The first cost of adding AI to a small tool is often not tokens. It is the time spent comparing provider docs, sign-up rules, base URLs, and model names that may already be stale. We tested midudev/itsfree.ai, the open-source directory created by Miguel Ángel Durán (midudev), to see whether it could shorten that discovery work and lead to a result we could actually verify.
itsfree.ai is a directory, not a model proxy operated by us. At the time of the experiment, its rendered pages listed 25 providers with free allowances, 463 free models, and 9 local runtimes. Those numbers describe directory records; we did not test every listed model or runtime. The useful design choice is that provider data, requirements, base URLs, and gotchas are collected in structured TypeScript files and exposed through filters such as no-sign-up and OpenAI-compatible.
The repository built successfully, but the development server did not. Two pnpm dev attempts failed because Vite could not find a generated deps_ssr file, and clearing .vite reproduced the failure. Serving the completed build with pnpm preview returned HTTP 200 and rendered the pages we needed. The distinction matters: the current commit was usable for directory exploration in our environment, while the dev compatibility issue remained unresolved.
The API path showed why live verification matters. The documented gpt-oss example returned model_unavailable. Other model choices produced missing-key or invalid-key errors. After reading the llm7 provider gotchas and querying the live model list, we selected codestral-latest and sent the placeholder bearer value unused to the OpenAI-compatible endpoint at https://api.llm7.io/v1. That request returned HTTP 200.
We then asked the model to do useful, checkable work: extract six provider records and sort them by freeModels. A separate verifier derived truth from the source data and compared record count, fields, values, and order. The model output matched all six records (match=true). The call used 272 prompt tokens and 101 completion tokens, 373 total; curl measured 1.69 seconds, and the experiment recorded CNY 0 for the call.
That success is not a general accuracy claim. When the same model was asked what the project does, it incorrectly answered that the project verifies domain availability. The comparison gives us a practical boundary: a free model can be useful for structured transformations when expected output is machine-checkable, but fluent prose is not evidence of factual accuracy.
You can reproduce the decision process without moving an entire application. First, open the no-sign-up or OpenAI-compatible filter on itsfree.ai and pick one candidate. Second, read its gotchas, query the provider’s live model list, send the smallest representative request, and automatically check the fields or ordering you care about. Only then decide whether the endpoint is suitable for a larger prototype or whether a paid, more stable path is justified.
The result supports a narrow, useful conclusion: itsfree.ai reduced provider discovery to a structured shortlist, and one directory-guided route completed a real, machine-verified task at no recorded cost in this run. The durable asset is not the name of a model that happens to be free today. It is a repeatable sequence: filter, read gotchas, check the live list, test minimally, and verify.
Links
- Project: https://itsfree.ai
- Official repository: https://github.com/midudev/itsfree.ai
Images / captions
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01-home.png— locally rendered homepage. -
04-no-signup.png— real no-sign-up filter result. -
02-provider-llm7.png— real llm7 provider page. - Comparison card generated from
task-request.json,task-response.json, andtask-verification.json; label it as generated from experiment files.
Upload order
- Title and homepage image. 2. Body with the screenshots at their matching sections. 3. Verification card after the task result. 4. Repository and project links. 5. Preview captions and links before publishing.
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