LLMTest is a proxy for LLM APIs that tracks cost, benchmarks models against each other, and automatically rewrites prompts to be shorter and cheaper while preserving quality.

Does LLMTest work with OpenAI and Anthropic?

Yes. LLMTest exposes an OpenAI-compatible endpoint at https://llmtest.io/v1 and routes to 340+ models across OpenAI, Anthropic, Google, Meta, Mistral, DeepSeek, Groq and more.

How much does it cost?

LLMTest takes a small margin on proxied LLM calls. There is no subscription. Autopilot optimizations cost about $1–2 per run and only execute on flows with enough real traffic.

Can I use my own API keys?

Yes. Bring your own OpenAI or Anthropic key, or use LLMTest credits to route across every supported model through a single API key.

LLMTest — Cut LLM costs without breaking your prompts

How it works

You build it. We make it production-grade.

1

Build your AI feature

Write a quick prompt. Pick any model. Ship it through LLMTest. Don't overthink it.

2

We watch and learn

LLMTest sees your real traffic. It learns what your feature does, how it's used, and where it fails.

3

We optimize automatically

Better prompts. Cheaper models. Automatic failover. One click to accept — or let it run on autopilot.

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 Smart benchmarks across 340+ models

Ship with the best model from day one

No real traffic needed
AI judge scores every output
Smart selection picks the most relevant challengers

Scale phase

Already live with real users? Autopilot keeps tuning your flows every week while you focus on the next feature.

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
New models detected and tested daily

NEW

LLMTest Autopilot

Optimization that runs itself.

Autopilot rewrites your prompts and finds better or cheaper models every week on your real traffic. Safe wins go live. One click reverts any of them.

1

Toggle it on

One switch on the dashboard, or ask your IDE agent. Kicks in once an account is 14+ days old and a flow has 20+ real calls.

2

We watch your flows

Weekly runs test shorter and cheaper variants against your real traffic.

3

Only safe wins ship

Two independent judges. 95% confidence. Regression checks on a golden set.

Monday-morning diff

Email with what changed, what you saved, and a 24h revert link.

Safe by default. Every change clears 5 gates.

95% confidence win rate. Wilson lower bound has to clear 50% (or 4 wins and zero losses).
Two independent judges mostly agree. Claude Sonnet and GPT-4o, position-swapped. Agreement ≥ 80%.
At least 20% savings. Smaller wins aren't worth the churn.
Golden set passes. 5 known-good inputs must not regress.
No length bias. Variants 50% longer than baseline need a human to sign off.

Plus: autopilot only runs on accounts 14+ days old, and won't re-optimize the same flow inside a 14-day cooldown.

What you get

Built for anyone shipping AI features

NEW

Prompt optimization

Shorten, clarify, or restructure any prompt automatically. Four strategies run in parallel. The winner has to beat the baseline at 95% confidence or it doesn't ship.

NEW

Autopilot

Weekly background runs rewrite your prompts and test better or cheaper models on your real traffic. Only changes that clear 5 safety gates go live. One click reverts any of them.

Automatic fallbacks

When a model is down or rate-limited, traffic routes to the next best model on its own. Your users don't notice the seam.

Drift detection

We keep checking optimizations weekly. If quality slips because a model changed or your traffic shifted, we roll back and tell you why.

Cost tracking per flow

See what each AI feature actually costs. Per model, per flow, per day. No more end-of-month surprises.

MCP integration

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

Model radar

We check for new models and price drops every day. Your flows get benchmarked against them before most people have even heard about the release.

AI quality judge

Every model switch gets scored by an AI judge against your actual prompts. You never trade quality for cost without seeing the tradeoff first.

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. Sometimes a model returns something that won't parse. Without LLMTest, your app crashes. With LLMTest, it retries on a different model inside the same request.

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, 5xx errors. Every AI API has bad days. LLMTest catches failures and routes to the next best model in the same request. Your users don't see the seam.

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