GPT-5.5 (low) vs. Claude Opus 4.7: A Comparative Analysis

What the benchmarks show

When evaluating the raw performance metrics of these two models, a clear trade-off emerges between general intelligence and specific task execution. Claude Opus 4.7 leads in the broader Intelligence index with a score of 57.3 compared to GPT-5.5’s 50.8. This advantage carries over into the Coding index (52.5 vs 52.1) and the TAU2 benchmark (0.886 vs 0.839), suggesting that the Claude model is better equipped for complex, multi-step logical reasoning and high-level programming challenges.

Conversely, GPT-5.5 (low) demonstrates higher proficiency in instruction following, as evidenced by its IFBench score of 0.644 compared to Claude’s 0.586. While both models perform nearly identically on the GPQA benchmark—a key indicator of expert-level knowledge—the GPT-5.5 model shows a slight edge in TerminalBench Hard, indicating a more robust capability for handling command-line environments and system-level tasks. Neither model provides data for the Math index, leaving a gap in their comparative assessment for purely quantitative reasoning.

Speed and cost

Operational efficiency is where these models diverge most sharply. GPT-5.5 (low) is optimized for speed, delivering an output rate of 63.516 tokens per second with a remarkably low time-to-first-token of 1.542 seconds. In contrast, Claude Opus 4.7 is significantly slower, producing 48.002 tokens per second and requiring 21.112 seconds to generate the first token. This latency difference makes GPT-5.5 the clear winner for real-time chat interfaces or dynamic user-facing applications.

From a cost perspective, the models are surprisingly competitive. GPT-5.5 (low) carries a blended cost of $11.25 per million tokens, while Claude Opus 4.7 is slightly more economical at $10.94 per million tokens. While GPT-5.5 is cheaper on input ($5.00 vs $6.25), Claude Opus 4.7 offers a lower output cost ($25.00 vs $30.00). Organizations with heavy output-heavy workloads may find the Claude model more cost-effective over time, provided the latency overhead is acceptable for their specific use case.

Which model fits which workflow

Selecting the right model requires an honest assessment of your project’s latency requirements versus its reasoning complexity. GPT-5.5 (low) is engineered for high-velocity environments. Its rapid time-to-first-token ensures that users do not experience the "hang" often associated with large-scale models. This makes it ideal for customer support bots, real-time code completion tools, and any application where the user expects an instantaneous response.

Claude Opus 4.7 is designed for the "Max Effort" approach. Because it prioritizes deep reasoning and higher-order intelligence, it is best utilized in background processes where the model has time to "think." This includes complex data analysis, long-form document synthesis, and architectural code planning, where the extra 20 seconds of initial wait time is negligible compared to the benefit of a more accurate or logically sound output.

Decision takeaway

Ultimately, the comparison between GPT-5.5 (low) and Claude Opus 4.7 is a study in optimization. OpenAI has prioritized the user experience through speed, while Anthropic has pushed the boundaries of the model's reasoning capacity. Neither model is objectively "better"; rather, they serve different stages of the development and production lifecycle. By matching your specific needs—whether that be the immediate responsiveness of GPT-5.5 or the deep analytical rigor of Claude Opus—you can ensure your infrastructure is aligned with your operational goals.