Claude Opus 4.6 chat from Anthropic through an OpenAI-compatible endpoint. It takes up to 1,000,000 tokens of text and image context and returns up to 128,000, with temperature and top_p control, up to 128 tool definitions and SSE streaming.
Workbench
These examples were written for this endpoint. Replace the prompt and sample media URLs with your own accessible inputs, then check the current quote and model availability before submitting.
- Input types
- Text
- Output types
- Text
- Input
- $5 / 1 million tokens
- Cached input
- $0.50 / 1 million tokens
- Output
- $25 / 1 million tokens
- Context
- 1M
- Max output
- 128K
$5/25per 1 million tokens · input / output
persona-continuity Continuing a long exchange. History, persona and established limits all travel with the request, with no summarisation layer in between - that is what decides whether the character still remembers, thirty turns in, what it said. Leaving temperature at the default is what keeps the register alive.
- 4096
- 1
- Input types
- Text
- Output types
- Text
- Input
- $5 / 1 million tokens
- Cached input
- $0.50 / 1 million tokens
- Output
- $25 / 1 million tokens
- Context
- 1M
- Max output
- 128K
$5/25per 1 million tokens · input / output
long-running-refactor A change that spans several files with dependencies between the steps. What is tight here is not the output length but whether it remembers the decision it made first, so the whole context stays in the request.
- 16000
- Input types
- Text
- Output types
- Text
- Input
- $5 / 1 million tokens
- Cached input
- $0.50 / 1 million tokens
- Output
- $25 / 1 million tokens
- Context
- 1M
- Max output
- 128K
$5/25per 1 million tokens · input / output
spec-vs-implementation Specification and implementation in the same request, with the model left to find what does not line up.
- 8000
This example starts at Input $5 / Output $25 per 1 million tokens. The Playground updates the estimate when you change the settings.
