This post was created with AI assistance and reviewed for accuracy before publishing.
Claude, Gemini, and GPT families all ship frequent updates. Public benchmarks move slower than weekly model tweaks, so your selection criteria should be operational: latency, price per token, context window, tool-calling quality on your stack, and compliance (data residency, logging).
Run your own evals
Create a dozen real tasks from your repo: refactors, bug fixes, test authoring. Score outcomes with the same rubric across vendors. One heroic run is not data.
Cost and caps
Compare input versus output pricing and whether your workload is token-heavy on either side. Watch org-level rate limits during spikes.
Practical takeaway
Document a model policy per use case (interactive dev, batch translation, customer-facing chat). Revisit quarterly as vendors ship new defaults.
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