).

o4-mini seems particularly adept at untangling complex code logic or figuring out high-level implementation strategies (like choosing frameworks or libraries). I'll often throw my initial idea at Gemini 2.5, o4-mini, GPT 4.1, ChatGPT, maybe o3-mini (try duck.ai - often free), and Phind to get a range of ideas. If the free/cheap options don't crack it, I'll escalate to pricier models via API.

Alternative Agents & Setups

Trae.ai (from Bytedance, makers of TikTok) is a free VS Code compatible IDE with free AI usage, including Claude 4, Claude 3.7, Claude 3.5, and GPT 4.1. Their agents aren't as good as Cline (nothing is as good, to be honest!) but it's free and gives access to the best models. Sometimes, I find its built-in agent isn't as robust as Cline. However, since Trae seems to be a VS Code clone, you can likely install the Cline extension within it! However... it is too overloaded to get any free usage from it, its too slow. I'll still mention it though.. but meh.

So, you could have two setups:

• VS Code + Cline extension + Copilot extension (get the $10/mo subscription for cheap API access via Cline, though the free tier might offer some basic use).
• Trae.ai + Cline extension (potentially leveraging Trae's free model access if Cline can use it, or using your own API keys).

Try both! Sometimes the native Copilot agent solves things Cline struggles with, and vice-versa. I suspect Cline sometimes sends overly large prompts which might hinder performance on certain tasks compared to the more integrated Copilot agent.

Roo Code: Cline's Clone

Roo Code is a clone of Cline, very similar but with some different features that are worth trying out. Sometimes Cline might work better for your workflow, and sometimes Roo Code will. It's a good idea to try both and see which fits your needs for a given project or coding style.

Cline for VS Code is free, but remember you pay for the API calls unless you're leveraging the Copilot subscription trick. Using the VS Code LM API setting in Cline with a $10/month Copilot sub is currently the most cost-effective way to get near-unlimited access to powerful models within the agent.

New CLI Tools: Claude Code, Qwen Code, Gemini CLI

There's a lot of buzz about new CLI tools for coding, especially Claude Code, Qwen Code, and Gemini CLI. People rave about Claude Code's capabilities, though I haven't tried it myself yet. When I do, I plan to set it up to use GLM 4.5 instead (there's a guide for this on the z.ai website).

Claude Code supports subagents—these are agents that only do one task and don't use extra tools. This setup can mimic the streamlined workflow described in this guide, focusing the model's intelligence on a single job. Subagents are a clever way to avoid the "bloat" of agentic instructions and keep things efficient.

If you want to experiment, check out the guides and community tips for configuring these tools. The ecosystem is evolving quickly, and each tool has its own strengths for different workflows.

TL;DR & Model Updates – September 2025

TL;DR: Quickstart Guide

• Models & Roles:
• Planning & Brainstorming: GLM 4.5, Kimi K2, the newest Qwen3 Coder and 2507's, Gemini 2.5 Pro (AI Studio), o4-mini (OpenRouter), Claude 3.7 or 4 (Poe), if you have OpenAI Playground configured for the 250k free daily tokens, I recommend using those up with o3 and GPT 5.
• Problem Solving & Debugging: GPT-5 (free tokens in Playground), GLM-4.5 (it seems to be a genius, about Claude 4 level) Claude 4 (free daily on Poe)
• Actual Coding: GPT-4.1 via Cline; fallback to Claude 3.5.. or the new ones: Qwen3 Coder, Instruct, 2507, GLM 4.5, Kimi K2.
• Key Tools:
• VS Code
• AI Code Prep GUI – locally scan & curate only the files you need, saves so much time
• Cline (VS Code agent) for step-by-step code execution
• Free web chats for multi-perspective advice: Poe.com, ChatGPT, Grok, Deepseek, Perplexity, OpenAI Playground, AI Studio w/Gemini 2.5 Pro, Openrouter, duck.ai
• Quick Workflow: 1. Run AI Code Prep GUI to bundle your (if already existing) project's relevant files. 2. Paste that context into your favorite web chat models for planning & debugging. 3. Ask one model to "Write me a detailed Cline prompt for these tasks," then refine it (e.g. in ChatGPT). 4. Copy/paste into Cline set to GPT-4.1 to generate or fix code; if it stalls, switch to Claude 3.5.
• Cost-Saving Hacks:
• Enable "share data" in OpenAI Playground for 250k free GPT-4.5, o3, (both genius expensive models) & 2.5 MILLION free tokens/day for o4-mini, o3-mini!!
• $10/mo GitHub Copilot subscription gives you rate-limited access to Claude models via Cline
• Pay-as-you-go on OpenRouter for o4-mini, Claude 3.7, and other new models

