Claude Sonnet 4.6 (Non-reasoning, High Effort) vs Claude Sonnet 4.6 (Adaptive Reasoning, Max Effort)
Comparing 2 AI models · 2 benchmarks · Anthropic
Composite Indices
Intelligence, Coding, Math
Standard Benchmarks
Academic and industry benchmarks
Benchmark Winners
Claude Sonnet 4.6 (Non-reasoning, High Effort)
No clear wins
Claude Sonnet 4.6 (Adaptive Reasoning, Max Effort)
- GPQA
- HLE
| Metric | An Claude Sonnet 4.6 (Non-reasoning, High Effort) | An Claude Sonnet 4.6 (Adaptive Reasoning, Max Effort) |
|---|---|---|
| Pricing Per 1M tokens | ||
| Input Cost | $3.00/1M | $3.00/1M |
| Output Cost | $15.00/1M | $15.00/1M |
| Blended Cost 3:1 input/output ratio | $6.00/1M | $6.00/1M |
| Specifications | ||
| Organization Model creator | Anthropic | Anthropic |
| Release Date Launch date | Feb 17, 2026 | Feb 17, 2026 |
| Performance & Speed | ||
| Throughput Output speed | 55.4 tok/s | 58.3 tok/s |
| Time to First Token (TTFT) Initial response delay | 756ms | 684ms |
| Latency Time to first answer token | 756ms | 34975ms |
| Composite Indices | ||
| Intelligence Index Overall reasoning capability | 44.3 | 51.3 |
| Coding Index Programming ability | 46.4 | 50.9 |
| Math Index Mathematical reasoning | — | — |
| Standard Benchmarks | ||
| GPQA Graduate-level reasoning | 79.9% | 87.5% |
| MMLU Pro Advanced knowledge | — | — |
| HLE Hard language evaluation | 13.2% | 30.0% |
| LiveCodeBench Real-world coding tasks | — | — |
| MATH 500 Mathematical problems | — | — |
| AIME 2025 Advanced math competition | — | — |
| AIME (Original) Math olympiad problems | — | — |
| SciCode Scientific code generation | 46.9% | 46.8% |
| LCR Code review capability | 57.7% | 70.7% |
| IFBench Instruction-following | 41.2% | 56.6% |
| TAU-bench v2 Tool use & agentic tasks | 79.5% | 75.7% |
| TerminalBench Hard CLI command generation | 46.2% | 53.0% |
Key Takeaways
Claude Sonnet 4.6 (Non-reasoning, High Effort) offers the best value at $3.00/1M, making it ideal for high-volume applications and cost-conscious projects.
Claude Sonnet 4.6 (Adaptive Reasoning, Max Effort) leads in reasoning capabilities with a 87.5% GPQA score, excelling at complex analytical tasks and problem-solving.
Claude Sonnet 4.6 (Adaptive Reasoning, Max Effort) achieves a 50.9 coding index, making it the top choice for software development and code generation tasks.
All models support context windows of ∞+ tokens, suitable for processing lengthy documents and maintaining extended conversations.
When to Choose Each Model
Claude Sonnet 4.6 (Non-reasoning, High Effort)
- Cost-sensitive applications
- High-volume processing
Claude Sonnet 4.6 (Adaptive Reasoning, Max Effort)
- Complex reasoning tasks
- Research & analysis
- Code generation
- Software development
Try Models for Free
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Cost Calculator
Claude Sonnet 4.6 (Non-reasoning, High Effort) Claude Sonnet 4.6 (Adaptive Reasoning, Max Effort) Costs are estimates based on API pricing. Actual costs may vary based on caching, batch processing, and volume discounts.
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AI Model Comparison Guide
Compare large language models (LLMs) side-by-side with detailed benchmark scores, pricing, and performance metrics. Our interactive comparison tool helps you evaluate AI models from OpenAI, Anthropic, Google, Meta, DeepSeek, and other leading providers. Use our AI model leaderboard to discover more models to compare.
Understanding Composite Indices
- Intelligence Index: Aggregated score combining MMLU-Pro, GPQA, and HLE benchmarks - measures overall reasoning and knowledge capabilities
- Coding Index: Composite metric from LiveCodeBench, SciCode, and LiveCodeBench Review - evaluates programming proficiency across multiple languages
- Math Index: Combined score from AIME, AIME 2025, and MATH-500 benchmarks - assesses mathematical reasoning from high school to competition level
Key Comparison Metrics
- Benchmark Scores: Standardized tests measuring intelligence, coding, math, and specialized capabilities - higher percentages indicate better performance
- Pricing Analysis: Compare input and output token costs across models - critical for budgeting API usage and scaling applications
- Performance Metrics: Throughput (tokens/second) and latency measurements for real-time application planning
- Context Windows: Maximum token capacity for processing documents and maintaining conversation history
How to Compare AI Models Effectively
Performance vs Cost
Balance benchmark scores against token pricing - flagship models offer 10-15% better performance but cost 5-10x more than smaller alternatives
Task-Specific Selection
Prioritize relevant indices: coding index for development tasks, math index for STEM applications, intelligence index for general reasoning
Real-World Testing
Use our free AI chat interface to test models with your specific prompts before committing to API integration
All benchmark scores, pricing data, and performance metrics are sourced from Artificial Analysis and updated daily. Compare models by intelligence, coding ability, math performance, speed, cost, or release date using our comprehensive AI model leaderboard.