free-coding-models

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1. Create a free API key on NVIDIA → https://build.nvidia.com
2. npm i -g free-coding-models
3. free-coding-models

Find the fastest coding LLM models in seconds
_{Ping free NVIDIA NIM models in real-time — pick the best one for OpenCode, OpenClaw, or any AI coding assistant}

Features • Requirements • Installation • Usage • Models • OpenCode • OpenClaw • How it works

✨ Features

• 🎯 Coding-focused — Only LLM models optimized for code generation, not chat or vision
• 🚀 Parallel pings — All 44 models tested simultaneously via native fetch
• 📊 Real-time animation — Watch latency appear live in alternate screen buffer
• 🏆 Smart ranking — Top 3 fastest models highlighted with medals 🥇🥈🥉
• ⏱ Continuous monitoring — Pings all models every 2 seconds forever, never stops
• 📈 Rolling averages — Avg calculated from ALL successful pings since start
• 📊 Uptime tracking — Percentage of successful pings shown in real-time
• 🔄 Auto-retry — Timeout models keep getting retried, nothing is ever "given up on"
• 🎮 Interactive selection — Navigate with arrow keys directly in the table, press Enter to act
• 🔀 Startup mode menu — Choose between OpenCode and OpenClaw before the TUI launches
• 💻 OpenCode integration — Auto-detects NIM setup, sets model as default, launches OpenCode
• 🦞 OpenClaw integration — Sets selected model as default provider in ~/.openclaw/openclaw.json
• 🎨 Clean output — Zero scrollback pollution, interface stays open until Ctrl+C
• 📶 Status indicators — UP ✅ · Timeout ⏳ · Overloaded 🔥 · Not Found 🚫
• 🔧 Multi-source support — Extensible architecture via sources.js (add new providers easily)
• 🏷 Tier filtering — Filter models by tier letter (S, A, B, C) with --tier flag or dynamically with E/D keys

📋 Requirements

Before using free-coding-models, make sure you have:

1. Node.js 18+ — Required for native fetch API
2. NVIDIA NIM account — Free tier available at build.nvidia.com
3. API key — Generate one from Profile → API Keys → Generate API Key
4. OpenCode (optional) — Install OpenCode to use the OpenCode integration
5. OpenClaw (optional) — Install OpenClaw to use the OpenClaw integration

💡 Tip: Without OpenCode/OpenClaw installed, you can still benchmark models and get latency data.

📦 Installation

# npm (global install — recommended)
npm install -g free-coding-models

# pnpm
pnpm add -g free-coding-models

# bun
bun add -g free-coding-models

# Or use directly with npx/pnpx/bunx
npx free-coding-models YOUR_API_KEY
pnpx free-coding-models YOUR_API_KEY
bunx free-coding-models YOUR_API_KEY

🚀 Usage

# Just run it — shows a startup menu to pick OpenCode or OpenClaw, prompts for API key if not set
free-coding-models

# Explicitly target OpenCode CLI (TUI + Enter launches OpenCode CLI)
free-coding-models --opencode

# Explicitly target OpenCode Desktop (TUI + Enter sets model & opens Desktop app)
free-coding-models --opencode-desktop

# Explicitly target OpenClaw (TUI + Enter sets model as default in OpenClaw)
free-coding-models --openclaw

# Show only top-tier models (A+, S, S+)
free-coding-models --best

# Analyze for 10 seconds and output the most reliable model
free-coding-models --fiable

# Filter models by tier letter
free-coding-models --tier S          # S+ and S only
free-coding-models --tier A          # A+, A, A- only
free-coding-models --tier B          # B+, B only
free-coding-models --tier C          # C only

# Combine flags freely
free-coding-models --openclaw --tier S
free-coding-models --opencode --best

Startup mode menu

When you run free-coding-models without --opencode or --openclaw, you get an interactive startup menu:

  ⚡ Free Coding Models — Choose your tool

  ❯ 💻 OpenCode CLI
       Press Enter on a model → launch OpenCode CLI with it as default

    🖥 OpenCode Desktop
       Press Enter on a model → set model & open OpenCode Desktop app

    🦞 OpenClaw
       Press Enter on a model → set it as default in OpenClaw config

  ↑↓ Navigate  •  Enter Select  •  Ctrl+C Exit

Use ↑↓ arrows to select, Enter to confirm. Then the TUI launches with your chosen mode shown in the header badge.

