AI API Models Prices
Last updated: September, 2026
1. What's the hardest part of your task?
2. Where will it run?
3. What matters more?
How to choose a frontier model
1. Use them where they earn their price
Flagship models cost much more. Save them for the tasks where a mistake is expensive — and use cheaper models everywhere else.
2. Compare on your own task
Top models trade places depending on the job. Run the same hard request through two or three and pick the one whose answer you trust most.
3. Have a fallback
With one key you can switch between flagships instantly if one is slow or down.
Frequently asked questions
What is a frontier model?
The newest, most capable model from a major provider.
Why are they so expensive?
They are larger and cost more to run. You pay for top quality.
Can I mix frontier and cheap models?
Yes, and most teams do — just change the model name per request.
What does the rating for frontier models mean?
Each model is rated from 1.0 to 5.0 specifically for frontier. Green is 4.0 and above, yellow 3.0–3.9, red below 3.0. The same model can score differently in another category.
How is the rating calculated?
It comes from public data, not paid reviews: 50% production adoption (public leaderboard standing, routing popularity and download volume), 30% the benchmark that matters for this category, and 20% tooling and value for money (tool calling, structured outputs, context size and price against the category median).
What does the ✓ next to a rating mean?
It means an independent lab — LMArena or Artificial Analysis — has tested the model. Ratings without it are estimated from published specs such as context size, tooling support and price.
How are brand-new frontier models rated?
When a new model has strong independent benchmark results but no public usage data yet, adoption is left out instead of counted as low, and the rating uses 60% benchmark and 40% tooling and value.
How often are ratings updated?
Every week. If a source can't be reached, the previous week's figure is kept.
