Claude vs GPT vs Gemini: A Decision Framework

Don't ask "which model is best" — ask "best at what, for how much, how fast." Frontier models converge in raw capability faster than any comparison chart can track, so build a framework instead of memorizing a leaderboard: 1. Task shape: long documents / large codebases favor models built for big context windows and agentic tool use. Short, high-volume tasks (classification, extraction, simple rewrites) favor small/fast tiers regardless of vendor. 2. Cost per task, not cost per token: a smaller model that needs three retries costs more than a larger model that gets it right once. Measure end-to-end. 3. Latency budget: an interactive chat UI and a nightly batch job have wildly different tolerances — pick the fastest model that clears your quality bar, not the most capable one available. 4. Multimodal need: if the task involves images, audio, or video, narrow to models that actually support that input natively rather than bolting on OCR/transcription. 5. Ecosystem fit: which vendor's tool-calling, structured-output, and agent SDKs match how your system is already built — switching later is possible but not free. Re-run this framework whenever a new model generation ships — the answer to "which one" changes constantly; the framework doesn't.