Claude 4.1 Opus
Anthropic's most advanced model, purpose-built for complex coding, agentic tasks, and deep research requiring sustained multi-step reasoning.
Anthropic Flagship model for complex coding and agentic tasks
Claude Opus 4.1 is Anthropic's flagship text generation model, released on August 5, 2025 as an upgrade to Claude Opus 4. It is designed for demanding workflows that require sustained reasoning across long, multi-step tasks, with particular strength in software development, autonomous research, and agentic problem solving. The model supports a 200,000-token context window, up to 32,000 output tokens, and accepts both text and image inputs. It is multilingual, with documented support for French, Arabic, Mandarin, Japanese, Korean, Spanish, and Hindi.
On the SWE-bench Verified benchmark for real-world software bug fixing, Claude Opus 4.1 scores 74.5%, and it delivers a one standard deviation improvement over Opus 4 on Windsurf's junior developer benchmark for autonomous coding tasks. It supports extended thinking with up to 64,000 reasoning tokens, enabling deeper deliberation on complex problems. The model is available through the Anthropic API, Claude Code, Amazon Bedrock, and Google Cloud Vertex AI, making it suited for developers, researchers, and enterprises running complex multi-file code refactoring, long-horizon agent workflows, and in-depth research synthesis.
What Claude 4.1 Opus supports
Advanced Coding
Handles complex software engineering tasks including multi-file refactoring and bug fixing, scoring 74.5% on SWE-bench Verified.
Extended Thinking
Supports up to 64,000 tokens of extended reasoning, allowing the model to deliberate more deeply on complex, multi-step problems.
Agentic Task Execution
Runs long-horizon autonomous workflows with fewer errors, achieving a one standard deviation improvement over Opus 4 on Windsurf's junior developer benchmark.
Large Context Window
Processes up to 200,000 tokens of input context, enabling analysis of large codebases, lengthy documents, or extended conversation histories in a single pass.
Vision Input
Accepts image inputs alongside text, allowing the model to analyze diagrams, screenshots, and other visual content within a prompt.
Multilingual Support
Handles multiple languages including French, Arabic, Mandarin, Japanese, Korean, Spanish, and Hindi.
Deep Research Synthesis
Performs agentic search and detail tracking across sources such as patent databases, academic papers, and market reports to synthesize insights independently.
High Output Length
Generates responses of up to 32,000 output tokens, supporting long-form documents, detailed code files, and extended analytical reports.
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Get Started FreeCommon questions about Claude 4.1 Opus
What is the context window size for Claude Opus 4.1?
Claude Opus 4.1 supports a 200,000-token context window, with a maximum output of 32,000 tokens per response.
What is the model ID to use Claude Opus 4.1 via the API?
The model ID is claude-opus-4-1-20250805. You can find the full list of available model identifiers in Anthropic's API Model Reference documentation.
Where can I find pricing for Claude Opus 4.1?
API pricing for Claude Opus 4.1 is listed on Anthropic's pricing page at anthropic.com/pricing#api.
What is the training data cutoff for Claude Opus 4.1?
The model's training date is listed as August 2025. For precise knowledge cutoff details, refer to the System Card or API documentation provided by Anthropic.
What platforms support Claude Opus 4.1?
Claude Opus 4.1 is available through the Anthropic API, Claude Code, Amazon Bedrock, and Google Cloud Vertex AI.
Does Claude Opus 4.1 support extended thinking?
Yes. Claude Opus 4.1 supports extended thinking with up to 64,000 reasoning tokens, which allows the model to work through complex problems with deeper deliberation before producing a response.
What people think about Claude 4.1 Opus
Community discussions referencing Claude Opus 4.1 are limited among the available threads, though one thread notes it scored 60% on SimpleBench, with a later model scoring 2% higher. General sentiment in benchmark-focused communities treats it as a reference point for evaluating other models in coding and reasoning tasks.
Discussions frequently center on benchmark comparisons rather than direct usage feedback, with threads on SWE-bench, Livebench, and SimpleBench scores being common contexts where the model is mentioned. Users appear to track its coding performance closely, particularly in relation to agentic and software engineering benchmarks.
GPT-5 scores a poor 56.7% on SimpleBench, putting it at 5th place
Livebench coding numbers finally fixed
Kimi K2 Thinking scores lower than Gemini 2.5 Flash on Livebench
Claude 4.5 Opus scores 62% in SimpleBench, 2% higher than Claude 4.1 Opus.
GPT 5.1 scores lower than GPT 5.0 on livebench
Documentation & links
Parameters & options
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