Claude 4.5 Opus
Anthropic's most powerful flagship model, setting the state of the art in coding, agentic workflows, and real-world computer use.
Anthropic Flagship model for coding and agentic workflows
Claude 4.5 Opus is Anthropic's top-tier large language model, released on November 24, 2025. It is designed for demanding tasks including software engineering, long-horizon autonomous workflows, and complex reasoning, with a 200,000-token context window that supports multi-file operations and extended document analysis. The model includes an "effort" parameter that gives developers control over reasoning depth, allowing optimization for either speed or accuracy depending on the task at hand.
Claude 4.5 Opus is particularly suited for enterprises and developers working on large-scale software engineering, autonomous agent orchestration, financial modeling, legal analysis, and deep research workflows. It features enhanced computer use capabilities, including a zoom tool for detailed screen inspection, enabling UI-based automation. Early users reported that the model handles ambiguous, multi-system problems with minimal guidance, and some reported token usage reductions of up to 65% compared to earlier models when solving equivalent problems.
What Claude 4.5 Opus supports
Large Context Window
Processes up to 200,000 tokens in a single context, enabling multi-file code operations, long document analysis, and extended retrieval tasks.
Tool Use
Supports structured tool calling, allowing the model to invoke external functions and APIs as part of multi-step task execution.
Advanced Reasoning
Applies deep reasoning to complex, ambiguous problems with an "effort" parameter that lets developers tune reasoning depth for speed or accuracy.
MCP Support
Compatible with Model Context Protocol (MCP) servers, enabling integration with external data sources and services in agentic pipelines.
Agentic Orchestration
Designed to act as an orchestrator for long-horizon autonomous workflows, maintaining state across extended sessions and coordinating multiple agents simultaneously.
Computer Use
Includes enhanced computer use capabilities with a zoom tool for detailed screen inspection, supporting reliable UI-based automation tasks.
Code Generation
Handles complex refactors, multi-file code migrations, and sustained autonomous coding sessions, with benchmark results on SWE-bench Verified.
Configurable Effort
Exposes a numeric "effort" parameter so developers can dial reasoning intensity up or down, balancing latency against output depth per request.
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Get Started FreeBenchmark scores
Scores represent accuracy — the percentage of questions answered correctly on each test.
| Benchmark | What it tests | Score |
|---|---|---|
| MMLU-Pro | Expert knowledge across 14 academic disciplines | 88.9% |
| GPQA Diamond | PhD-level science questions (biology, physics, chemistry) | 81.0% |
| LiveCodeBench | Real-world coding tasks from recent competitions | 73.8% |
| HLE | Questions that challenge frontier models across many domains | 12.9% |
| SciCode | Scientific research coding and numerical methods | 47.0% |
| SWE-bench Verified | Real GitHub issues requiring multi-file code fixes | 80.9% |
| Terminal-Bench | Agentic coding and terminal command tasks | 59.3% |
| ARC-AGI-2 | Novel abstract reasoning and pattern recognition | 37.6% |
| MMMLU | Multilingual and multimodal understanding | 90.8% |
Common questions about Claude 4.5 Opus
What is the context window size for Claude 4.5 Opus?
Claude 4.5 Opus has a 200,000-token context window, which supports long-context retrieval, multi-file code operations, and extended document workflows.
What is the training data cutoff for Claude 4.5 Opus?
Based on the available metadata, the training data cutoff is November 2025.
What tasks is Claude 4.5 Opus best suited for?
It is designed for demanding tasks such as large-scale software engineering, autonomous agent orchestration, deep research, financial modeling, legal analysis, and complex document workflows.
Does Claude 4.5 Opus support tool use and MCP integrations?
Yes. The model supports structured tool calling and is compatible with Model Context Protocol (MCP) servers, making it suitable for agentic pipelines that connect to external data sources and services.
What is the "effort" parameter and how does it work?
The "effort" parameter is a numeric input that lets developers control the model's reasoning depth on a per-request basis, allowing them to trade off between response speed and reasoning thoroughness depending on the task.
What people think about Claude 4.5 Opus
Community discussion around Claude 4.5 Opus has been active since its release, with users highlighting its performance on autonomous research tasks and software engineering benchmarks. A widely shared post reported over a 100x speed improvement on autonomous AI research workflows, and a leaked internal "soul document" generated significant discussion about the model's design philosophy.
Some community members raised questions about benchmark reporting practices, with one thread specifically discussing concerns about deceptive benchmark results. Comparative benchmarks against later model versions, including a 3D VoxelBuild test, have also been a recurring topic, reflecting ongoing interest in tracking how the model holds up over time.
Claude 4.5 opus is over a 100x speed up on autonomous ai research (beating anthropic threshold)
Leaked Claude 4.5 Opus "Soul document"
Difference Between Opus 4.6 and Opus 4.5 On My 3D VoxelBuild Benchmark
Claude 4.5 Opus deceptive benchmark reporting
Difference Between Opus 4.6 and Opus 4.5 On My 3D VoxelBuild Benchmark
Documentation & links
Parameters & options
When enabled, the model will explain its thought process step-by-step before providing a final answer. This can help users understand how the model arrived at its conclusions, but may result in longer responses.
You can allocate a larger thinking budget to support more thorough reasoning. Must be less than max. response size
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