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Anthropic's Claude 3.7 Sonnet offers real-time insight into its thought process, blending quick answers with detailed steps, while Claude Code empowers developers to automate complex tasks from their command line.
Today, Anthropic is rolling out Claude 3.7 Sonnet, their latest and most advanced AI model to date. This new iteration introduces a groundbreaking hybrid reasoning capability, allowing the model to produce both quick responses and detailed, step-by-step thinking that users can observe in real-time. Additionally, Anthropic has launched Claude Code, a command-line tool for agentic coding, which enables developers to delegate complex tasks directly from their terminal.
Claude 3.7 Sonnet stands out by integrating reasoning capabilities within the same model used for general language tasks. This is a significant departure from previous models, where reasoning was often handled by separate systems. The hybrid approach means that Claude 3.7 Sonnet can switch between quick responses and extended thinking modes seamlessly.
API users have unprecedented control over how long Claude 3.7 Sonnet can think before responding. This feature is crucial for balancing speed and quality:
Claude 3.7 Sonnet shows significant improvements in coding and front-end web development, making it a powerful tool for developers:

Claude Code is a new command-line tool that leverages Claude 3.7 Sonnet's capabilities for agentic coding. This tool allows developers to delegate substantial engineering tasks directly from their terminal:
Claude 3.7 Sonnet is now available on all Claude plans, including Free, Pro, Team, and Enterprise, as well as the Claude Developer Platform, Amazon Bedrock, and Google Cloud’s Vertex AI. The extended thinking mode is supported across all platforms except the free tier.
Claude 3.7 Sonnet represents a significant leap in hybrid reasoning capabilities, offering both speed and depth of analysis. Combined with the new Claude Code tool, it provides developers with powerful tools to enhance their productivity and tackle complex tasks more efficiently.
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Kai built ML infrastructure at a Bay Area startup before developing an obsession with transformer architectures and inference optimisation that eventually pulled him out of product work entirely. A stint at a compute research lab sharpened his instinct for what actually matters in a model release versus what is marketing. He writes from the inside — from the perspective of someone who has debugged the systems he is describing at three in the morning. He is allergic to hype and instinctively drawn to the unglamorous plumbing questions that everyone else skips over.
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