How it works:

1. Ping phase — All 44 models are pinged in parallel
2. Continuous monitoring — Models are re-pinged every 2 seconds forever
3. Real-time updates — Watch "Latest", "Avg", and "Up%" columns update live
4. Select anytime — Use ↑↓ arrows to navigate, press Enter on a model to act
5. Smart detection — Automatically detects if NVIDIA NIM is configured in OpenCode or OpenClaw

Setup wizard:

  🔑 Setup your NVIDIA API key
  📝 Get a free key at: https://build.nvidia.com
  💾 Key will be saved to ~/.free-coding-models

  Enter your API key: nvapi-xxxx-xxxx

  ✅ API key saved to ~/.free-coding-models

Other ways to provide the key

# Pass directly
free-coding-models nvapi-xxxx-your-key-here

# Use environment variable
NVIDIA_API_KEY=nvapi-xxx free-coding-models

# Or add to your shell profile
export NVIDIA_API_KEY=nvapi-xxxx-your-key-here
free-coding-models

Get your free API key

1. Create NVIDIA Account — Sign up at build.nvidia.com with your email
2. Verify — Confirm email, set privacy options, create NGC account, verify phone
3. Generate Key — Go to Profile → API Keys → Generate API Key
4. Name it — e.g., "free-coding-models" or "OpenCode-NIM"
5. Set expiration — Choose "Never" for convenience
6. Copy securely — Key is shown only once!

💡 Free credits — NVIDIA offers free credits for NIM models via their API Catalog for developers.

🤖 Coding Models

44 coding models across 8 tiers, ranked by SWE-bench Verified — the industry-standard benchmark measuring real GitHub issue resolution. Scores are self-reported by providers unless noted.

Tier scale

• S+/S — Elite frontier coders (≥60% SWE-bench), best for complex real-world tasks and refactors
• A+/A — Great alternatives, strong at most coding tasks
• A-/B+ — Solid performers, good for targeted programming tasks
• B/C — Lightweight or older models, good for code completion on constrained infra

Filtering by tier

Use --tier to focus on a specific capability band:

free-coding-models --tier S     # Only S+ and S (frontier models)
free-coding-models --tier A     # Only A+, A, A- (solid performers)
free-coding-models --tier B     # Only B+, B (lightweight options)
free-coding-models --tier C     # Only C (edge/minimal models)

Dynamic tier filtering with E/D keys

During runtime, use E and D keys to dynamically adjust the tier filter:

• E (Elevate) — Show fewer, higher-tier models (cycle: All → S → A → B → C → All)
• D (Descend) — Show more, lower-tier models (cycle: All → C → B → A → S → All)

Current tier filter is shown in the header badge (e.g., [Tier S])

🔌 OpenCode Integration

The easiest way — let free-coding-models do everything:

1. Run: free-coding-models --opencode (or choose OpenCode from the startup menu)
2. Wait for models to be pinged (green ✅ status)
3. Navigate with ↑↓ arrows to your preferred model
4. Press Enter — tool automatically:
   • Detects if NVIDIA NIM is configured in OpenCode
   • Sets your selected model as default in ~/.config/opencode/opencode.json
   • Launches OpenCode with the model ready to use

Manual OpenCode Setup (Optional)

Create or edit ~/.config/opencode/opencode.json:

{
  "provider": {
    "nvidia": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "NVIDIA NIM",
      "options": {
        "baseURL": "https://integrate.api.nvidia.com/v1",
        "apiKey": "{env:NVIDIA_API_KEY}"
      }
    }
  },
  "model": "nvidia/deepseek-ai/deepseek-v3.2"
}

Then set the environment variable:

export NVIDIA_API_KEY=nvapi-xxxx-your-key-here
# Add to ~/.bashrc or ~/.zshrc for persistence

Run /models in OpenCode and select NVIDIA NIM provider and your chosen model.

⚠️ Note: Free models have usage limits based on NVIDIA's tier — check build.nvidia.com for quotas.

Automatic Installation Fallback

If NVIDIA NIM is not yet configured in OpenCode, the tool:

• Shows installation instructions in your terminal
• Creates a prompt file in $HOME/prompt with the exact configuration
• Launches OpenCode, which will detect and display the prompt automatically

🦞 OpenClaw Integration

OpenClaw is an autonomous AI agent daemon. free-coding-models can configure it to use NVIDIA NIM models as its default provider — no download or local setup needed, everything runs via the NIM remote API.

Quick Start

free-coding-models --openclaw

Or run without flags and choose OpenClaw from the startup menu.

1. Wait for models to be pinged
2. Navigate with ↑↓ arrows to your preferred model
3. Press Enter — tool automatically:
   • Reads ~/.openclaw/openclaw.json
   • Adds the nvidia provider block (NIM base URL + your API key) if missing
   • Sets agents.defaults.model.primary to nvidia/<model-id>
   • Saves config and prints next steps

What gets written to OpenClaw config

{
  "models": {
    "providers": {
      "nvidia": {
        "baseUrl": "https://integrate.api.nvidia.com/v1",
        "api": "openai-completions"
      }
    }
  },
  "env": {
    "NVIDIA_API_KEY": "nvapi-xxxx-your-key"
  },
  "agents": {
    "defaults": {
      "model": {
        "primary": "nvidia/deepseek-ai/deepseek-v3.2"
      },
      "models": {
        "nvidia/deepseek-ai/deepseek-v3.2": {}
      }
    }
  }
}

⚠️ Note: providers must be nested under models.providers — not at the config root. A root-level providers key is ignored by OpenClaw.