## Note about some free CLI agent tools Currently as of end of August 2025, Qwen Code (using the GREAT Qwen3 Coder 480b model!) is totally free, 1000-2000 requests a day (I have not run into limits with heavy use). It is an agentic tool that runs in the terminal, but you can also 'borrow' the API key to use in anything. Kilo Code currently has it set up automatically, just choose the Qwen provider. Gemini CLI is also free with high limits but I have heard about getting limited fast. Claude Code is currently said to be the best CLI tool, but it really eats tokens if you aren't getting them for free. Claude Code Router, a free repo on github, allows you to use whatever API you want for Claude Code. OpenCode.ai is another one, it allows me to use my unlimited GPT 4.1 from the $10/month co-pilot subcription. I still have not found that any of these are as good as doing it my way where I use [AI Code Prep GUI](https://wuu73.org/aicp) to plan with models on their native web chat's, then implement with GPT 4.1. Maybe I should try to create my own agentic tool, that separates things so that difficult problems are sent to the smartest AIs without the tools, MCPs, emulating this way of doing it, then send to smaller model for coding.

Latest Model Updates (Aug 2025)

💰 Budget-Conscious: Getting Max Value

GPT 4.5 (DISCONTINUED)

• This model was discontinued.

o3

• Possibly equal to Claude 4 in abilities, really great at fixing hard problems, genius level. Throw your entire codebase in here with AI Code Prep GUI
• Free Tokens: 250k daily when you enable data sharing in Data Controls/Sharing settings

o4-mini

• Not as smart as o3, but very very good, like o3's younger brother
• Free Tokens: 2.5 million daily when you enable data sharing in Data Controls/Sharing settings

Gemini 2.5 Pro

• Free to use in AI Studio. Also very good at fixing hard problems
• Best For: Complex debugging and architectural planning

Deepseek R1 0528

• Super smart model with enhanced reasoning capabilities
• Availability: Free on Deepseek's web interface

🚀 Premium: Fix Problems NOW

Claude 4 Sonnet

• The mega genius, can fix most problems in one shot if you provide enough context (AICodePrep is what I use). These also seem to be the best writers, best at everything really.. the secret sauce
• Use Case: When you absolutely need it fixed right the first time

Claude 4 Opus

• $75 per million tokens, haven't tried it yet myself but I hear its a super mega genius like Sonnet but even better, magic sauce
• Performance: Rumored to be the ultimate problem solver

Solid Worker Models

These models listen to instructions really well and do as they are told:

GPT 4.1

• Use the above smarter models for abstract high level design or to fix problems, then 4.1 to make changes. You can cut and paste output from anywhere else right into Cline with 4.1 to do the coding.

Claude Sonnet 3.5

• Good solid model for coding and editing, slightly slower vs 4.1 but very reliable.

Deepseek v3

• Pretty good and very cheap for a model that can do edits/code/agent work.

OpenRouter Free Models

• Drag the price filter to $0 on OpenRouter to see free models available for testing. Worth experimenting with new ones as they become available.

Free Claude 4: lmarena.ai, and More

Claude Opus 4 and lmarena.ai

• lmarena.ai offers free access to Claude Opus 4 and Sonnet 4 and others.
• Any free usage of anything by Anthropic is worth saving, remembering, and using. When all else fails, or when you need to get stuff done, perfectly, and now, choose Claude 4 Sonnet or Opus.

