What happened: With dozens of viable models across cloud and local, "which model should I use" doesn't have one answer — it depends on four variables that matter more than benchmark leaderboards: latency needs, context length, cost per token, and data residency.
Why it matters: Teams often over-index on capability benchmarks and under-index on the constraints that actually determine whether a model works in production — a brilliant model that's too slow or too expensive for your volume is the wrong choice.
The decision tree
- Data can't leave your network? → local model (Ollama, quantized 8B–70B class)
- Need the largest context window or frontier reasoning? → cloud flagship (Claude, GPT, Gemini top tier)
- High volume, narrow task (extraction, tagging, routing)? → small/fast cloud tier or local 8B model
- Latency-critical, user-facing chat? → fast cloud tier, benchmark time-to-first-token specifically
What to actually benchmark
Run your own eval set on your own task — public leaderboards rarely reflect your specific data shape. Cost per successful task (not per token) is the number that matters at scale.
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