LLMTest — Stop overpaying for AI

How it works

Three steps, no coding needed

1

Create an account

Sign up, get your API key, add some credits. Takes 30 seconds.

2

Add to your coding tool

One line in your MCP config. Works with Claude Code, Cursor, Windsurf, and more.

3

Ask your AI assistant

"Find cheaper models for my AI calls." It reads your code, runs benchmarks, and makes the changes for you.

Two modes, one tool

Whether you're building or scaling

Build phase

Choosing models for the first time? Don't guess. Benchmark before you ship.

1 Describe your AI feature

2 AI generates test prompts

3 Benchmarks run on 10+ models

Ship with the best model from day one

No real traffic needed
AI judge scores every output
Compare cost, speed, and quality side by side

Scale phase

Already live with real users? Keep optimizing automatically as models evolve.

Live traffic monitored

New model detected: gemini-2.5-pro

Benchmark triggered automatically

Suggestion: 40% cheaper, same quality

API 529 → fallback to gpt-4.1 (auto)

Weekly benchmarks on real prompts
Automatic fallbacks when APIs go down
Price drop and new model alerts

What you get

Built for anyone shipping AI features

Automatic fallbacks

When a model is down or rate-limited, traffic automatically routes to the next best model. No downtime.

Cost tracking per flow

See exactly how much each AI feature costs. Per model, per flow, per day. No more surprise bills.

MCP integration

Get suggestions directly in Claude Code, Cursor, or any MCP-compatible tool. Accept and it edits your code.

Model radar

New models and price drops detected daily. You get benchmarked against the latest before anyone else notices.

AI quality judge

Every model switch is validated by an AI judge that scores output quality on your actual prompts. You never trade quality for cost blindly.

Real-world examples

See it in action

SEO Blog Post Generator

A 7-step pipeline that researches, writes, and formats blog posts. Most people run every step on the same expensive model. LLMTest finds where you can use cheaper ones.

Step	Task	Model	Time	Cost
1	Analyze customer website	claude-opus-4-6	8s	$0.12
2	Keyword research	claude-opus-4-6	12s	$0.18
3	Analyze ranking content	claude-opus-4-6	15s	$0.22
4	Create post structure	claude-opus-4-6	6s	$0.09
5	Write post content	claude-opus-4-6	25s	$0.35
6	Humanize content	claude-opus-4-6	10s	$0.14
7	Format in markdown	claude-opus-4-6	3s	$0.05

79stotal time

$1.15per post

1 modelfor everything

Step	Task	Model	Time	Cost	Judge
1	Analyze customer website	gemini-2.5-flash	3s	$0.01	4.8/5
2	Keyword research	gpt-4.1-mini	4s	$0.02	4.6/5
3	Analyze ranking content	gemini-2.5-flash	5s	$0.02	4.7/5
4	Create post structure	gpt-4.1-mini	2s	$0.01	4.9/5
5	Write post content	claude-opus-4-6	25s	$0.35	baseline
6	Humanize content	claude-sonnet-4-6	6s	$0.05	4.5/5
7	Format in markdown	gemini-2.5-flash	1s	$0.003	5.0/5

Auto-recovery from bad JSON

Your app needs structured JSON output. Sometimes a model returns broken formatting. Without LLMTest, your app crashes. With LLMTest, it retries on a different model automatically.

14:23:01 POST /v1/chat/completions flow=product-tagger model=gpt-4.1-mini 14:23:03 200 OK - response received 14:23:03 ERROR JSON.parse failed: Unexpected token 'H' at position 0 14:23:03 CRASH Unhandled exception in product-tagger pipeline 14:23:03 DOWN Feature offline until manual restart

Seamless failover when APIs go down

Rate limits, outages, server errors. Every AI API has them. LLMTest detects failures and instantly routes to the next best model. Your users never notice.

09:14:22 POST /v1/chat/completions flow=support-bot model=claude-sonnet-4-6 09:14:30 529 OVERLOADED - Anthropic API at capacity 09:14:30 ERROR "Sorry, something went wrong. Please try again later." 09:14:30 LOST Customer leaves support chat