Claude-Sonnet-4 API | AIMLAPI (original) (raw)
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.
Claude-Sonnet-4
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: $3.9 per million tokens.
- Output tokens: $19.5 per million tokens.
- Cost for 1,000 tokens: 0.00315(input)+0.00315 (input) + 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.
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: $3.9 per million tokens.
- Output tokens: $19.5 per million tokens.
- Cost for 1,000 tokens: 0.00315(input)+0.00315 (input) + 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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