Benchmarks tell you which model is smartest. They don't tell you which model is right for your workload. Here's the decision framework I use after shipping several AI features.
Start with the task, not the model
Classification and extraction. Small, cheap models win outright. You need consistent schema adherence, not prose quality. Cost difference can be 20-50x versus frontier models.
Drafting and summarisation. Mid-tier models hit the quality bar for most business writing. Test one tier below your instinct before paying for the top.
Complex reasoning and code. Frontier models earn their price here — but only for the subset of traffic that genuinely needs it.
Latency and context matter too
A model with great quality but 8-second p95 latency will hurt your product more than a slightly weaker fast one. Long-context tasks also price differently: a model that's cheap per token can be expensive once you're sending 100K-token prompts.
Build a scorecard
For each candidate model, record: quality on your own eval set, p50/p95 latency, cost per successful task, and failure modes (refusals, malformed JSON). Rank on cost per successful task, not cost per token — retries are real money.
Where to compare
Public per-token tables make the first cut cheap. Our full rate card across GPT-class, Claude-class, Gemini-class, DeepSeek and open-source models lives here: KeyoAPI [OI] rates.
One prepaid key, one [OI]-compatible endpoint, so you can A/B a candidate model in minutes rather than rebuilding an integration.
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