LLMTest

LLMTest is a prompt and model optimization platform built by PixelGrid that sits between your application and LLM providers. It routes real traffic through a proxy layer, benchmarks outputs across 340+ models, and automatically applies prompt rewrites and model swaps that clear a multi-gate safety check. The tool targets developers who are already shipping AI features and want to reduce cost and latency without manually tuning prompts or tracking new model releases.

What It Is

LLMTest is an LLM optimization proxy and benchmarking service. Developers integrate it via an OpenAI-compatible API endpoint, and it handles model routing, fallback logic, cost tracking, and prompt optimization in the background. It covers two phases: a build phase for benchmarking models before launch, and a scale phase (called Autopilot) for continuous weekly optimization on live traffic.

How Autopilot Works

Autopilot is LLMTest's flagship automated optimization mode. Once enabled, it runs weekly background jobs that test shorter or cheaper prompt variants and alternative models against real traffic. A change only ships if it clears five safety gates:

• 95% confidence win rate using a Wilson lower bound
• Two independent AI judges (Claude Sonnet and GPT-4o, position-swapped) must agree ≥ 80%
• At least 20% cost savings — smaller wins are skipped
• Golden set regression check — 5 known-good inputs must not regress
• No length bias — variants 50% longer than baseline require human sign-off

Autopilot only activates on accounts 14+ days old with flows that have 20+ real calls, and enforces a 14-day cooldown per flow. Every auto-applied change includes a 24-hour revert link delivered via a Monday-morning email diff.

Core Capabilities

Beyond Autopilot, LLMTest provides several production-focused features:

• Automatic fallbacks — when a model returns a 529 or fails to produce valid JSON, traffic routes to the next best model within the same request
• Drift detection — weekly checks catch quality regressions caused by model updates or traffic shifts, triggering automatic rollbacks
• Cost tracking per flow — per-model, per-flow, per-day cost visibility
• Model radar — daily checks for new model releases and price drops, with automatic benchmarking
• MCP integration — suggestions surface directly in Claude Code, Cursor, Windsurf, Cline, Roo Code, and other MCP-compatible IDEs; accepting a suggestion edits the code in place
• Smart benchmarks — AI-generated test prompts scored by an AI judge across 340+ models

Compatibility and Integrations

LLMTest works with any OpenAI-compatible application. The homepage lists explicit compatibility with Claude Code, Cursor, Windsurf, OpenAI Codex, Cline, Roo Code, GitHub Copilot, Bolt, Lovable, v0, and Replit. The MCP server integration means developers can receive and accept optimization suggestions without leaving their IDE.

Why It Matters

The platform's real-world example on the homepage illustrates the value proposition: a 7-step SEO blog post pipeline running entirely on Claude Opus is shown dropping from $1.15 per post to $0.46 per post (60% cheaper) and from 79 seconds to 46 seconds (42% faster) after LLMTest reassigns cheaper models to lower-complexity steps while keeping the expensive model only where quality requires it. The AI judge scores each step to verify quality is maintained. This per-step model routing is the core differentiator versus simply switching to a cheaper model globally.