Large language models (LLMs) are central to AI development today, yet the cost of inference quickly adds up when you’re experimenting or prototyping. The mnfst/awesome-free-llm-apis repository offers a practical catalog of permanently free-tier LLM APIs that are compatible with the OpenAI SDK. It’s not a code library, but rather a structured reference that helps developers navigate the fragmented landscape of free LLM inference options without incurring costs.
What the awesome-free-llm-apis catalog provides
This repository is essentially a curated list of permanent free-tier LLM APIs from various providers. It includes both original model providers and third-party inference platforms. The goal is to document the available free usage limits, model characteristics, and API details so developers can build or prototype AI agents without hitting cost barriers early on.
Under the hood, the catalog covers providers like Cohere, Google Gemini, Mistral, and Z AI — each offering their own models with distinct context window sizes, token limits, and supported modalities. It also lists third-party inference platforms such as Cerebras, Cloudflare Workers AI, GitHub Models, and Groq that serve multiple models with varying capabilities.
Each entry in the catalog includes base URLs for API calls, tables describing the models available, context windows for prompt length, maximum output tokens, supported input types (text, audio, images), and the rate limits for calls per minute or day. This structured data makes it easier to compare offerings side by side.
The stack behind this repo is JavaScript-based documentation, focusing on clear, concise information rather than running code. It’s a resource for developers who want to build a multi-provider LLM router, fallback system, or simply explore free-tier APIs suitable for their AI experiments.
Why this catalog stands out and its tradeoffs
The distinguishing feature of this repo is its focus on permanent free tiers that are OpenAI SDK-compatible. This compatibility angle means you can design your code to switch between providers with minimal changes, making it easier to build zero-cost failover or combined usage strategies.
The repo’s strength lies in the comprehensive and up-to-date rate limit data it provides. For example, Cohere offers 1,000 API calls per month with a 20 requests per minute (RPM) limit, while Google Gemini 2.5 Flash has 10 RPM and 250 requests per day (RPD). Cerebras boasts ultra-fast inference at about 2,600 tokens per second with a daily cap of 1 million tokens and a 30 RPM limit. Cloudflare Workers AI provides 10,000 neurons per day and access to over 50 models.
Such concrete metrics are invaluable when you want to architect an LLM router that dynamically falls back between providers based on rate limits or model suitability. You can mix and match providers for cost-efficiency and coverage.
That said, the tradeoff is clear: this repo is purely a reference list. It doesn’t provide SDKs, client libraries, or code samples for how to integrate these providers — you’ll need to build your own tooling around the data here.
The documentation quality is consistent and factual but doesn’t extend into usage patterns or error handling strategies. That means the repo is a starting point, not a finished solution.
Explore the project
Since the repo is a documentation resource, the best way to engage with it is to explore the README and the structured tables it contains. The README organizes providers into categories and gives detailed rate limits and model characteristics.
Start by reviewing the model tables for providers you’re interested in. Notice the context window sizes — key for prompt engineering — and max output token limits, which affect how much response you can generate.
Look at the rate limiting sections to understand how many requests you can make per minute or per day. These figures will guide your design for fallback logic or multi-provider orchestration.
The base URLs listed will be your starting points when configuring HTTP clients or adapting OpenAI SDK calls to these alternative providers.
Here’s an example snippet of how the repo documents one provider’s rate limits:
Cohere:
- 1,000 API calls/month
- 20 RPM (requests per minute)
Cerebras:
- Ultra-fast inference (~2,600 tokens/second)
- 1 million tokens/day cap
- 30 RPM
- 14,400 RPD (requests per day)
Although the repo doesn’t provide client code, this information underpins any serious attempt to build a multi-provider LLM router that respects limits and balances load.
Verdict
The awesome-free-llm-apis repository is a solid, practical resource for developers building AI agents or prototypes where inference cost is a barrier. The detailed, structured catalog of free-tier providers with concrete rate limits and model specs allows for informed decisions when architecting zero-cost or fallback-enabled LLM usage.
It’s not a plug-and-play library, so you’ll need to invest in building integration layers and handling provider-specific quirks yourself. But if you’re managing multiple free-tier APIs or want to experiment broadly without incurring charges, this repo is a must-bookmark reference.
For anyone working on multi-provider LLM routing, fallback strategies, or cost-conscious AI prototyping, this catalog is worth understanding and incorporating into your tooling.
The tradeoff is that it requires effort to translate this data into production-ready code. However, that’s the nature of the space right now — free-tier LLM offerings are fragmented, and this repo helps you navigate that complexity with clarity.
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