Latest Model Updates (2025)

Claude Sonnet 4 & Opus 4

• Status: Just released and proving to be the best models for everything
• Performance: Fixed some bugs today that were hard for most other models to handle
• Cost: More expensive, but accessible through GitHub Copilot $10/month (rate limited)
• GitHub Copilot Value: The cheap $10/mo GitHub Copilot subscription seems to be giving me a very generous amount of Claude 4 uses through the VS Code LM API. I keep using it as my main model in Cline/Roo Code, and it has not run out yet. I either use GPT 4.1 or Claude 4, been waiting for it to limit me but it hasn't yet (they say that GPT 4.1 is unlimited). I always hate subscriptions for anything, but this particular one is insanely good value.
• Strategy: Save Claude 4 for tough problems, use GPT 4.1 for regular coding

GPT 4.5

• Performance: Excellent for bug fixing and complex problem solving
• Token Limits: 250k tokens daily if you allow data for model training
• Cost Hack: Free under the 250k token limit when you enable data sharing in settings
• Use Case: Great for using AI Code Prep tool to analyze entire codebases

## NEW!! Bad ass new Chinese models + GPT 5 **GLM 4.5** - Very much like Claude 4 Opus or Sonnet; follows agentic rules and uses tools near perfectly. Fixes really hard bugs and handles complicated tasks with lots of context. **Qwen3 Coder 480B** - Another super great one; a favorite for being powerful and cheap. **Qwen3 Instruct & Thinking 2507** - Similar to Qwen3 Coder—strong, dependable, and cost-effective. **Kimi K2 (Moonshot)** - Feels Anthropic-inspired or trained on Claude-like synthetic data. Really good; used a lot. **GPT-5** - **edit** -- OpenAI has vastly improved GPT-5 since I wrote this and it is actually pretty decent at tools now....but the first week was bad which is when i wrote this: Not very good at custom tool usage (e.g., your own tools/MCP/Cline). Better to plan and fix like this guide suggests using GPT 5 and other insanely great models like GLM 4.5, then have it write a prompt for a simpler agent model to do actual editing and tool usage. GPT 4.1 still wins on value, and the new Chinese models handle custom tools/Cline easily. I have not played around with this one yet to know if its good but usually all models are going to be great at certain things. I am honestly more excited about the Chinese models, because of cost and the experiences so far have proven them to be reliably great pretty much all the time.

Current Coding Workflows that I use (2025)

For New Projects:

1. Planning Phase: Type your idea(s) and ask several AI's in web chat's to brainstorm with you and help you figure out a real plan, into notepad or a blank file. - this is just so you can paste into multiple web chat's
2. Multi-Model Consultation: Paste into two or three of these models web chat's for different "doctor's opinions": - Gemini 2.5 Pro (free) - GLM 4.5 (free and unlimited!) - Qwen3-Coder or one of the -2507 models - o4-mini on OpenAI Playground to use the free 2.5mil daily tokens - Claude 4 on Poe.com (free daily credits)
3. Refinement: Go back and forth to fine-tune details
4. Task Generation: Have model write a PRD (Product Requirements Document) markdown file or similar, like "APP_REQUIREMENTS.md" or website requirements etc with a list of all the things it must have. Then have it write step-by-step task list with subtasks, for an AI coding agent to implement. Save this to a new project folder - and you might want to run 'git init' if you use Git.
5. Execution: Copy/paste into Cline (or just tell it "your task is in the project requirements .md file") set to GPT 4.1 for 'act' mode (or Qwen 3 Coder, GLM 4.5 Flash.. also free currently and excellent). If you prompt the web models to break a big project into small enough sub-tasks, then you can get away with using a cheaper implementation/agent model. "Cheap" right now for me is GPT 4.1 using the Github Copilot API (I don't even use the actual copilot much I just like the unlimited 4.1 - also every month you get credits to use all the top expensive models like Opus/Sonnet 4 which are great for top level planning, you can also use copilot's own web chat interface)

For Problem Solving, fixing hard bugs:

