200K
3.15
15.75
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Claude 4 Sonnet

Anthropic’s Claude Sonnet 4 is a precise AI model for coding and reasoning. With a 200K-token context window, it excels in software development.
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Claude 4 Sonnet

Claude Sonnet 4 excels in coding, reasoning, and analytics. Anthropic’s model offers precise API solutions for developers and enterprises.

Claude Sonnet 4 Description

Anthropic’s Claude Sonnet 4 is an efficient AI model for coding, reasoning, and analytics. With a 200K-token context window, it offers precise solutions.

Technical Specifications

Performance Benchmarks

Claude Sonnet 4 balances efficiency and performance for coding and reasoning.

  • Context Window: 200K tokens.
  • Output Capacity: Up to 64K tokens per response.
  • Performance Benchmarks: SWE-bench: 72.7%, Terminal-bench: 35.5%.
  • API Pricing:
    • Input tokens: $3.15 per million tokens.
    • Output tokens: $15.75 per million tokens.
    • Cost for 1,000 tokens: $0.00315 (input) + $0.01575 (output) = $0.0189 total.

Performance Metrics

Sonnet 4 Metrics

Key Capabilities

Claude Sonnet 4 delivers reliable outputs for diverse workflows.

  • Advanced Coding: Excels in code reviews, bug fixes, and multi-file edits.
  • Advanced Reasoning: Strong in multi-step reasoning for analytics and problem-solving.
  • Tool Utilization: Supports function calling and JSON structuring for API automation.
  • API Features: Provides streaming and function calling for scalable applications.

Optimal Use Cases

  • Coding: Code reviews, bug fixes, and multi-file code edits.
  • Data Analysis: Processing business datasets.
  • Business Automation: Streamlining workflows with API integration.
  • Complex Problem-Solving: Tackling multi-step reasoning tasks.

Comparison with Other Models

  • Vs. Gemini 2.5 Flash: Superior coding accuracy (72.7% vs. 63.8% SWE-bench), ideal for software development.
  • Vs. OpenAI o3-mini: Stronger coding performance (72.7% vs. 69.1% SWE-bench), better for efficient coding tasks.
  • Vs. Qwen3-235B-A22B: Higher coding precision (72.7% vs. ~60% SWE-bench, estimated), optimized for code efficiency.

Code Samples

Limitations

  • No vision capabilities.
  • No fine-tuning support.
  • Limited to text-based tasks.

API Integration

Accessible via AI/ML API Documentation: available here.

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