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Evaluation & benchmarking

Prove accuracy on your own data before anything ships. We build held-out test sets from your verified outcomes and run your model side by side with Claude Opus 5.5, Claude Fable 5.1, GPT-6 Astra and GPT-6.1 Sol, measuring accuracy, cost and latency on the tasks you actually run.

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Engagement

What’s included in this service?

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Held-out test sets

Built from your verified outcomes and kept out of training, so every score reflects tasks the model has never seen.

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Frontier comparisons

Side-by-side runs against Opus 5.5, Fable 5.1, GPT-6 Astra and GPT-6.1 Sol on the same tasks, with the same inputs.

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Cost and latency tracking

Accuracy, cost per 1,000 tasks, cost per 1,000 successful tasks, median latency and fallback rate for every model and version.

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Release quality gates

A release that regresses on a tracked task is blocked before it reaches production traffic.

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Why it matters

Why this service matters

Public benchmarks don’t tell you how a model reads your orders, your requisitions or your abbreviations. On held-out customer tasks, Applied Crew models average 97.4% accuracy at $1.40 per 1,000 tasks, against 93.1% for Claude Fable 5.1 and 92.6% for GPT-6 Astra at $65.00.

Scored on your data, not public benchmarks
Cost per 1,000 successful tasks
Fallback rate tracked per version
No release ships if it regresses
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FAQ

Frequently asked questions

Have more questions?

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Why not rely on public benchmarks?

They measure general ability. Your decision depends on accuracy on your own tasks, so every test set we build comes from your verified outcomes and stays out of training.

How do frontier models score on customer tasks?

Averaged across held-out customer tasks: Claude Fable 5.1 93.1%, GPT-6 Astra 92.6%, Claude Opus 5.5 91.8%, GPT-6.1 Sol 88.9%. Applied Crew fine-tuned models average 97.4%.

What is cost per 1,000 successful tasks?

Cost per 1,000 tasks divided by the share completed correctly. A cheaper model that fails more often can cost more per correct result, so we report both.

Can we benchmark before committing to fine-tuning?

Yes. We benchmark frontier models on your own data first, so you see the baseline and the expected gain before you commit to anything.

What blocks a release?

Any regression against the previous version on a tracked task. The gate checks accuracy, cost and latency, and the release does not ship until it passes.

Your own model, trained on your work, in production in weeks

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Applied CrewOakland, California

We train AI models on how your company works, and keep them improving.

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