1. Load AI Code Prep GUI tool (type 'aicp' in terminal) and type the problem into the prompt box, look at which files are already selected to make sure that it looks good for context (when in doubt, add more code files, usually it helps)
2. Set the options to 'Add prompt to top' and ''..bottom' is usually how i leave that setting.. it seems like it would be wasteful but it seems to help focus the model on the problem better than only having the prompt on top or bottom
3. If I am confident that it will be able to fix my problem I will click one of the preset buttons for Cline/Roo Code or one for an agent prompt which just pastes some extra text to the end of the current text in the prompt box, telling the AI to write a solution for an agent to implement the changes (experimenting with this will be the easiest way to understand what it is for - it just saves you from typing that)
4. Click the "GENERATE CONTEXT!" button, and if its a hard problem.. i'll paste it into Gemini 2.5 Pro, GLM 4.5, Kimi K2, Qwen3 Coder or 2507 Thinking.. all on their native web chat website (Qwen has an annoying web chat where it pastes it into a file so you have to tell it to read the file)
5. Sit back and watch several solutions stream in, bathroom break. Using several AI's from several different companies is a great way to get some diversity in the outputs and it is better than for example.. just using 5 OpenAI models or just using Anthropic's models. It reminds me of how people will have healthier kids if they mate with people from different cultures/different parts of the world. It just produces a faster / better solution. You can even cut and paste the outputs of all of these into Gemini 2.5 Pro (its got such a large context window to handle it) and have AI again compare them all and produce a final "best of all" solution.
6. Problem solving like this will often "one shot" a perfect solution, instead of letting agents run around your file system trying to slowly figure out solutions with all the tools and MCPs and other stuff it is often just faster to do it this way!

Task List & Test Driven Development (Coming Soon)

Test Driven Development & Task Lists:

Coming to this guide soon (these other topics)Have the AIs create a detailed task list for Cline, Roo Code, Trae agent, to execute. You can also instruct Cline or Roo Code to use a markdown file to keep track of everything it does, checking things off as it completes them. This will make it easier to track and ensure nothing gets missed.

For now, you can experiment by having a model generate a checklist in markdown, and then ask Cline or Roo Code to update the file as tasks are completed.

Money-Saving Hacks

• GLM 4.5, Kimi K2, o3: 250k tokens free daily by enabling data sharing for model training
• Cheaper Models: 2.5 million tokens on o4-mini (excellent model), 4.1-mini/nano, GPT 5 Mini
• GitHub Copilot: $10/month gives access to new Claude models (rate limited) and GPT 4.1 UNLIMITED for agent work
• Qwen3 has new models Coder and 2507's
• Poe.com: Free daily credits for every type of model
• Web Interfaces: Use free web chat interfaces for planning and consultation

Coming Soon: Live Reddit Data & Insights

Live Reddit Data Scraping & Daily Insights:

A new feature is coming soon: live scraping of Reddit data and daily updated info about how people are using AI models. This will include detailed usage breakdowns, data visualizations, and new insights into real-world coding workflows and trends.

If you buy the Pro version of [aicodeprep-gui](https://tombrothers.gumroad.com/l/zthvs), you will get updated content about more free stuff, free api's, updated model information, any new cost hacks I discover but updated more frequently

AI Coding Tools & Alternatives – September 2025

AI Guide 4: AI Coding Tools & Alternatives

🎯 September 2025 Updates: Free & Cheap Coding Models

GLM 4.5 - The Best Coding Model Right Now

• Price: Only $3-6/month
• Limits: 120 prompts every 5 hours (users report rarely hitting limits)
• Access: z.ai subscription

#### 🔧 Compatible Tools & Integrations GLM 4.5 works seamlessly with these coding assistants using a simple API key: - **Claude Code** - Special Anthropic compatible endpoint available - **Cline** - Direct API integration - **Roo Code** - Full compatibility - **Kilo Code** - API key setup - **OpenCode** - Non-subscription API access (recommended)

✅ Claude Code Works Great with GLM 4.5!

Recommended Setup: Claude Code works excellently with GLM 4.5 from z.ai using their Anthropic-compatible API endpoint. You can use either:

• $3-6/month subscription - Great value with generous limits
• Pay-as-you-go API - Only pay for what you use

Note: I tried using Claude Code Router to allow any OpenAI-compatible API endpoint, but it barely works and I don't recommend it. Just use the official z.ai GLM 4.5 API directly - it's much more reliable!

🔥 Recommended: OpenCode.ai

Why OpenCode.ai is Excellent

• Multiple Models: Works great with GLM-4.5 and many other top models
• Agent Setup: Like Claude Code, you can set up agents that get full fresh context for subtasks
• Superior Performance: Much better output/code than Cline, Roo Code, or Kilo Code
• Fast Iteration: Can quickly iterate and test code changes
• Reliable: Stable and consistent performance

Note: Claude Code is very good with GLM 4.5, but OpenCode offers better stability. OpenCode may have more options than other alternatives.

