LLMTest

LLMTest exposes a single API at https://llmtest.io/v1 that speaks the OpenAI format. That means the official openai package for JavaScript and Python works after you change the base URL and key. You can call gpt-4o, anthropic/claude-sonnet-4, google/gemini-2.5-flash, meta-llama/llama-4-scout, or any other supported model through the same code path. Think of it as an LLM gateway that hides the provider sprawl. For a team already using an OpenAI SDK, this is the smallest possible migration.

Fallbacks and JSON repair work the moment you route through the proxy, with no feature flags or extra code. If a model returns a 429 or a 5xx, LLMTest retries your request on another model and your app never sees the failure. If you ask for JSON output and the model returns something malformed, the proxy fixes it or retries. You only see the standard response shape, so your parsing logic stays unchanged. If you want to log when a swap happened, the x-llmtest-fallback-model response header tells you which model actually served the call. No guessing. It just tells you.

Every call is logged with token counts, latency, and cost. You tag calls with an optional X-Flow header, and LLMTest groups everything by that name. So "support chatbot" and "product descriptions" show up as separate line items instead of one blurry total. That's the useful part. This is what makes it possible to argue about AI cost with numbers instead of vibes.

If you haven't picked a model yet, LLMTest's benchmark mode fills the gap. You describe the AI feature, the system generates test prompts, and it runs smart benchmarks across 340+ models. An AI judge scores every output, and the selection logic picks the most relevant challengers rather than testing all 340 blindly. No real traffic needed. You can compare models before launch.

Autopilot rewrites your prompts and hunts for cheaper or better models on your real traffic, running weekly in the background. It tests shorter and cheaper variants, and only safe wins go live. You get a Monday-morning email with what changed, what you saved, and a 24-hour revert link. One click undoes any change.

Autopilot changes clear five checks before shipping, which is the part that matters if you care about not breaking production. A win needs a 95% confidence win rate, two independent judges (Claude Sonnet and GPT-4o, position-swapped) agreeing at 80%+, at least 20% savings, a golden set of five known-good inputs passing, and no length bias (variants 50% longer than baseline need human sign-off). It also skips accounts younger than 14 days and enforces a 14-day cooldown per flow, so the same feature never gets retuned twice inside that window. That's a lot of gates.

LLMTest also runs as an MCP server inside your IDE, alongside the production proxy. Most teams use the proxy in production for reliability and cost tracking, and the MCP server during development for benchmarking and model selection. Both share the same API key and the same data, so what you test during development lines up with what runs live.