⚠️ Note: The model must also be listed in agents.defaults.models (the allowlist). Without this entry, OpenClaw rejects the model with "not allowed" even if it is set as primary.

After updating OpenClaw config

OpenClaw's gateway auto-reloads config file changes (depending on gateway.reload.mode). To apply manually:

# Apply via CLI
openclaw models set nvidia/deepseek-ai/deepseek-v3.2

# Or re-run the interactive setup wizard
openclaw configure

⚠️ Note: openclaw restart does not exist as a CLI command. Kill and relaunch the process manually if you need a full restart.

💡 Why use remote NIM models with OpenClaw? NVIDIA NIM serves models via a fast API — no local GPU required, no VRAM limits, free credits for developers. You get frontier-class coding models (DeepSeek V3, Kimi K2, Qwen3 Coder) without downloading anything.

Patching OpenClaw for full NVIDIA model support

Problem: By default, OpenClaw only allows a few specific NVIDIA models in its allowlist. If you try to use a model that's not in the list, you'll get this error:

Model "nvidia/mistralai/devstral-2-123b-instruct-2512" is not allowed. Use /models to list providers, or /models <provider> to list models.

Solution: Patch OpenClaw's configuration to add ALL 47 NVIDIA models from free-coding-models to the allowlist:

# From the free-coding-models package directory
node patch-openclaw.js

This script:

• Backs up ~/.openclaw/agents/main/agent/models.json and ~/.openclaw/openclaw.json
• Adds all 47 NVIDIA models with proper context window and token limits
• Preserves existing models and configuration
• Prints a summary of what was added

After patching:

1. Restart OpenClaw gateway:

   systemctl --user restart openclaw-gateway

2. Verify models are available:

   free-coding-models --openclaw

3. Select any model — no more "not allowed" errors!

Why this is needed: OpenClaw uses a strict allowlist system to prevent typos and invalid models. The patch-openclaw.js script populates the allowlist with all known working NVIDIA models, so you can freely switch between them without manually editing config files.

⚙️ How it works

┌─────────────────────────────────────────────────────────────┐
│  1. Enter alternate screen buffer (like vim/htop/less)      │
│  2. Ping ALL models in parallel                             │
│  3. Display real-time table with Latest/Avg/Up% columns     │
│  4. Re-ping ALL models every 2 seconds (forever)           │
│  5. Update rolling averages from ALL successful pings      │
│  6. User can navigate with ↑↓ and select with Enter       │
│  7. On Enter (OpenCode): set model, launch OpenCode        │
│  8. On Enter (OpenClaw): update ~/.openclaw/openclaw.json  │
└─────────────────────────────────────────────────────────────┘

Result: Continuous monitoring interface that stays open until you select a model or press Ctrl+C. Rolling averages give you accurate long-term latency data, uptime percentage tracks reliability, and you can configure your tool of choice with your chosen model in one keystroke.

📋 API Reference

Configuration:

• Ping timeout: 15 seconds per attempt (slow models get more time)
• Ping interval: 2 seconds between complete re-pings of all models (adjustable with W/X keys)
• Monitor mode: Interface stays open forever, press Ctrl+C to exit

Flags:

Keyboard shortcuts:

• ↑↓ — Navigate models
• Enter — Select model (launches OpenCode or sets OpenClaw default, depending on mode)
• R/T/O/M/P/A/S/V/U — Sort by Rank/Tier/Origin/Model/Ping/Avg/Status/Verdict/Uptime
• W — Decrease ping interval (faster pings)
• X — Increase ping interval (slower pings)
• E — Elevate tier filter (show fewer, higher-tier models)
• D — Descend tier filter (show more, lower-tier models)
• Ctrl+C — Exit

🔧 Development

git clone https://github.com/vava-nessa/free-coding-models
cd free-coding-models
npm install
npm start -- YOUR_API_KEY

Releasing a new version

1. Make your changes and commit them with a descriptive message
2. Update CHANGELOG.md with the new version entry
3. Bump "version" in package.json (e.g. 0.1.3 → 0.1.4)
4. Commit with just the version number as the message:

git add .
git commit -m "0.1.4"
git push

The GitHub Actions workflow automatically publishes to npm on every push to main.

📄 License

MIT © vava

_{Built with ☕ and 🌹 by vava}

📬 Contribute

We welcome contributions! Feel free to open issues, submit pull requests, or get involved in the project.

Q: Can I use this with other providers? A: Yes, the tool is designed to be extensible; see the source for examples of customizing endpoints.

Q: How accurate are the latency numbers? A: They represent average round-trip times measured during testing; actual performance may vary based on network conditions.

Q: Do I need to download models locally for OpenClaw? A: No — free-coding-models configures OpenClaw to use NVIDIA NIM's remote API, so models run on NVIDIA's infrastructure. No GPU or local setup required.

📧 Support

For questions or issues, open a GitHub issue or join our community Discord: https://discord.gg/WKA3TwYVuZ