Legacy repository
AI Models & Assistants / Knowledge library
Claude Opus 5.5
For long-running agentic coding and knowledge work
Why learn Claude Opus 5.5?
Offers the documented Anthropic model capabilities through hosted.
The problem it solves
Provides model inference for supported text inputs.
Where it came from
Claude Opus 5.5: For long-running agentic coding and knowledge work Published aliases: claude-opus-5-5. 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 Opus 5.5 does.
- Identify a use case for Claude Opus 5.5.
- Describe a limitation of Claude Opus 5.5.
- Name a compatible tool for Claude Opus 5.5.
- Compare Claude Opus 5.5 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
Automated Legacy Codebase Migration
Enterprise software firms managing massive, multi-decade legacy codebases.
The business problem
Manual refactoring of monolithic legacy systems is error-prone, costly, and often stalls due to the sheer volume of interdependencies that exceed standard context windows.
How this topic solves it
Claude Opus 5.5 utilizes a 1M token context window and adaptive thinking to analyze entire repository structures, allowing it to perform complex refactoring tasks while maintaining architectural consistency across long-running agentic sessions.
- Ingest the entire legacy codebase into the 1M token context window.
- Initialize the agentic loop using the claude-opus-5-5 model ID.
- Configure the display setting to ensure progress updates are captured from thinking blocks.
- Execute iterative refactoring tasks using the adaptive thinking capability to resolve interdependencies.
- Validate output against the original system architecture.
The flow shows how the model processes code and manages state through thinking blocks.
Model with adaptive thinking
Internal reasoning
Output
- Codebase → Ingest → Opus 5.5
- Opus 5.5 → Generates → Thinking Block
- Thinking Block → Refines → Opus 5.5
- Opus 5.5 → Produces → Refactored Code
Implementation example
This code snippet demonstrates how to configure the display setting to ensure that progress updates, which occur in thinking blocks between tool calls, are accessible to the application.
# Example of handling thinking blocks in the streaming response
# The display setting must be configured to see progress between tool calls
response = client.messages.create(
model="claude-opus-5-5",
messages=[{"role": "user", "content": "Refactor this module..."}],
thinking={"display": "visible"} # Ensures text between tool calls is returned
)Expected business outcome
A more maintainable, modular codebase achieved through automated, context-aware refactoring that preserves business logic integrity.
Limitations and checks
The model's adaptive thinking is always enabled, which can introduce latency; developers must monitor the display setting to ensure streaming progress updates are visible between tool calls.