Which AI Model Should You Use?

There's no single best model — only best-for-the-job

Models trade off against each other. The frontier hosted models tend to lead on hard reasoning and instruction-following. Smaller and open models can be cheaper, faster, and self-hostable. The right pick depends on what you're actually doing.

Often the answer isn't one model at all — it's a strong one for the hard step and a cheap one for the bulk.

• Reasoning-heavy work (analysis, code, drafting): frontier models
• High-volume simple tasks (tagging, extraction): smaller, cheaper models
• Strict data residency: open models you host yourself
• Long documents: models with large context windows
• Latency-sensitive UX: faster, lighter models

The factors that actually decide it

We weigh the same handful of dimensions on every project. Rarely is one model best on all of them, which is why a mix is common.

Getting these right up front also avoids lock-in — so a better or cheaper model next quarter isn't a rewrite.

• Task difficulty and the accuracy you truly need
• Cost per request at your expected volume
• Latency your users will tolerate
• Context-window size for your inputs
• Data privacy and where processing is allowed to happen
• Vendor lock-in and how easily you can switch later

Test on your data, don't trust leaderboards

Public benchmarks are a starting point, not an answer. The model that wins on a generic test can lose on your specific documents and prompts.

We build a small evaluation set from your real tasks and compare candidates on it before committing — and add a layer that lets you swap models later without rebuilding.

• A small graded test set from your actual use case
• Head-to-head comparison on accuracy, cost, and speed
• An abstraction layer so you can swap models later
• Re-evaluation as new models ship (they ship often)

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