💰 Chutes.ai - Affordable API Provider

Pricing & Features

• $3/month: 300 requests per day of any model + some free models
• $10/month: 1000-2000 requests per day (resets daily!)
• Top Models Available: GLM 4.5, Qwen3 Coder 480B, Kimi K2 (Sept updated), and more

Note: I typically avoid subscriptions, but this one offers exceptional value for the model access.

**WSL Setup Tip:** Running Claude Code in WSL (Ubuntu inside Windows) tends to be more stable than native Windows versions, while still allowing interaction with Windows files.#### 🚀 Current Top Models Available Most of the best models right now are accessible through Chutes.ai: GLM 4.5, the new Qwen3 Coder 480B, Kimi K2 September update, and many more cutting-edge models at fraction of the cost of direct API access.

💡 Free vs. Really Cheap: The Smart Choice

Yes, You CAN Code 100% for Free, BUT...

It's much better to code for REALLY CHEAP! $10 or less a month is good enough. Just a little bit of money spent in the right places from this guide buys extremely good reliability.

The Reality of "Free" Resources

With the few totally free resources out there, you will often be stuck waiting for API calls. Well... that's not totally right because at the moment you can use Qwen Code with their best coding model pretty much unlimited and it works great (not as good as GLM 4.5 though). I believe Gemini CLI will also let you have generous API calls.

My Current Setup (Total: ~$13/month)

• GitHub Copilot: Unlimited GPT-4.1 + limited GPT-5 & Claude 4
• Chutes.ai: $3/month for access to cutting-edge models

Bottom Line: It's really basically a free-for-all right now - no need to be complaining about Claude's $200/month crap! Smart spending on the right tools gives you professional-grade AI coding assistance for pocket change.

My Browser Setup: The Free AI Buffet

First things first, I have a browser open loaded with tabs pointing to the free tiers of powerful AI models. Why stick to one when you can get multiple perspectives for free? My typical lineup includes:

• At least one tab of OpenAI Playground. If you set your account's data settings to allow OpenAI to use your data for model training, you get free tokens to use on GPT-4.5, o3, and other models.
• At least one tab but more like three of Google Gemini AI Studio (Gemini 2.5 Pro/Flash are often free and unlimited here).
  Also, try Google Gemini 2.5 Pro (different than AI Studio, has better image generation and deep research; I always have a couple tabs of this along with a couple tabs of AI Studio).
• Couple tabs of Poe.com usually set to Claude 4 or o4-mini for its free daily credits on premium models.
• Several tabs of OpenRouter, set to several models, some free models, some not.
• At least one tab for ChatGPT (the free version is still useful).
• At least one tab for Perplexity AI, especially good for research-heavy questions.
• At least one tab for Deepseek (v3 and r1 are free on their web interface, though watch the context limit).
• One tab for Grok.com. Good, free and seemingly unlimited for general use and deep research/image editing. I mainly use the deep research feature, similar to perplexity.
• Phind is another free one, it tries to show you flowcharts/diagram visuals.
• lmarena.ai offers free access to Claude Opus 4 and Sonnet 4 and others. Free Opus 4 is so good.

[Claude.ai](https://claude.ai/new) - Free but sometimes so limited it's annoying to use, so I use other sites/ways to access Claude like Cody extension, Copilot, etc.### ⚠️ Important Disclaimer about Grok Grok offers free compute and uncensored image generation, which can be useful when other models' safety systems interfere with legitimate tasks. However, reports indicate that Grok has been instructed to lie about about some things. While the misinformation appears to be mostly on X, if you keep in mind to restrict usage to coding or be cautious knowing it might be programmed with questionable motives, it can occasionally be useful. It was never that great though but its sometimes okay.

A smarter, cheaper workflow: Focused Context

When you use AI in web chat's (the chat interfaces like AI Studio, ChatGPT, Openrouter, instead of thru an IDE or agent framework) are almost always better at solving problems, and coming up with solutions compared to the agents like Cline, Trae, Copilot.. Not always, but usually.

When you use things like Cursor, Cline, Roo Code for everything, they are sending tons of text to the AI about how to use their tools, how to use or activate MCP server stuff, edit files, etc it "dumbs it down" too much. It gets confused. People end up paying for the most expensive best models to do everything and even that isn't enough to get over the dumbing down effect from the AIs getting tons of unneeded information unrelated to your problem.

