"OpenRouter + Intelligence" LLMTest helps devs and vibe coders automatically: â Pick better models for AI-powered features (faster, cheaper, better, sometimes all 3 combined) âŞď¸ Automatically add fallbacks when LLM providers fail (API is overloaded or JSON format isn't respected) All through one single API and MCP functions so you can just tell Claude or Codex to optimize everything.
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LLMTest is an optimization engine for AI-powered features that automatically improves prompts and selects better modelsâfaster, cheaper, and more reliably. It works as a single API and MCP integration, meaning you can ship a rough prompt and any model, then let LLMTest watch real traffic, learn how your feature behaves, and optimize everything automatically. The tool runs in two modes: a Build phase for benchmarking before you launch, and a Scale phase (Autopilot) that keeps tuning live flows every week while you focus on building the next feature.
Toggle it on once your account is 14+ days old and a flow has 20+ real calls. LLMTest runs weekly benchmarks on your live traffic, testing shorter and cheaper prompt variants against your baseline. Only changes that clear five safety gatesâincluding 95% confidence win rate, two independent judges agreeing, and at least 20% savingsâgo live automatically.
Four parallel strategies shorten, clarify, or restructure any prompt. The winning variant must beat the baseline at 95% confidence or it never ships. This runs both during the Build phase (on synthetic test prompts) and continuously in Autopilot mode.
When a model is down or rate-limited, traffic routes seamlessly to the next best model. Your users never notice the switch. This works out of the box with the LLMTest API, so you don't need to build custom failover logic.
Every optimized flow is checked weekly. If quality slips because a model changed or your traffic shifted, LLMTest rolls back the change automatically and tells you why. This keeps your AI features stable even as underlying models evolve.
"Safe wins go live. One click reverts any of them."
LLMTest doesn't just optimize blindlyâit enforces a rigorous safety net before any change ships. Every optimization must pass five gates: 95% confidence win rate, agreement between two independent judges (Claude Sonnet and GPT-4o), at least 20% savings, a golden set of known-good inputs that must not regress, and a length bias check. If any gate fails, the change becomes a pending suggestion instead of going live. Every auto-applied change has a 24-hour revert button, and drift detection continues monitoring afterward. This means you get continuous improvement without the risk of breaking your product.
You're shipping AI features and want to stop manually testing models, writing fallback logic, or worrying about prompt quality degrading over time. LLMTest is especially useful if you're using an IDE agent like Claude Code or Cursor and want to tell it to "optimize everything" through MCP functions. It's also a strong fit if you're scaling a live AI product and need weekly optimization with safety guaranteesâno more end-of-month surprises on cost or quality.
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