Raw archival text files
AI Models & Assistants / Knowledge library
Claude Haiku 4.5
Claude Haiku 4.5 is a versioned Claude language model. Consult its lifecycle and model documentation before selecting it.
Why learn Claude Haiku 4.5?
Offers the documented Anthropic model capabilities through hosted.
The problem it solves
Provides model inference for supported text inputs.
Where it came from
Claude Haiku 4.5: Claude Haiku 4.5 is a versioned Claude language model. Consult its lifecycle and model documentation before selecting it. Published aliases: claude-haiku-4-5-20251001. Input: text. Output: consult source. Checked 2026-10-13. Model capabilities are distinct from tools available in a particular agent.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Explain what Claude Haiku 4.5 does.
- Identify a use case for Claude Haiku 4.5.
- Describe a limitation of Claude Haiku 4.5.
- Name a compatible tool for Claude Haiku 4.5.
- Compare Claude Haiku 4.5 against an alternative.
Documentation and sources
- Primary source / model card (platform.claude.com)
Catalog research notes
- See the AI Workbench for source dates, pricing evidence and configuration details.
Connect the ideas
Built on
Real World Example
Legacy Archive Summarization for Legal Compliance
Legal firms managing massive volumes of historical case files that require structured metadata extraction.
The business problem
High costs and latency when using larger, adaptive-thinking models to process static, legacy document archives that do not require real-time reasoning.
How this topic solves it
Utilizing the Claude Haiku 4.5 legacy model with manual extended thinking to perform cost-effective, high-throughput extraction of key entities from 200K token document batches.
- Identify legacy document sets that fall within the February 2025 knowledge cutoff.
- Configure the API request to use the claude-haiku-4-5-20251001 model ID.
- Enable manual extended thinking by setting the budget_tokens parameter to guide the model's reasoning depth.
- Process batches of documents using the 200K token context window to minimize fragmentation.
- Validate extracted metadata against existing database schemas.
The flow of archival documents through the Haiku 4.5 processing engine.
claude-haiku-4-5-20251001
Structured extraction results
- Document Store → Batch Input (200K tokens) → Haiku 4.5 Engine
- Haiku 4.5 Engine → Structured Output → Metadata DB
Expected business outcome
A stable, predictable cost structure for bulk processing of archival data without the overhead of adaptive-thinking model pricing.
Limitations and checks
The model is a legacy version with a February 2025 knowledge cutoff; check for data drift by comparing a sample of outputs against current regulatory standards.