Best AI API

Who this page is for

This page is for developers and product teams who call a language model through an API: chat features, document processing, extraction, agents that call your own functions, or any back-end job that sends tokens and pays per token. It is about the models behind those APIs, not about chat apps, SDKs or hosting platforms as such.

How this ranking works

The ranking combines five criteria into one score. The weights are fixed and add up to 100%. Our methodology explains how each criterion is scaled and combined.

• Text Arena score, 45%. Text Arena ranks models from blind pairwise votes by people across many everyday prompts, on one shared public leaderboard ^[1]. It is the broadest third-party quality signal we have for API models, so it gets the largest weight. Higher is better, on a linear scale.
• Blended price, 30%. API cost grows with every request, so the list price per token matters. We blend input and output prices at three input tokens per output token. Lower is better, on a log scale, so that a few very expensive models don't flatten the differences among the rest.
• Cached input price, 10%. Applications that resend long system prompts, documents or tool definitions pay mostly the cached rate, and the cache discount differs widely between models. In our catalog the cached rate ranges from 2% of the input price for DeepSeek-V4.1-Flash ^[2] to 25% for several models, for example Grok 4.6 and 4.7 ^[3]. Lower is better, on a log scale.
• Context window, 10%. A larger window lets an application send more documents or history in one request. Larger is better, on a log scale.
• Tool calling, 5%. Most API integrations call functions or tools. The weight is low because nearly all current text models support it.

Which Text Arena results count

Text Arena rows are recorded with slightly different labels, for example notes on the reasoning-effort variant of a model. For this ranking we treat all rows from the Overall leaderboard with style control as one comparable group, whatever the reasoning effort or variant note. A model counts with its best result in that group. Independent results are preferred over results a provider reports itself; if only a provider result exists, it is used and marked "self-reported". The methodology box on this page lists every stored setting that belongs to the group.

A model needs data for at least 60% of the total weight to be ranked. Price, cached input, context and tool calling together make up 55%, so a model without a Text Arena result is listed as "not ranked" instead of being ranked on price alone.

Batch pricing is not a criterion. Where providers list it, it is usually half the standard price ^{[4] [5] [6]} (xAI lists 80% ^[3]), so it would mostly repeat the price criterion.

How to read the results

• Scores are relative. Each criterion is rescaled from 0 to 100 across the candidates on this page. 100 means best among these models, not perfect, and adding or removing a model can shift the others.
• Check the setting. The table shows the setting behind each Text Arena score, such as the effort variant on the leaderboard row.
• "Self-reported" marks a result that comes from the model's own provider rather than from the public leaderboard.
• Small gaps are noise. Arena ratings move as votes come in. Treat models a few points apart on Text Arena, or close in overall score, as roughly equal.
• Price can dominate the lower half. With 40% of the weight on cost, a cheap model with a modest quality score can sit close to a more capable but expensive one. Look at the individual criteria in the table, not only the total.

Limitations

• Text Arena reflects people's preferences in open-ended chat. It does not directly measure extraction accuracy, instruction following in long prompts or tool-use reliability ^[1].
• The grouped Text Arena rows differ in reasoning effort and variant. Grouping lets us compare most current API models, at the cost of comparing runs that were not set up identically.
• Only models with a Text Arena result in the group can be ranked. A missing result doesn't mean a model is weak, only that we have no comparable measurement.
• List prices change often and can differ by region, tier and long-context surcharges. Check the provider's pricing page before committing ^{[7] [8] [6]}.
• We have no data field yet that records whether a provider offers batch processing or caching at all; cached pricing only counts where a price is listed.
• The data behind this page is as of October 2026.

All published results

The table lists every Text Arena result we hold, with the setting as recorded and who reported it.

How to choose for your own work

1. Estimate your token mix first. Work out input tokens, output tokens and how much of each prompt repeats. Then compare models at that volume with AI pricing and the cost calculator. Prices span a wide range: GPT-6 Luna and Claude Haiku 5.5 list $0.10 per million input tokens and $0.50 per million output tokens ^{[7] [8]}, while Claude Fable 5.1 and GPT-6 Astra list $10 and $50 ^{[8] [7]}.
2. Check the cached rate if you resend context. For long system prompts or retrieval-heavy apps, the cached input price can matter more than the list price ^{[2] [3]}.
3. Use batch processing for work that can wait. OpenAI, Anthropic and Google list batch prices at about half the standard rate ^{[4] [5] [6]}.
4. Shortlist across price tiers. Pick one small, one mid-size and one frontier model from the ranking and the model pages, and compare them with model comparisons: context window, maximum output, tool calling and data policy.
5. Test on your own prompts. Run 20 to 50 real requests through each candidate and grade the answers. That is the only quality result that measures your task.

Related guides

• How to choose an AI model
• How to compare AI models
• AI token pricing explained
• How to reduce AI API costs
• Context windows explained