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Claude 3.7 Sonnet
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About

Claude 3.7 Sonnet is a hybrid-reasoning language model built to give users a practical balance between speed and depth. It offers two operational modes: a fast standard mode for quick, high-quality replies, and an extended thinking mode that performs step-by-step reasoning, planning, and multi-perspective analysis before returning an answer. This makes it useful both for near-instant support and for tackling complex problems that need careful deliberation. The model excels at software engineering tasks — it achieves industry-leading coding benchmark results and is Anthropic’s most capable model for creative and context-aware coding. Developers can use agentic workflows via the Claude Code command-line tool to delegate substantial engineering tasks directly from the terminal. Claude 3.7 Sonnet also supports very large interactions: up to 200,000 input tokens and up to 128,000 output tokens (64K generally available, 128K in beta), letting you handle long codebases, detailed reports, and extensive research in a single session. API users get fine-grained control over how long the model ‘thinks’, helping balance response speed, depth, and cost. Practical features include batch predictions, prompt caching, function calling, and token counting. The model is widely available across Anthropic’s plans and major cloud providers (Anthropic API, Amazon Bedrock, Google Vertex AI); extended thinking is included on paid tiers. Knowledge is current through November 2024. Use Claude 3.7 Sonnet for complex coding and debugging, AI agents requiring multi-step workflows, long-form content and technical documentation, and problems that benefit from deliberate, stepwise reasoning. While Sonnet 4 improves on some capabilities, Claude 3.7 remains a powerful, flexible choice when you need strong coding performance, large-context handling, and controllable reasoning depth.

Percs

Large context
Strong coding
High accuracy
Support file upload

Settings

Diversity control (Top P)-  Filters AI responses based on probability.
Lower values = top few likely responses,
Higher values = larger pool of options.
Temperature-  The temperature of the model. Higher values make the model more creative and lower values make it more focused.
Response length-  The maximum number of tokens to generate in the output.
Context length-  The maximum number of tokens to use as input for a model.
Reasoning-  Ability to think deeper
Reasoning Tokens-  Budget for reasoning. Must be lesser than Max Tokens (length of an output)