What is the effort parameter in Claude Opus 4.5?

The effort parameter is a unique feature of Opus 4.5 that allows developers to choose between High, Medium, and Low reasoning levels to balance response quality against token cost.

Which tasks are best suited for 'High' effort?

High effort is recommended for complex system design, difficult debugging, and autonomous agent tasks where maximum reasoning depth is required.

How much does Claude Opus 4.5 cost compared to 4.1?

Opus 4.5 is significantly more affordable, costing $5/M input tokens and $25/M output tokens, which is roughly 5x cheaper than the previous Opus 4.1 model.

Can I use Claude Opus 4.5 on Amazon Bedrock or Google Vertex AI?

Yes, Claude Opus 4.5 is available through the Anthropic API as well as major cloud providers including Amazon Bedrock, Google Vertex AI, and Microsoft Azure.

What is the context window for Claude Opus 4.5?

Claude Opus 4.5 supports a 200K token context window, making it ideal for multi-document analysis and long-form agentic workflows.

Claude Opus 4.5 Guide

Name: Claude Opus 4.5 Guide
Author: adaptationio

byadaptationio

학습 및 문서화

Provides comprehensive technical guidance for leveraging Anthropic's Claude Opus 4.5 model and its revolutionary reasoning control features.

소개

This skill serves as a definitive reference for developers and engineers working with Claude Opus 4.5, Anthropic's most intelligent model. It offers deep insights into the unique effort parameter, allowing users to dynamically control reasoning depth and token consumption across high, medium, and low levels. Whether you are optimizing API costs, implementing autonomous agents, or migrating from previous model versions, this guide provides the benchmarks, implementation patterns, and strategic advice needed to maximize the performance of state-of-the-art AI workflows.

주요 기능

Comprehensive documentation on the Opus 4.5 effort parameter for reasoning control
Strategic guidance for hybrid reasoning and token consumption optimization
Python and API implementation examples for rapid deployment
Detailed performance benchmarks for SWE-bench, MMLU, and GPQA Diamond
Model selection matrices comparing Opus, Sonnet, and Haiku tiers
0 GitHub stars

사용 사례

Building autonomous coding agents that leverage Opus 4.5's high SWE-bench accuracy
Optimizing production costs by adjusting reasoning effort for specific task complexities
Migrating legacy Claude 4.1 implementations to the more cost-efficient 4.5 architecture

소개

주요 기능

Comprehensive documentation on the Opus 4.5 effort parameter for reasoning control
Strategic guidance for hybrid reasoning and token consumption optimization
Python and API implementation examples for rapid deployment
Detailed performance benchmarks for SWE-bench, MMLU, and GPQA Diamond
Model selection matrices comparing Opus, Sonnet, and Haiku tiers
0 GitHub stars

사용 사례

Building autonomous coding agents that leverage Opus 4.5's high SWE-bench accuracy
Optimizing production costs by adjusting reasoning effort for specific task complexities
Migrating legacy Claude 4.1 implementations to the more cost-efficient 4.5 architecture