Claude 4.5 Opus
Claude 4.5 Opus is a text generation model from Anthropic with a 200,000-token context window and extended reasoning support.
AnthropicExtended reasoning with large context and tools
Claude 4.5 Opus is a large language model developed by Anthropic, released in November 2025. It supports a 200,000-token context window and can generate responses up to 64,000 tokens in length. The model accepts text and image inputs and is designed for complex, multi-step tasks that benefit from extended reasoning capabilities.
Claude 4.5 Opus is built with native support for tool use and Model Context Protocol (MCP) servers, making it well-suited for agentic workflows where the model needs to interact with external systems or APIs. Its configurable reasoning mode allows developers to allocate additional compute toward deliberate, step-by-step problem solving. These characteristics make it a practical choice for tasks like document analysis, code generation, research assistance, and multi-tool orchestration.
What Claude 4.5 Opus supports
Extended Reasoning
Supports a configurable reasoning mode that enables step-by-step deliberation before producing a final response. Developers can set a maximum reasoning token budget to control compute usage.
Large Context Window
Accepts up to 200,000 tokens of input per request, allowing entire codebases, long documents, or multi-turn conversation histories to be processed in a single call.
Tool Use
Natively supports structured tool calling, enabling the model to invoke external functions or APIs as part of a response. Tool definitions are passed as a structured input at inference time.
MCP Server Integration
Supports Model Context Protocol (MCP) servers as a first-class input type, allowing the model to connect to and interact with MCP-compatible external services and data sources.
Vision / Image Input
Accepts image inputs alongside text, enabling tasks such as document understanding, diagram analysis, and visual question answering within the same 200,000-token context.
Long-Form Output
Can generate responses up to 64,000 tokens, supporting detailed reports, lengthy code files, or extended structured documents in a single completion.
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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 supports a context window of 200,000 tokens, meaning you can include up to 200,000 tokens of combined input (text, images, conversation history) in a single request.
What is the maximum response length?
The model can generate responses up to 64,000 tokens in a single completion, which is suitable for long-form documents, detailed code, or extended structured outputs.
Does Claude 4.5 Opus support image inputs?
Yes. Claude 4.5 Opus accepts both text and image inputs, allowing you to include visual content such as screenshots, diagrams, or documents alongside your text prompts.
What is the reasoning feature and how does it work?
The reasoning feature enables the model to perform extended, step-by-step deliberation before producing its final answer. You can configure it via the 'Reasoning' select input and set a token budget using the 'Max Reasoning Size' number input to control how much compute is allocated to the reasoning process.
What is MCP support and why does it matter?
MCP stands for Model Context Protocol. Claude 4.5 Opus accepts MCP server configurations as a native input type, allowing it to connect to external tools and data sources that implement the MCP standard. This is useful for building agentic applications that need to interact with live systems.
What is the pricing for Claude 4.5 Opus on MindStudio?
Pricing information for Claude 4.5 Opus is not published in the current metadata. You can check MindStudio's pricing page or Anthropic's official API pricing page for the latest rates.
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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