RRepoGEO

REPOGEO REPORT · LITE

vava-nessa/free-coding-models

Default branch main · commit 1e0b0969 · scanned 5/29/2026, 5:36:27 AM

GitHub: 1,852 stars · 210 forks

AI VISIBILITY SCORE
27 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface vava-nessa/free-coding-models, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition README H1 and opening paragraph to clarify tool type and purpose

    Why:

    CURRENT
    <h1 align="center">free-coding-models</h1>

<p align="center">
  <strong>Find the fastest free coding model in seconds</strong><br>
  Track ~170 models across ~15 trusted free or free-limited AI providers in real time<br><br>
  <strong>Install Free API endpoints to your favorite AI coding tools:</strong><br>
  OpenCode CLI / Desktop / WebUI, OpenClaw, Crush, Goose, Aider, Kilo CLI, Qwen Code, OpenHands, Amp, Hermes, Continue, Cline, Xcode, Pi, Rovo, Gemini and more...<br><br>
  <strong>Use Kimi K2, DeepSeek V3, GPT-OSS, Qwen3, MiniMax M2, GLM, Llama 4, Gemma 4, Devstral and more — for free</strong>
</p>
    COPY-PASTE FIX
    <h1 align="center">free-coding-models: The CLI for Finding, Benchmarking, and Installing Free Coding LLMs</h1>

<p align="center">
  <strong>A powerful command-line interface (CLI) to discover, benchmark, and install over 170 free coding LLM models from 15+ providers in real time.</strong><br>
  Quickly find the fastest free coding model and integrate its API endpoints into your favorite AI coding tools: OpenCode CLI, OpenClaw, Crush, Goose, Aider, Kilo CLI, Continue, and many more. Use models like Kimi K2, DeepSeek V3, GPT-OSS, Qwen3, MiniMax M2, GLM, Llama 4, Gemma 4, and Devstral — all for free.
</p>
  • mediumtopics#2
    Add specific topics for CLI, benchmarking, and model management

    Why:

    CURRENT
    ai, deepseek, free, free-ai, freeai, gpt, gptoss, kimi, nim, nvidia, nvidia-nim, nvidia-nim-api, nvidia-nims, openclaw, opencode
    COPY-PASTE FIX
    ai, deepseek, free, free-ai, freeai, gpt, gptoss, kimi, nim, nvidia, nvidia-nim, nvidia-nim-api, nvidia-nims, openclaw, opencode, cli-tool, llm-benchmarking, model-management, coding-assistant-integration, free-llms
  • lowlicense#3
    Clarify the project's license(s) in the README

    Why:

    COPY-PASTE FIX
    Add a clear statement in the '⚖️ Licensing' section of the README, specifying the exact license(s) that apply to the project, e.g., 'This project is licensed under [License Name 1] and [License Name 2]. See the LICENSE file for full details.'

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface vava-nessa/free-coding-models
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Hub
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Hub · recommended 1×
  2. Code Llama · recommended 1×
  3. StarCoder/StarCoder2 · recommended 1×
  4. DeepSeek Coder · recommended 1×
  5. Phi-2 · recommended 1×
  • CATEGORY QUERY
    How can I discover and benchmark various free large language models for coding assistance?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Hub
    2. Code Llama
    3. StarCoder/StarCoder2
    4. DeepSeek Coder
    5. Phi-2
    6. Mistral 7B / Mixtral 8x7B
    7. Open LLM Leaderboard
    8. Papers With Code
    9. GitHub
    10. LM Sys Chatbot Arena / AlpacaEval
    11. HumanEval
    12. MBPP (Mostly Basic Python Problems)
    13. evaluate
    14. llama.cpp

    AI recommended 14 alternatives but never named vava-nessa/free-coding-models. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help integrate free AI coding models into my development environment or CLI?
    you: not recommended
    AI recommended (in order):
    1. GitHub Copilot
    2. Tabnine
    3. Codeium
    4. Continue
    5. Ollama
    6. CodeGPT
    7. JetBrains AI Assistant
    8. FauxPilot

    AI recommended 8 alternatives but never named vava-nessa/free-coding-models. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of vava-nessa/free-coding-models?
    pass
    AI did not name vava-nessa/free-coding-models — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts vava-nessa/free-coding-models in production, what risks or prerequisites should they evaluate first?
    pass
    AI named vava-nessa/free-coding-models explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo vava-nessa/free-coding-models solve, and who is the primary audience?
    pass
    AI did not name vava-nessa/free-coding-models — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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vava-nessa/free-coding-models — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite