Claude 4.6 Opus
Claude 4.6 Opus is a text generation model from Anthropic with a 1,000,000-token context window and native tool and MCP support.
AnthropicLarge context reasoning with tool and MCP support
Claude 4.6 Opus is a large language model developed by Anthropic, released in February 2026. It supports a context window of up to one million tokens and a maximum response size of 128,000 tokens, making it suited for tasks that involve processing or generating large volumes of text in a single session. The model accepts text and image inputs and is available as a first-party provider on MindStudio.
The model is tagged for reasoning, large context, tools, and MCP (Model Context Protocol), reflecting its design for agentic and tool-augmented workflows. It supports configurable reasoning, external tool calling, and MCP server connections as structured inputs, which makes it applicable to multi-step tasks, document analysis, and automated pipelines. Developers working on complex, long-horizon tasks that require integrating external systems or processing extensive documents are the primary intended audience.
What Claude 4.6 Opus supports
Large Context Window
Processes up to 1,000,000 tokens in a single context, enabling analysis of long documents, codebases, or extended conversation histories without truncation.
Extended Response Output
Generates responses of up to 128,000 tokens, supporting long-form content generation, detailed reports, and comprehensive code outputs in one pass.
Configurable Reasoning
Offers a selectable reasoning mode that allows users to enable or adjust the model's internal reasoning process before producing a response.
Tool Calling
Supports structured tool inputs, allowing the model to invoke external functions or APIs as part of a multi-step agentic workflow.
MCP Server Integration
Accepts Model Context Protocol (MCP) server connections as a native input type, enabling integration with MCP-compatible external data sources and services.
Image Understanding
Accepts image inputs alongside text, allowing the model to analyze, describe, or reason about visual content within a prompt.
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Get Started FreeBenchmark scores
Scores represent accuracy — the percentage of questions answered correctly on each test.
| Benchmark | What it tests | Standard | Extended Thinking |
|---|---|---|---|
| GPQA Diamond | PhD-level science questions (biology, physics, chemistry) | 84.0% | — |
| HLE | Questions that challenge frontier models across many domains | 18.6% | — |
| SciCode | Scientific research coding and numerical methods | 45.7% | — |
| SWE-bench Verified | Real GitHub issues requiring multi-file code fixes | 80.8% | — |
| Terminal-Bench 2.0 | Agentic coding and terminal command tasks | 65.4% | — |
| ARC-AGI-2 | Novel abstract reasoning and pattern recognition | 68.8% | — |
| BigLaw Bench | Legal reasoning and analysis tasks | 90.2% | — |
Common questions about Claude 4.6 Opus
What is the context window size for Claude 4.6 Opus?
Claude 4.6 Opus supports a context window of 1,000,000 tokens, meaning it can process up to one million tokens of combined input and conversation history in a single session.
What is the maximum response length?
The model can generate responses of up to 128,000 tokens in a single output, which is suitable for long-form documents, detailed analyses, and extended code generation.
Does Claude 4.6 Opus support image inputs?
Yes, Claude 4.6 Opus supports image inputs in addition to text, allowing it to process and reason about visual content provided in a prompt.
What is the pricing for Claude 4.6 Opus?
Pricing information for Claude 4.6 Opus is not published in the available metadata. You can use the model directly on MindStudio without managing separate API keys, and pricing details may be available through MindStudio's billing settings or Anthropic's official pricing page.
What kinds of workflows is Claude 4.6 Opus designed for?
Based on its tags and input types, Claude 4.6 Opus is designed for reasoning-intensive, tool-augmented, and agentic workflows. It supports tool calling and MCP server connections, making it applicable to multi-step automated pipelines, large document processing, and tasks requiring integration with external systems.
What is the knowledge cutoff date for Claude 4.6 Opus?
A specific knowledge cutoff date is not included in the available metadata for Claude 4.6 Opus. For the most accurate information, refer to Anthropic's official documentation.
What people think about Claude 4.6 Opus
Community discussion around Claude Opus 4.6 is generally positive, with the highest-engagement thread focusing on a user-created 3D VoxelBuild benchmark comparing Opus 4.6 to its predecessor, Opus 4.5, and drawing significant interest shortly after the model's release. Users in the r/singularity community appear engaged with benchmark comparisons across frontier models, including Opus 4.6's positioning on coding and reasoning evaluations.
Some threads in the community reference competing models and broader benchmark results, suggesting users are actively tracking how Opus 4.6 performs relative to other releases in the same period. A real-world coding comparison thread on r/LocalLLaMA received lower engagement, indicating that hands-on practical evaluations are discussed but attract a smaller audience than aggregate benchmark discussions.
Difference Between Opus 4.6 and Opus 4.5 On My 3D VoxelBuild Benchmark
Gemini 3.1 livebench results
Livebench just dropped their run of codex 5.3. New SOTA for agentic coding, but regression overall
I compared 8 AI coding models on the same real-world feature in an open-source TypeScript project. Here are the results
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. Opus 4.6 uses adaptive thinking mode. The model dynamically decides when and how much to think.
Controls how much the model thinks vs. how quickly it responds. Higher effort produces better quality but uses more tokens and is slower. Recommended: High for coding and agentic work; Medium for general use; Low for short, latency-sensitive tasks. Only applies when Reasoning is enabled.
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