awesome-free-llm-apis is a catalogue of large-language-model APIs that offer permanent free tiers for text inference, meaning they accept text requests and return model-generated responses. It is for developers who need API providers and keys for applications or AI agents.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add mnfst/awesome-free-llm-apis --skill free-llm-apisgit clone --depth 1 https://github.com/mnfst/awesome-free-llm-apisWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mnfst/awesome-free-llm-apis/free-llm-apis)<a href="https://agentmods.dev/skills/mnfst/awesome-free-llm-apis/free-llm-apis"><img src="https://agentmods.dev/badge/skills/mnfst/awesome-free-llm-apis/free-llm-apis/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mnfst/awesome-free-llm-apis/free-llm-apis"><img src="https://agentmods.dev/badge/skills/mnfst/awesome-free-llm-apis/free-llm-apis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00116 | $0.00746 |
| Opus 5 | $0.00058 | $0.00373 |
| Sonnet 5 | $0.00023 | $0.00149 |
| Haiku 4.5 | $0.00012 | $0.00075 |
Grade A, and why
free-llm-apis scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
4. Offer a quick test script or curl command so they can confirm the key works. How it starts
The opening of the file, as written. The whole thing — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Free LLM API Setup
Help users pick a free LLM provider and configure their API key. Every provider here has a permanent free tier, no credit card needed.
Provider Selection
Ask the user what matters most, then recommend accordingly:
| Priority | Best picks |
|---|---|
| Highest rate limits | Groq (30 RPM, 14.4K RPD), Cerebras (30 RPM, 14.4K RPD) |
| Largest model selection | Cloudflare Workers AI (49+ models), OpenRouter (32+ models) |
| Strongest proprietary models | Google Gemini (Gemini 2.5 Pro), GitHub Models (GPT-4o) |
| Fastest inference | Groq, Cerebras (both optimized for speed) |
| Highest token budget | Mistral AI (1B tokens/month) |
| European provider | Mistral AI (EU), LLM7.io (UK) |
| No signup required | LLM7.io (basic tier works without token) |
Provider categories
Provider APIs -- run by the companies that train the models:
- Google Gemini, Cohere, Mistral AI, Zhipu AI
- See references/provider-apis.md for setup instructions.
Inference providers -- third-party platforms hosting open-weight models:
- GitHub Models, NVIDIA NIM, Groq, Cerebras, Cloudflare Workers AI, LLM7.io, Kluster AI, OpenRouter, Hugging Face
- See references/inference-providers.md for setup instructions.
Workflow
- Ask what models, rate limits, or features the user cares about. If they already know which provider they want, skip to step 3.
- Match their priorities against the table above. Suggest 1-2 options with a short reason.
- Load the right reference file and walk through the setup steps for that provider (API key, code example, env var).
- Offer a quick test script or curl command so they can confirm the key works.
Quick Test Template
After setup, use this to verify any provider:
from openai import OpenAI
client = OpenAI(api_key="KEY", base_url="BASE_URL")
response = client.chat.completions.create(
model="MODEL_NAME",
messages=[{"role": "user", "content": "Say hello in one sentence."}],
max_tokens=50
)
print(response.choices[0].message.content)
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 67 lines · 116 tokens per session scan A 953a86065e6f
free-llm-apis is a skill published in the GitHub repository mnfst/awesome-free-llm-apis (7,524 stars, last pushed 22d ago), licensed CC0-1.0. It adds 116 tokens to every session and 746 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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outlines
Outlines: structured JSON/regex/Pydantic LLM generation.