So when that happens, I use my tool to generate the right context to solve my problem. Then I paste it into one of the many AI web chat's (sometimes more than one, since they sometimes give different answers) and just ask it questions or ask it to code review, to try to figure out why x is happening when y is happening...etc then when it figures out a solution.. i have it write a prompt for Cline or another agent type thing to do the actual file edits. GPT 4.1 can handle this just fine and I have unlimited. No reason to be wasting Claude credits to edit files. No reason to be sending Claude a bunch of crap it doesn't need making it dumb. I can use Claude to plan out anything or fix really hard problems, cheap, using Openrouter web chat then just paste it back in Cline and let it run.

After doing this for a while, you really get a feel for which models excel at which types of tasks.

**How AI Code Prep Helps (Example Prompt Structure):** _Can you help me figure out why my program does x instead of y?_ Then, [AI Code Prep GUI](https://wuu73.org/aicp) (for Windows, Mac, Linux, and web) steps in. It recursively scans your project folder (subfolders, sub-subfolders, you name it) and grabs the code, formatting it nicely for AI like this: The context block generated by AI Code Prep looks like this:Can you help me figure out why my program does x instead of y?

fileName.js:
<code>
... the contents of the file..
</code>

nextFile.py:
<code>
import example
...etc
</code>

Can you help me figure out why my program does x instead of y?
It writes it twice if you have that option enabled, which helps get the AI to focus better on your question/prompt. You can choose to have it on top, bottom, or both. OpenAI claims this helps, I haven't really tested to see if that's true but it seems logical. On Windows, you just right-click somewhere inside your project folder (or on the folder itself) and select "AI Code Prep GUI" from the context menu (look at the screenshots on the site). A GUI window pops up, usually with the right code files pre-selected. It smartly tries to skip things you likely don't need, like `node_modules`, `.git`, etc. If its guess isn't perfect, you can easily check or uncheck files. This is super useful when your project is huge and blows past an AI's context limit. You can manually curate exactly what the AI needs to see. The problem with many coding agents like [Cline](https://cline.bot/), Github Copilot, Cursor, Windsurf, etc., is that they often send either WAY too much context or WAY too little. This is why they can seem dumb or ineffective sometimes. Sometimes, you just gotta do things yourself, use a tool like mine to select the files yourself, but it helps auto-select the code files while skipping the stuff you probably don't need (but still have the option to add what you want with the checkboxes) and then dump that curated context into several AIs (especially the free web ones!). There are other context-generating tools, but many are command-line only, or need a public GitHub repo link. What if your code is private? What if you want to keep it local? What if you prefer checkboxes on a GUI? For something like this a GUI makes sense. It has some other things I haven't seen anywhere else like the preset buttons, dual placement of problem/prompt, per-project saves.

Note: This page isn't updated with all the latest AI Code Prep GUI features, check wuu73.org/aicp for latest major upgrade

Model Strategy: Picking the Right Brain for the Job

Since many great models are free to use via web interfaces (like Gemini in AI Studio, Grok, Deepseek), I prioritize these. Poe.com also gives free daily credits for top models like Claude and the new o4 series.

Gemini 2.5 Pro/Preview (via AI Studio) is great for debugging, great for planning, and also finding it the best at lots of things now. For really thorny issues, I might try the new o4-mini (available via OpenRouter or Poe). It surprisingly fixed a persistent bug for me right away, though I'm still figuring out its best use cases. It's notably cheaper via API than the previous top dogs like Claude 3.5/3.7/4.

I usually try Claude 3.7 or 4 at some point, via Poe or API (OpenRouter makes this easy), or github Copilot chat (you can get some free usage from that if you don't pay) but it's pricier for frequent use. Think of Claude 3.7 and 4 as Claude on Adderall – brilliant, sometimes verbose, maybe a bit 'psychotic' like Hunter S. Thompson. Lots of great output, but you might need a calmer model like Claude 3.5 to refine it or do the actual coding.

