# OpenAI API vs Claude API: Which to Build On

> OpenAI and Claude API compared on price per million tokens, 1M-token context, tool use and long-context surcharges — plus how to decide with your own eval.

- Canonical: https://guides-ai.pages.dev/guides/openai-vs-claude-api/
- Plate 11.04 · Topic: Choosing tools (https://guides-ai.pages.dev/topics/choosing/)
- Published: 02 Sept 2026 · Updated: 06 Sept 2026 · 3 min read
- Source site: guides-ai — https://guides-ai.pages.dev/

**Neither API is generically better, and the gap that used to decide it — context window — has closed.** Both vendors' current flagships hold about a million tokens. What is left is price per tier, how your workload maps onto that tier, and which SDK your codebase already speaks.

## Price and size, side by side

Published list prices per million tokens, September 2026:

| Model | Context | Input | Output |
| --- | --- | --- | --- |
| GPT-6 Astra | 1.05M | $10 | $50 |
| Claude Opus 5 | 1M | $5 | $25 |
| GPT-5.6 Sol | 1.05M | $4 | $20 |
| GPT-5.6 Terra | 1.05M | $2 | $12 |
| Claude Sonnet 5 | 1M | $2 | $10 |
| Claude Haiku 4.5 | 200K | $1 | $5 |

Compare across rows, not vendors: Sonnet 5 and Terra are the same tier at nearly the same price, and either is the sane default. Reach for a top row only when an eval says the cheaper tier fails. Model naming is a moving target — pin the exact [model ID](/glossary/#model-id) rather than a floating alias.

## The costs that don't show up in the table

Long inputs can change the rate. OpenAI charges 2× input and 1.5× output on requests over 272K input tokens for the whole session, so a "cheap" model streaming a million tokens is not cheap. Discounts run the other way: Anthropic's Batch API is 50% off and cache reads cost 10% of base input, which reshapes any workload with a stable prefix — see [prompt caching](/guides/claude-api-prompt-caching/) and [batch requests](/guides/claude-api-batch-requests/).

## Tool use and structure

Both APIs define tools as JSON schemas, let the model choose when to call one, and loop until the task is done; both enforce structured output against a schema. The mechanics differ enough to matter when porting — compare [Claude tool use](/guides/claude-api-tool-use/) with [OpenAI structured outputs](/guides/openai-api-structured-outputs/) — but no idea in one is missing from the other.

## Decide with an eval, not a table

Run this against both, on twenty real inputs, before you commit:

```text
You are scoring two model outputs for the same task.
Task spec: <paste your actual prompt>
Output A: <model 1>
Output B: <model 2>
Score each 1-5 on: instruction-following, factual accuracy, format
compliance, and verbosity. Return a markdown table, then one sentence
naming the cheaper output that still passes.
```

Twenty of your own inputs beat any benchmark. Then read the numbers off the [Claude models overview](https://platform.claude.com/docs/en/about-claude/models/overview), because prices move.