Anthropic’s Transparency Hub \ Anthropic

Anthropic’s Transparency Hub A look at Anthropic's key processes, programs, and practices for responsible AI development. 0 1 Model Report 0 2 System Trust and Reporting 0 3 Voluntary Commitments 0 1 Model Report 0 1 Model Report 0 2 System Trust and Reporting 0 3 Voluntary Commitments Model Report Last updated February 20, 2026 Select a model to see a summary that provides quick access to essential information about Claude models, condensing key details about the models' capabilities, safety evaluations, and deployment safeguards. We've distilled comprehensive technical assessments into accessible highlights to provide clear understanding of how the models function, what they can do, and how we're addressing potential risks. Claude Sonnet 4.6 Claude Opus 4.6 Claude Opus 4.5 Claude Haiku 4.5 Claude Sonnet 4.5 Claude Opus 4 and Sonnet 4 Claude Opus 4.1 Claude Sonnet 3.7 Claude Sonnet 4.6 Summary Table Model description Claude Sonnet 4.6 our most capable Sonnet model. It’s a full upgrade of the model’s skills across coding, computer use, long-context reasoning, agent planning, knowledge work, and design Benchmarked Capabilities See our Claude Sonnet 4.6 system card ’s Section 2 on capabilities Acceptable Uses See our Usage Policy Release date February 2026 Access Surfaces Claude Sonnet 4.6 can be accessed through: Claude.ai The Anthropic API Amazon Bedrock Google Vertex AI Microsoft Azure AI Foundry Software Integration Guidance See our Developer Documentation Modalities Claude Sonnet 4.6 can understand both text (including voice dictation) and image inputs, engaging in conversation, analysis, coding, and creative tasks. Claude can output text, including text-based artifacts, diagrams, and audio via text-to-speech. Knowledge Cutoff Date Claude Sonnet 4.6 has a knowledge cutoff date of May 2025. This means the models’ knowledge base is most extensive and reliable on information and events up to May 2025. Software and Hardware Used in Development Cloud computing resources from Amazon Web Services and Google Cloud Platform, supported by development frameworks including PyTorch, JAX, and Triton. Model architecture and training methodology Claude Sonnet 4.6 was pretrained on large, diverse datasets to acquire language capabilities. To elicit helpful, honest, and harmless responses, we used a variety of techniques including reinforcement from AI feedback, and the training of selected character traits highlighted in Claude’s Constitution . Training Data Claude Sonnet 4.6 was trained on a proprietary mix of publicly available information on the Internet as of May 2025, as well as non-public data from third parties, data provided by data-labeling services and paid contractors, data from Claude users who have opted in to have their data used for training, and data we generated internally at Anthropic. Testing Methods and Results Based on our assessments, we have decided to deploy Claude Sonnet 4.6 under the ASL-3 Standard. See below for s

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