My Workflow is something like this when starting a new project:

1. Plan & Brainstorm: Use the smarter/free web models (Gemini 2.5, o4-mini, Claude 3.7, 4, o3, etc) to figure out the approach, plan the steps, identify libraries, etc.
2. Generate Agent Prompt: Ask one of these smart models: "Write a detailed-enough prompt for Cline, my AI coding agent, to complete the following tasks: [describe tasks]". Sometimes, I'll copy this generated prompt and paste it into another free AI good at rewriting (like ChatGPT) to refine it further.
3. Execute with Cline: Paste the step-by-step task list into Cline, configured to use a stable and efficient model like GPT 4.1 or Claude 3.5 (or Claude 4 if it is doing really complicated things). The 4.1's have been trained to follow instructions well.
4. Fallback: If GPT 4.1 struggles, switch Cline to use Claude 3.5 via API. It seems to be the next best for reliable execution. Deepseek v3 or R1 is really great at following instructions as well.

Essentially: Use expensive/smart models (and the excellent free Gemini 2.5 Pro) to strategize and plan. Validate the plan by pasting it into 2-3 other free models (Deepseek R1, Claude on Poe if context allows) and ask "Is this good? Can you improve it or find flaws?". Then, use a stable workhorse like GPT 4.1 or Claude 3.5 within Cline to do the heavy lifting (coding).

o4-mini seems particularly adept at untangling complex code logic or figuring out high-level implementation strategies (like choosing frameworks or libraries). I'll often throw my initial idea at Gemini 2.5, o4-mini, GPT 4.1, ChatGPT, maybe o3-mini (try duck.ai - often free), and Phind to get a range of ideas. If the free/cheap options don't crack it, I'll escalate to pricier models via API.

Alternative Agents & Setups

Trae.ai (from Bytedance, makers of TikTok) is a free VS Code compatible IDE with free AI usage, including Claude 4, Claude 3.7, Claude 3.5, and GPT 4.1. Their agents aren't as good as Cline (nothing is as good, to be honest!) but it's free and gives access to the best models. Sometimes, I find its built-in agent isn't as robust as Cline. However, since Trae seems to be a VS Code clone, you can likely install the Cline extension within it! However... it is too overloaded to get any free usage from it, its too slow. I'll still mention it though.. but meh.

So, you could have two setups:

• VS Code + Cline extension + Copilot extension (get the $10/mo subscription for cheap API access via Cline, though the free tier might offer some basic use).
• Trae.ai + Cline extension (potentially leveraging Trae's free model access if Cline can use it, or using your own API keys).

Try both! Sometimes the native Copilot agent solves things Cline struggles with, and vice-versa. I suspect Cline sometimes sends overly large prompts which might hinder performance on certain tasks compared to the more integrated Copilot agent.

Roo Code: Cline's Clone

Roo Code is a clone of Cline, very similar but with some different features that are worth trying out. Sometimes Cline might work better for your workflow, and sometimes Roo Code will. It's a good idea to try both and see which fits your needs for a given project or coding style.

Cline for VS Code is free, but remember you pay for the API calls unless you're leveraging the Copilot subscription trick. Using the VS Code LM API setting in Cline with a $10/month Copilot sub is currently the most cost-effective way to get near-unlimited access to powerful models within the agent.

TL;DR: Quickstart Guide

• Models & Roles:
• Planning & Brainstorming: Gemini 2.5 Pro (AI Studio), o4-mini (OpenRouter), Claude 3.7 (Poe), if you have OpenAI Playground configured for the 250k free daily tokens, I recommend using those up with o3 and GPT 4.5... VERY good
• Problem Solving & Debugging: GPT-4.5 (free tokens in Playground), Claude 4 (free daily on Poe)
• Actual Coding: GPT-4.1 via Cline; fallback to Claude 3.5 if needed
• Key Tools:
• VS Code and Trae for editing/running
• AI Code Prep GUI – locally scan & curate only the files you need, saves so much time
• Cline (VS Code agent) for step-by-step code execution
• Free web chats for multi-perspective advice: Poe.com, ChatGPT, Grok, Deepseek, Perplexity, OpenAI Playground, AI Studio w/Gemini 2.5 Pro, Openrouter, duck.ai
• Quick Workflow: 1. Run AI Code Prep GUI to bundle your (if already existing) project's relevant files. 2. Paste that context into your favorite web chat models for planning & debugging. 3. Ask one model to "Write me a detailed Cline prompt for these tasks," then refine it (e.g. in ChatGPT). 4. Copy/paste into Cline set to GPT-4.1 to generate or fix code; if it stalls, switch to Claude 3.5.
• Cost-Saving Hacks:
• Enable "share data" in OpenAI Playground for 250k free GPT-4.5, o3, (both genius expensive models) & 2.5 MILLION free tokens/day for o4-mini, o3-mini!!
• $10/mo GitHub Copilot subscription gives you rate-limited access to Claude models via Cline
• Pay-as-you-go on OpenRouter for o4-mini, Claude 3.7, and other new models

