prompting-claude-sonnet-5

 description: Behavioral differences and prompting patterns for Claude Sonnet 5, covering effort, adaptive thinking defaults, tool use, and migration from Claude Sonnet 4.6.
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-This guide covers the prompting patterns specific to Claude Sonnet 5. For the model's capabilities and API changes, see [What's new in Claude Sonnet 5](https://platform.claude.com/docs/en/about-claude/models/whats-new-sonnet-5). For techniques that apply across all current Claude models, see [Prompting best practices](https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/claude-prompting-best-practices).
+This guide covers the prompting patterns specific to Claude Sonnet 5. For the model's capabilities and API changes, see [What's new in Claude Sonnet 5](https://platform.claude.com/docs/en/models/sonnet-5/whats-new-sonnet-5). For techniques that apply across all current Claude models, see [Prompting best practices](https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/claude-prompting-best-practices).
 
 Claude Sonnet 5 has particular strengths in coding and agentic tasks. It performs well out of the box on existing Claude Sonnet 4.6 prompts. The patterns in this guide cover the behaviors that most often require tuning.
 

 Manual extended thinking (`thinking: {type: "enabled", budget_tokens: N}`) is not supported on Claude Sonnet 5 and returns a 400 error. It was deprecated on Claude Sonnet 4.6 and is now removed. Use adaptive thinking with the effort parameter instead.
 
 <Note>
-  If you are running Claude Sonnet 5 at `high`, `xhigh`, or `max` effort, leave headroom in `max_tokens` so the model has room for thinking and tool calls. On long tasks, adaptive thinking can use a large share of the budget; if the budget is tight, you may see a response that is almost entirely thinking followed by a truncated answer and `stop_reason: "max_tokens"`. Raising `max_tokens` or dropping to `medium` effort resolves this. Because Claude Sonnet 5 uses a [new tokenizer](https://platform.claude.com/docs/en/about-claude/models/whats-new-sonnet-5#new-tokenizer) that produces approximately 30% more tokens for the same text, `max_tokens` limits tuned for Claude Sonnet 4.6 may truncate equivalent output. The exact increase depends on the content and workload shape.
+  If you are running Claude Sonnet 5 at `high`, `xhigh`, or `max` effort, leave headroom in `max_tokens` so the model has room for thinking and tool calls. On long tasks, adaptive thinking can use a large share of the budget; if the budget is tight, you may see a response that is almost entirely thinking followed by a truncated answer and `stop_reason: "max_tokens"`. Raising `max_tokens` or dropping to `medium` effort resolves this. Because Claude Sonnet 5 uses a [new tokenizer](https://platform.claude.com/docs/en/models/sonnet-5/whats-new-sonnet-5#new-tokenizer) that produces approximately 30% more tokens for the same text, `max_tokens` limits tuned for Claude Sonnet 4.6 may truncate equivalent output. The exact increase depends on the content and workload shape.
 </Note>
 
 ## Tool use triggering