Claude Sonnet 4.6 vs. GPT-5.5: A Comparative Analysis

What the Benchmarks Show

The performance gap between Claude Sonnet 4.6 and GPT-5.5 is measurable across nearly all standardized metrics. GPT-5.5 leads with an intelligence index of 60.2 compared to Sonnet 4.6’s 51.7, and a coding index of 59.1 versus 50.9. This trend continues in specialized testing: GPT-5.5 achieves a GPQA score of 0.935 against Sonnet’s 0.875, and demonstrates a significant advantage in the TAU2 benchmark, scoring 0.938 compared to 0.757.

While both models show proficiency, GPT-5.5 consistently outperforms Sonnet 4.6 in complex reasoning and technical execution. The HLE and IFBench scores further highlight this disparity, with GPT-5.5 scoring 0.443 and 0.758 respectively, while Sonnet 4.6 trails at 0.3 and 0.565. These benchmarks suggest that for tasks requiring high-level logical synthesis or intricate instruction following, GPT-5.5 provides a more robust foundation.

Speed and Cost

Operational efficiency is a critical trade-off when choosing between these models. GPT-5.5 is the more expensive option, with a blended cost of $11.25 per million tokens, nearly double the $6.56 blended cost of Claude Sonnet 4.6. Specifically, the output cost for GPT-5.5 is $30.00 per million tokens, compared to $15.00 for Sonnet 4.6. Organizations scaling high-volume applications will find the pricing difference significant over time.

In terms of raw performance, the models are remarkably similar in output speed. GPT-5.5 generates text at 68.227 tokens per second, while Sonnet 4.6 follows closely at 67.675 tokens per second. However, GPT-5.5 offers a faster time to first token at 47.763 seconds, compared to the 53.09 seconds required by Sonnet 4.6. While the output speed is negligible, the faster initial response time of GPT-5.5 may provide a more responsive feel for interactive applications.

Which model fits which workflow

Choosing between these models requires balancing the need for raw capability against budgetary constraints. GPT-5.5 is designed for workflows that demand maximum accuracy and complex problem-solving. Its superior performance in coding and logic-heavy benchmarks makes it the preferred tool for software engineering, complex data analysis, and tasks where error margins must be minimized. The higher cost is effectively a premium paid for increased reliability and depth of reasoning.

Conversely, Claude Sonnet 4.6 is optimized for high-throughput environments where cost-efficiency is paramount. It provides a highly capable reasoning engine that is more than sufficient for standard content generation, routine coding assistance, and general-purpose queries. For teams that require large-scale deployment or frequent model interaction, the lower blended price point of Sonnet 4.6 allows for greater volume without a proportional increase in operational expenditure.

Decision takeaway

Both models represent the current state of the art, yet they serve different operational needs. GPT-5.5 is the high-performance choice for users who require the highest possible intelligence and coding proficiency. Claude Sonnet 4.6 serves as a balanced, cost-effective alternative that maintains high utility for a wide range of standard tasks. Users should assess their specific project requirements—specifically the tolerance for cost versus the necessity for peak benchmark performance—before committing to a long-term integration.