What Benchwright actually does
You connect your models. We run the evals, catch the regressions, and tell you what to swap. That's the whole loop.
Connect your model endpoints
Point Benchwright at your model providers and API keys — OpenAI, Anthropic, Google, or any OpenAI-compatible endpoint. You can also pipe in your production traffic logs so we evaluate on real prompts, not just synthetic ones.
Benchwright runs continuous evaluations
The agent runs your models against curated benchmark suites — accuracy, reasoning, instruction-following, latency, cost per request — plus any real prompts from your traffic. Evaluations run on a schedule you set, or continuously on Pro.
Detects drift, regressions, and cost anomalies
When a model's accuracy drops, a cost-per-request spikes, or latency degrades beyond your thresholds — Benchwright flags it. Not as a number on a dashboard you'll never open. As an alert with context: what changed, by how much, and since when.
Delivers plain-language reports with recommendations
You get an email — or a Slack message — that says: "GPT-4.1 accuracy dropped 3.8% on coding tasks since last Tuesday. Claude Sonnet 4 outperforms it by 7% at 40% lower cost. Recommend swapping for coding tasks." No log diving. No interpretation required.
Eval scripts to maintain
No pytest fixtures, no custom eval harnesses, no "who owns this script" conversations when a model provider changes an API. Benchwright is the eval infrastructure.
Dashboards to babysit
You don't need to log into anything. If something breaks or degrades, we tell you. Silence means the models are healthy.
Quarterly review meetings about model performance
Real-time detection means issues surface in hours, not quarters. The postmortem meeting for "we didn't notice the regression until customers complained" goes away.
Models benchmarked live
GPT-4o, GPT-4.1, Claude Sonnet 4, Gemini 2.5 Pro, Llama 4, Mistral, and more — cost and performance, updated continuously.
Compare them now →Published eval reports
Deep-dives on LLM evaluation methodology, regression patterns, and what actually matters when benchmarking models in production.
Read the blog →Continuous evaluation
The agent doesn't sleep. Pro and Team plans run evaluations continuously — not just nightly — so drift is caught within hours.
Flat monthly pricing
No per-eval charges, no per-seat pricing, no usage-based surprises. One price, one application, daily evals. Scale up when you need it.
See plans →Get notified when we launch
Early access is free. You'll be the first to know — and the first to get Benchwright running on your models.
Or jump straight to the model comparison calculator →