LLMTest

by tjacquesson

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About

LLM proxy that benchmarks AI models on real prompts and finds cheaper, faster alternatives across 340+ models.

Details

Author
tjacquesson
Categories
Developer Tools, AI

Setup

Install LLMTest in your MCP client (Claude Desktop, Cursor, Windsurf, and others).

Repository: https://github.com/tjacquesson/llmtest-mcp

Follow the installation instructions in the repository README, then restart your MCP client.

MCP server that benchmarks AI models on your actual prompts and finds cheaper, faster alternatives. Works with Claude Code, Cursor, Windsurf, and any MCP-compatible tool.

Sign up atllmtest.ioand grab your API key from the dashboard.

claude mcp add llmtest -- npx llmtest-mcp
export LLMTEST_API_KEY=llmt_your_key_here
{ "mcpServers": { "llmtest": { "command": "npx", "args": ["llmtest-mcp"], "env": { "LLMTEST_API_KEY": "llmt_your_key_here" } } } }

- "Check my LLMTest status"
- "Find cheaper models for my AI calls"
- "Run a benchmark on my blog-writer flow"
- "What models are trending?"

LLMTest is a proxy that sits between your app and AI providers. Point your app athttps://llmtest.io/v1instead of calling OpenAI/Anthropic directly, and LLMTest tracks your usage, benchmarks alternatives, and suggests cost savings.

This MCP server gives your AI assistant access to LLMTest's tools so it can manage everything for you.

Autopilot automatically optimizes your flows on a weekly cadence. Changes that pass every safety gate go live with a 24-hour revert window. Drift detection keeps checking after that and rolls back if quality slips.

To enable from your IDE:ask your AI assistant something like "enable LLMTest autopilot". It will callenable_autopilot. Useget_autopilot_statusto confirm prerequisites.

Prerequisites(checked per flow each cycle):

- Autopilot enabled on the account
- Email verified
- Account age ≥ 14 days (trust ramp)
- Flow has ≥ 20 real calls in the last 7 days
- Flow not optimized by autopilot in the last 14 days (cooldown)
- Positive credit balance (~$1–2 per run)

Safety gates(all must pass for auto-accept): 95% CI lower bound > 50% win rate, multi-judge agreement ≥ 80%, ≥ 20% total savings, no length-bias warning, golden-set regression check.

Revert:24h window after auto-accept. After that, only drift detection can roll back.
- Tell your AI: "I'm building a support chatbot using gpt-4o"
- It seeds realistic test samples withseed_samples
- It runsrun_benchmarkto compare models
- It shows youget_suggestionswith cheaper alternatives
- Route your AI calls throughhttps://llmtest.io/v1
- LLMTest monitors usage and auto-benchmarks when flows hit 50+ calls
- Ask "any cost-saving suggestions?" to see recommendations
- Accept a suggestion and update your code

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