Building appsIntermediate · about 30 minFree · no signup

Evaluate free-tier AI routing with FreeLLMAPI

Explore the repository, check which models you can actually use and plan a small routing experiment.

Video cover: One setup, multiple free-tier AI providers

FreeLLMAPI combines configured providers behind a local API. It is a way to experiment with available free tiers, not a guarantee of free access to every named AI model.

Open the repository and its setup instructions: github.com/tashfeenahmed/freellmapi
The README describes routing requests across providers you configure, with fallback when requests encounter rate limits or server errors. You still need eligible provider accounts and keys.
The project explicitly lists no frontier models, variable latency and no service-level guarantee among its limitations. Compatibility with Claude Code or an OpenAI-style API does not mean free Claude or GPT frontier-model access.
Token totals are catalogue claims, not a personal allowance. Check each provider’s current terms and quotas. The project positions this for personal experimentation; its optional paid live catalogue is separate from provider usage.
Use the prompt below to compare a few suitable providers and design a small test before installing or relying on the router.

Context & limitations

Choose by task quality and dependable availability. A larger advertised token pool does not make different models interchangeable.

Do this yourself

Create a practical evaluation plan for FreeLLMAPI: https://github.com/tashfeenahmed/freellmapi. My use case is ___, my operating system is ___, my required capabilities are ___, and my spending limit is ___.

CHECK THE CURRENT PROJECT

Read the current README and relevant setup and routing documentation. Record the date and revision you reviewed. If you cannot access them, ask me for the files. Separate documented behaviour, promotional claims and things you have actually tested. Do not install or run anything yet.

COMPARE SUITABLE PROVIDERS

Recommend at most three providers for my task. Verify model IDs, region/account eligibility, current free quotas, rate limits, billing requirements and data-use terms using each provider's own documentation. Give source links and mark unknowns. Do not equate a compatible API or coding client with access to the model whose name it uses. Do not promise an aggregate free-token total without checking whether quotas are shared or conditional.

DESIGN A SMALL EXPERIMENT

Write a setup checklist for my operating system with credential placeholders only. Keep keys out of chat, committed files and frontend code. Propose a fallback order based on my task's capabilities and a clear stop condition when no eligible provider remains; require my approval before any paid fallback. Respect each provider's limits and terms.

MEASURE THE RESULT

Create five representative test inputs with expected outputs or a scoring rubric. Include a controlled fallback test that does not exhaust real quotas. Record the actual provider/model, quality, latency, errors and token usage where available. Do not invent results or send private data during the experiment.

DELIVER A DECISION

Return a comparison table, the setup checklist and a go/no-go recommendation for this use case. Explain the main trade-offs and which claims still need checking. Wait for my approval before installing software, changing credentials or making API calls.

Checking eligibility and testing a real task gives a more useful budget estimate than adding advertised token quotas.

Watch on Instagram Source

Want to build this with a teacher?

Pathwise Academy runs small, hands-on AI classes in Singapore for ages 10–17 and adults.

Young Builders Adults