## Some Thoughts AI is an incredible force multiplier, but it's not a magic wand. The real magic happens when you combine your curiosity, persistence, and willingness to experiment with these powerful tools. Don't get discouraged by bugs or setbacks—every challenge is a chance to learn something new. Mix and match models, try wild ideas, and don't be afraid to break things and rebuild. The best coders aren't the ones who never get stuck—they're the ones who keep moving forward, using every tool and trick at their disposal. Embrace the chaos, enjoy the process, and let your creativity lead the way!

Latest Model Updates (2025)

Claude Sonnet 4 & Opus 4

• Status: Just released and proving to be the best models for everything
• Performance: Fixed some bugs today that were hard for most other models to handle
• Cost: More expensive, but accessible through GitHub Copilot $10/month (rate limited)
• GitHub Copilot Value: The cheap $10/mo GitHub Copilot subscription seems to be giving me a very generous amount of Claude 4 uses through the VS Code LM API. I keep using it as my main model in Cline/Roo Code, and it has not run out yet. I either use GPT 4.1 or Claude 4, been waiting for it to limit me but it hasn't yet (they say that GPT 4.1 is unlimited). I always hate subscriptions for anything, but this particular one is insanely good value.
• Strategy: Save Claude 4 for tough problems, use GPT 4.1 for regular coding

GPT 4.5

• Performance: Excellent for bug fixing and complex problem solving
• Token Limits: 250k tokens daily if you allow data for model training
• Cost Hack: Free under the 250k token limit when you enable data sharing in settings
• Use Case: Great for using AI Code Prep tool to analyze entire codebases

Current Coding Workflow (2025)

For New Projects:

1. Planning Phase: Type all details in notepad (languages, libraries, servers, etc.)
2. Multi-Model Consultation: Paste into multiple models for different "doctor's opinions": - Gemini 2.5 Pro (free) - GPT 4.1 - o4-mini - Claude 4 on Poe.com (free daily credits)
3. Refinement: Go back and forth to fine-tune details
4. Task Generation: Have model write step-by-step task list for Cline AI coding agent
5. Execution: Copy/paste into Cline (or Roo Code) set to GPT 4.1 for 'act' mode

For Problem Solving:

• Use GPT 4.5 with AI Code Prep tool for complex codebase analysis
• Ask GPT 4.5 to "write me a prompt for Cline" to complete tasks
• Choose models based on problem complexity
• Use multiple models for different perspectives

Task List & Test Driven Development (Coming Soon)

Test Driven Development & Task Lists:

Coming to this guide soon (these other topics)Have the AIs create a detailed task list for Cline, Roo Code, Trae agent, to execute. You can also instruct Cline or Roo Code to use a markdown file to keep track of everything it does, checking things off as it completes them. This will make it easier to track and ensure nothing gets missed.

For now, you can experiment by having a model generate a checklist in markdown, and then ask Cline or Roo Code to update the file as tasks are completed.

Money-Saving Hacks

• GPT 4.5 & o3: 250k tokens free daily by enabling data sharing for model training
• Cheaper Models: 2.5 million tokens on o4-mini (excellent model), 4.1-mini/nano
• GitHub Copilot: $10/month gives access to new Claude models (rate limited)
• Trae IDE currently has free (not limited either, from what I can tell) Claude 4 and GPT 4.1 no subscription required
• Poe.com: Free daily credits for every type of model
• Web Interfaces: Use free web chat interfaces for planning and consultation

Coming Soon: Live Reddit Data & Insights

Live Reddit Data Scraping & Daily Insights:

A new feature is coming soon: live scraping of Reddit data and daily updated info about how people are using AI models. This will include detailed usage breakdowns, data visualizations, and new insights into real-world coding workflows and trends.