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LLMTest

from tjacquesson

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

๐Ÿ”ฅ๐Ÿ”ฅ๐Ÿ”ฅโœ“ VerifiedAccount requiredNeeds API keys

LLMTest MCP Server

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.

How It Works

LLMTest is a proxy that sits between your app and AI providers. Point your app at https://llmtest.io/v1 instead 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.

Available Tools

ToolDescription
statusShow proxy status and activity summary
list_flowsList all AI flows with cost and latency stats
get_suggestionsGet pending model-switch recommendations
update_suggestionAccept or dismiss a suggestion
run_benchmarkBenchmark a flow against challenger models
optimize_promptRewrite a flow's prompt and find a cheaper model that still works
seed_samplesAdd test prompts for pre-launch benchmarking
list_samplesShow stored test samples per flow
list_new_modelsShow new and trending models
get_accountCheck credit balance and usage
get_autopilot_statusCheck whether autopilot is on and whether the account is eligible
enable_autopilotTurn on weekly auto-optimization with safety gates + drift-based auto-revert
disable_autopilotTurn off autopilot (existing optimizations stay active)
list_active_optimizationsList auto-accepted optimizations still inside their 24h revert window
revert_optimizationRoll an auto-accepted optimization back to the previous prompt

Autopilot

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 call enable_autopilot. Use get_autopilot_status to confirm prerequisites.

Prerequisites (checked per flow each cycle):

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

Safety gates (all must pass for auto-accept): 95% CI lower bound > 50% win rate, multi-judge agreement โ‰ฅ 80%, โ‰ฅ 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.

Typical Workflow

Pre-launch (no traffic yet):

  1. Tell your AI: "I'm building a support chatbot using gpt-4o"
  2. It seeds realistic test samples with seed_samples
  3. It runs run_benchmark to compare models
  4. It shows you get_suggestions with cheaper alternatives

Post-launch (with real traffic):

  1. Route your AI calls through https://llmtest.io/v1
  2. LLMTest monitors usage and auto-benchmarks when flows hit 50+ calls
  3. Ask "any cost-saving suggestions?" to see recommendations
  4. Accept a suggestion and update your code

Environment Variables

VariableRequiredDescription
LLMTEST_API_KEYYesYour API key from llmtest.io/dashboard
LLMTEST_BASE_URLNoCustom API URL (defaults to https://llmtest.io)