Claude 3.7 Sonnet vs GPT-6 Astra
A direct, empirical evaluation of Claude 3.7 Sonnet (Anthropic) against GPT-6 Astra (OpenAI).
Claude 3.7 Sonnet
Developed by Anthropic · Proprietary parameters
Enterprise software engineering, full-repo refactoring, and agentic workflows.
GPT-6 Astra
Developed by OpenAI · Proprietary Frontier parameters
Complex multi-step engineering, formal math proofs, autonomous computer/browser navigation, and CAD synthesis.
| Metric | Claude 3.7 Sonnet | GPT-6 Astra |
|---|---|---|
| SWE-bench Verified | 70.3% | 79.4% |
| MMLU-Pro | 82.1% | 91.2% |
| MATH-500 | 96.2% | 98.6% |
| Context Window | 200,000 tokens | 1,050,000 tokens |
| Input Pricing (per 1M) | $3.00 | $10.00 |
| Output Pricing (per 1M) | $15.00 | $50.00 |
Architectural & Practical Deployment Analysis
Evaluating Claude 3.7 Sonnet (Anthropic) alongside GPT-6 Astra (OpenAI) highlights key technical trade-offs across neural parameter scale and compute efficiency. Claude 3.7 Sonnet executes across Proprietary total parameters with Proprietary active per token, whereas GPT-6 Astra operates on Proprietary Frontier parameters with Dynamic Recurrent MoE active per token.
On SWE-bench Verified software engineering tasks, Claude 3.7 Sonnet achieves 70.3% autonomous resolution rate, compared to 79.4% for GPT-6 Astra. On complex reasoning benchmarks, Claude 3.7 Sonnet posts 82.1% on MMLU-Pro and 96.2% on MATH-500, against 91.2% and 98.6% respectively for GPT-6 Astra.
From an operational economics perspective, processing 10,000,000 mixed tokens (7M input / 3M output) costs approximately $66.00 on Claude 3.7 Sonnet versus $220.00 on GPT-6 Astra.
Recommendation: Deploy Claude 3.7 Sonnet when your infrastructure prioritizes enterprise software engineering, full-repo refactoring, and agentic workflows.. Deploy GPT-6 Astra when workload requirements favor complex multi-step engineering, formal math proofs, autonomous computer/browser navigation, and cad synthesis..