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.
Originally published on ayraix.com, practical AI for enterprise builders.
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