What is LLMTest?

LLMTest is an optimization engine for AI-powered features that automatically improves prompts and selects better models—faster, 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.

Who it's for

• Developers shipping AI features who want to stop guessing which model to pick and instead get data-driven benchmarks across 340+ models before going live.
• Vibe coders using Claude or Codex who need automatic fallbacks when providers fail—API overloads, rate limits, or broken JSON formats—without writing custom error-handling code.
• Product teams scaling AI features who want weekly optimization of prompts and models on real traffic, with safety gates that prevent regressions and a one-click revert for any change.

Key features

Autopilot optimization

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 gates—including 95% confidence win rate, two independent judges agreeing, and at least 20% savings—go live automatically.

Prompt optimization

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.

Automatic fallbacks

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.

Drift detection

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.

What stands out

"Safe wins go live. One click reverts any of them."

LLMTest doesn't just optimize blindly—it 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.

Worth checking out if…

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 guarantees—no more end-of-month surprises on cost or quality.