ConsenseWhich LLM should we use for our product?
The best model on a general leaderboard is frequently not the best model for a specific, narrow task at your latency and cost budget. Model choice should follow from your task's shape, not from whichever model is newest.
Ask the AI panel this question →Free to try. GPT, Claude, Gemini and DeepSeek answer independently.
What actually decides it
Task shape, not general capability
Structured extraction, classification and narrow generation often run well on smaller, cheaper models. Open-ended reasoning and long-context synthesis are where frontier models earn their cost.
Latency and cost budget
A model that's 2% better on a benchmark but 5x the cost and twice the latency is usually the wrong trade for a production path in the critical user flow.
Vendor lock-in and portability
Prompts and evals tied tightly to one provider's quirks are expensive to port later. Keep the abstraction thin if you might switch.
Evaluate on your own task
Public benchmarks rarely match your actual data and failure modes. A small, real eval set beats trusting a leaderboard.
What would change the answer
- Results from evaluating 2-3 candidate models on your own task and data
- A hard latency or cost ceiling for the specific product surface
- Whether the task benefits from a specialist model (e.g. embeddings, code) over a general one
Get a verdict on your situation
Generic advice only goes so far. Add your numbers and see where four leading AI models agree, where they split, and what could change the answer.
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