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

What the benchmarks show

When evaluating the raw performance metrics, GPT-5.5 (xhigh) consistently outperforms Claude Opus 4.7 across the board. OpenAI’s model boasts an intelligence index of 60.2 compared to Anthropic’s 51.8, and a coding index of 59.1 against 53.1. These figures are reflected in specialized testing, where GPT-5.5 achieves a 0.935 score on GPQA and a 0.938 on TAU2, significantly outpacing the 0.885 and 0.739 scores recorded by Claude Opus 4.7.

However, benchmarks only tell part of the story. Claude Opus 4.7 remains competitive in specific domains, maintaining a respectable 0.501 in SciCode and 0.67 in LCR. While GPT-5.5 shows a clear lead in complex reasoning and instruction following—evidenced by its 0.758 IFBench score versus Opus 4.7’s 0.436—the gap suggests that GPT-5.5 is optimized for high-complexity, multi-step problem solving, whereas Opus 4.7 maintains a more balanced, albeit less powerful, profile.

Speed and cost

The most striking divergence between these two models is found in their operational performance. Claude Opus 4.7 is built for speed, delivering a time-to-first-token of just 1.338 seconds, compared to the 47.763 seconds required by GPT-5.5. This makes Opus 4.7 vastly more suitable for conversational interfaces where user experience is tied to immediate responsiveness. While GPT-5.5 offers a higher output speed of 68.227 tokens per second once the generation begins, the initial delay makes it unsuitable for real-time applications.

From a cost perspective, the models are surprisingly close. Claude Opus 4.7 has a blended cost of $10.94 per million tokens, while GPT-5.5 sits slightly higher at $11.25 per million tokens. While GPT-5.5 is cheaper on input ($5.00 vs $6.25), its significantly higher output cost of $30.00 per million tokens—compared to Opus 4.7’s $25.00—means that users generating long-form content or code will likely find Opus 4.7 more economical over time.

Which model fits which workflow

Selecting between these models requires balancing the need for raw cognitive power against the necessity of operational agility. GPT-5.5 is the clear choice for batch processing, complex data analysis, and difficult coding tasks where the model has time to "think" before providing a high-accuracy result. Its superior scores in TerminalBench Hard and HLE indicate that it handles technical, multi-layered instructions with greater reliability than its counterpart.

Claude Opus 4.7, by contrast, is a high-effort model designed for fluid, low-latency environments. Its architecture is optimized for scenarios where the user cannot afford to wait nearly a minute for a response. By prioritizing a near-instant time-to-first-token, Anthropic has positioned this model for customer-facing tools, interactive coding assistants, and real-time data synthesis where the speed of the delivery is as important as the content of the answer.

Decision takeaway

Ultimately, the choice comes down to the specific constraints of your project. If you are building a system that requires the absolute highest level of reasoning and instruction adherence, GPT-5.5 is the current industry leader. If your priority is a snappy, responsive user experience that keeps costs predictable during high-output generation, Claude Opus 4.7 provides a more efficient and user-friendly alternative.