Claude 4 Sonnet
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About
Claude Sonnet 4 is a versatile, mid-size AI model that balances strong reasoning and multimodal understanding with cost-efficiency and speed. It offers two operational modes — near-instant responses for quick tasks and an extended, step-by-step mode for deeper reasoning — so you can choose fast drafts or careful multi-step problem solving. Sonnet 4 reads and reasons over text and images, interacts with on-screen content programmatically, and supports rich code generation up to 64K output tokens, making it practical for planning, debugging, refactors, and end-to-end development tasks.
Its very large context capabilities (preview support for up to 1 million tokens) let teams synthesize and analyze entire codebases, long legal or research documents, and complex multi-step workflows without losing coherence. Improved steerability lets you control tone, structure, and behavior for consistent customer-facing agents, content pipelines, or internal automation. Compared with Anthropic’s largest Opus 4 model, Sonnet 4 provides faster and more cost-effective performance for everyday enterprise and developer workflows while still improving on previous Sonnet releases in coding and reasoning quality.
Practical use cases include building advanced customer-support agents that follow nuanced instructions and recover from errors, powering software engineering assistants across the lifecycle, summarizing and extracting structured insights from massive documents, and aiding research or marketing teams with fast, high-quality outputs. Limitations: Sonnet 4 is not as powerful as Opus 4 for the most demanding reasoning tasks, and some advanced features (like the 1M token window) are in preview and may have limited availability. For most teams, Sonnet 4 delivers a strong blend of capability, speed, and affordability for real-world applications.
Percs
Multi-modal
Large context
Cost-effective
Strong coding
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.
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)