Anthropic has published comprehensive documentation for Claude Haiku 4.5, outlining its intended applications, technical specifications, and integration pathways. The documentation covers aspects from its core functionality to its availability on various platforms.
Key Points
- Claude Haiku 4.5 supports manual extended thinking, enabled by
budget_tokens. - The model uses
thinking.type: "enabled"for manual extended thinking, not adaptive thinking. - Claude Haiku 4.5 is available through the InvokeModel Bedrock integration.
- A convenience alias,
claude-haiku-4-5, resolves to the pinned snapshotclaude-haiku-4-5-20251001. - The documentation includes a full price list, detailing batch discounts and prompt caching rates.
- It provides guidance on when to prioritize Haiku for efficiency and when to use larger models.
- The system prompt used by Claude Haiku 4.5 on claude.ai and Claude apps is described.
Context
According to Anthropic, the documentation for Claude Haiku 4.5 offers a complete overview, including its context window, output limits, and availability. It also addresses safety evaluations and deployment decisions for the model. The documentation further explains how model IDs, aliases, and pinned snapshots function, along with the lifecycle status and retirement commitments for all Claude models.
Why It Matters
Builders can use this information to understand the specific capabilities and cost implications of Claude Haiku 4.5, enabling informed decisions about its application in their projects. The details on model IDs and integration options clarify how to deploy and manage the model effectively.
What To Do
- Review the Claude Haiku 4.5 documentation for its full price list, including batch discounts.
- Note the model IDs and versioning details, especially the
claude-haiku-4-5-20251001snapshot. - Compare the use cases for Haiku against larger models to optimize for efficiency or capability.
- Explore the InvokeModel Bedrock integration if deploying on Bedrock.
- Query limits and capabilities programmatically using the Models API.
