Compare outputs, quality, latency, and cost on your real prompts. Start with ModelGrade included access, or use your own provider keys when you want full-scale runs.
Free included tier
100
checks per day
Decision layers
4
quality, cost, reliability, routing
Average savings story
40-70%
after task-level routing
Prompt Lab
Prompt
Summarize this customer complaint into a support handoff note and recommend the next action.
GPT-4.1 Mini
Fastest strong answer
Gemini 2.5 Flash
Best cost/quality balance
Claude Sonnet 4.6
Best quality
Recommendations
Summarization
93%Claude Opus 4.6 -> Claude Haiku 4.5
Small quality drop, huge cost win
Code generation
80%Claude Opus 4.6 -> Claude Sonnet 4.6
Near-equal quality, much cheaper
Q&A
94%GPT-4o -> GPT-4o Mini
Good enough quality for high-volume queries
Product
Paste one prompt, pick models, and compare outputs, scores, cost, and latency on one page.
Run whole prompt sets across many models, then compare quality, reliability, and spend at scale.
Turn benchmark results into per-task model recommendations instead of one-size-fits-all choices.
How It Works
Use ModelGrade included access for the free tier, or connect your own OpenAI, Anthropic, or Google keys at any time.
Run Prompt Lab for one prompt or full benchmarks for whole prompt sets, then read every output side by side.
See quality scores, reliability, cost, and why a model is recommended before you change routing.
Why teams keep using it
LLM-as-judge, reference comparison, and task-specific evaluators with confidence and score provenance.
Track failed calls, skipped calls, score coverage, and provider health alongside cost and quality.
Compare one benchmark to another so teams can see what improved, regressed, or became cheaper.
Save a strong prompt from Prompt Lab into a reusable prompt set for future regression checks.
Export structured routing recommendations as JSON for gateways, scripts, or internal tools.
Call ModelGrade from CI, local scripts, or internal systems without rebuilding the workflow yourself.
SDK + automation
Use the Python SDK to upload prompts, run benchmarks, wait for completion, and export router configs from CI or internal tooling.
pip install modelgrade from modelgrade import ModelGrade with ModelGrade(api_key="mg_live_...") as client: job = client.benchmarks.wait("job_123") config = client.recommendations.get_config(job.id)
Demo
Benchmark reader
Prompt
Classify this ticket, summarize the issue, and recommend a safe response policy.
Why the score is trusted
Judge model used, confidence score, evaluator mix, and representative prompt examples are all shown with the result.
Summarization
Save 93%Claude Opus 4.6 -> Claude Haiku 4.5
Small quality drop, huge cost win
Code generation
Save 80%Claude Opus 4.6 -> Claude Sonnet 4.6
Near-equal quality, much cheaper
Q&A
Save 94%GPT-4o -> GPT-4o Mini
Good enough quality for high-volume queries
Pricing
Free
100 included checks per day. Each prompt x model counts as one check. Bring your own key anytime for provider-billed usage.
Start FreePro
Higher daily quota, more team usage, and the same ability to fall back to your own provider keys when needed.
See PlansEnterprise
For heavier workloads, larger teams, and guided deployment or private-environment rollout planning.
Talk to usGet started
Start free, compare real outputs, and turn prompt evaluation into something your team can repeat and trust.
No credit card required for the free tier