Choosing a model
A practical method for narrowing 54 models down to the two or three worth testing.
Last updated 2026-07-24
Model choice is usually presented as a leaderboard question. It is closer to a fit question: the best model for a task is the cheapest one that clears your quality bar reliably. Working in this order tends to get there fastest.
1. Start with the job, not the model
Name the task precisely, 'classify support tickets into eight categories' rather than 'do NLP'. The category pages exist for this reason: each one explains what its models are actually for and how the trade-offs inside it differ.
2. Eliminate on hard constraints
Constraints are cheaper to apply than judgement. Filter on the things that are simply true or false for your situation before you weigh anything subjective.
- Context window: will your longest realistic input fit, with room for the response?
- Capabilities: do you need tool calling, JSON mode, or vision input?
- Open weights: do licensing or self-hosting requirements apply?
- Origin: do procurement or data-residency rules constrain provider country?
3. Compare the survivors
Put the remaining candidates side by side and look at price, latency and benchmark groups together rather than one at a time. A model that is 40% cheaper but twice as slow is a different trade for a batch pipeline than for a chat interface.
Warning: Benchmark scores are directional, not decisive. They tell you which models are plausible for a task; only your own evaluation on your own data tells you which one is right.
4. Plan the second choice
Pick a fallback at the same time as the primary. Providers have outages, deprecate models and change capacity. Knowing your second choice in advance turns an incident into a configuration change.
How we present benchmarks
Every benchmark on Webparam is grouped rather than averaged into a single number: reasoning, coding and multimodal scores are shown separately because a model strong at one is frequently mediocre at another, and a composite score hides exactly the trade-off you are trying to see.
- Scores sit beside a peer-median tick, so a number has a scale rather than floating free
- A model with no score for a benchmark shows an em dash, never a zero, which would read as a failure rather than an absence
- Media and retrieval models are not scored on text benchmarks at all, and say so instead of showing blanks
- Every surface carries the same caption, because these figures are illustrative placeholders in this build
What we deliberately do not do is rank the catalogue by a single composite score. A leaderboard would be the most clickable thing on the site and the least useful. It answers a question nobody actually has.