anthropic--claude-4-sonnet

Claude Sonnet 4 is Anthropic's mid-tier hybrid-reasoning model in the Claude family, designed to flex between quick conversational replies and a visible, step-by-step extended thinking mode. The sources describe it as a multimodal language model that accepts text, images, and PDF inputs while producing text outputs, with a large context window that supports working over long documents and sizable codebases. Anthropic positions it as the workhorse sibling beneath Claude 4 Opus, keeping Opus's analytical depth in reserve for the heaviest tasks while aiming Sonnet at day-to-day reasoning, coding assistance, and document understanding. The model retains Anthropic's standard interface for system prompts, temperature control, and tool calling, and it is made available across multiple cloud platforms rather than only through a first-party endpoint, which broadens how teams can integrate it into existing enterprise pipelines. The release emphasizes practical gains over Claude 3.7 Sonnet: better coding ability, stronger general reasoning, and more faithful instruction following, paired with a substantial reduction in reward hacking behavior during training. Source documentation reports a state-of-the-art 72.7% score on the SWE-bench coding benchmark, low hallucination rates over long contexts, and multimodal strengths such as extracting information from charts and diagrams. Extended thinking can be toggled on with its own token budget, allowing the model to spend more compute on multi-step problems before answering. Pricing remains at the same level as its predecessor for input and output tokens, reflecting Anthropic's intent to keep Sonnet the cost-effective default for production deployments that need reliable reasoning, coding help, and grounded document analysis at scale.

Claude Sonnet 4 builds on Anthropic's hybrid-reasoning lineage that began with Sonnet 3.7, refining the same architecture to improve coding quality, reasoning depth, and instruction accuracy. Sources describe a state-of-the-art 72.7% result on the SWE-bench coding benchmark, alongside reductions in reward hacking during training of up to roughly eightfold compared with the prior generation, signaling tighter post-training alignment rather than just a base-model swap. The model is not open-weight and is served through hosted APIs and cloud partners, with extended thinking activated per request up to its thinking-token ceiling, letting teams balance latency against analytical depth. In practice, Sonnet 4 is well suited to agentic coding workflows, long-context document and codebase analysis, and multimodal tasks like chart and diagram extraction. Its large context window and low hallucination rate make it a strong fit for retrieval-heavy pipelines, multi-file software tasks, and structured reasoning where step-by-step traces improve trust. For forward-looking deployments, the hybrid reasoning design and tool-calling support suggest a model meant to anchor production assistants and developer copilots that occasionally need deeper deliberation without leaving the Sonnet tier for the more expensive Opus model.