GPT-5.2 (xhigh) vs. Claude Opus 4.7: A Comparative Analysis

What the Benchmarks Show

The performance landscape reveals a clear divergence in specialization between GPT-5.2 (xhigh) and Claude Opus 4.7. GPT-5.2 exhibits exceptional mathematical aptitude, evidenced by its 0.99 score on the AIME 2025 benchmark, a metric for which data is unavailable for the Claude model. In contrast, Claude Opus 4.7 leads in general intelligence and complex reasoning, reflected in its higher Intelligence index of 57.3 compared to GPT-5.2’s 51.3.

Domain-specific benchmarks further highlight these differences. Claude Opus 4.7 outperforms GPT-5.2 in HLE (0.396 vs 0.354), SciCode (0.545 vs 0.521), and TerminalBench Hard (0.515 vs 0.469), suggesting that the newer model is better optimized for technical, multi-step reasoning and terminal-based environments. However, GPT-5.2 maintains a stronger showing in instruction following, with an IFBench score of 0.754 compared to Claude’s 0.586, indicating that it may be more reliable for tasks requiring strict adherence to complex formatting or procedural constraints.

Speed and Cost

Economic and latency considerations present a significant trade-off. GPT-5.2 is substantially more affordable, with a blended cost of $4.81 per million tokens, less than half of Claude Opus 4.7’s $10.94 per million tokens. This makes GPT-5.2 the more viable option for high-volume, long-running processes where cost efficiency is a primary driver.

However, the models behave differently under load. Claude Opus 4.7 provides a much faster time-to-first-token at 21.112 seconds, compared to the 68.593 seconds required by GPT-5.2. While GPT-5.2 achieves a higher output speed of 68.412 tokens per second versus Claude’s 48.002, the initial delay for GPT-5.2 may be prohibitive for interactive applications where immediate responsiveness is required. Users must decide whether they prioritize the lower per-token cost of the OpenAI model or the lower latency of the Anthropic model.

Which model fits which workflow

Selecting the appropriate model requires aligning these technical profiles with specific project requirements. GPT-5.2 is optimized for workflows that demand high-precision mathematical output and consistent instruction following. Its cost structure supports large-scale data processing and repetitive tasks where the initial latency penalty is offset by the sustained high-speed output and lower price point. It is an ideal candidate for backend automation and scientific research where mathematical accuracy is the primary success metric.

Claude Opus 4.7 is better suited for iterative, high-reasoning tasks where the model must navigate complex, multi-step environments. Its superior performance in TerminalBench and general intelligence indices makes it a robust partner for software engineering and complex problem-solving. While the higher cost and slower token generation speed are notable, the reduced time-to-first-token makes it a more effective tool for real-time human-in-the-loop collaboration, where waiting over a minute for an initial response is not feasible.

Decision takeaway

Ultimately, the comparison between GPT-5.2 and Claude Opus 4.7 is a study in trade-offs. OpenAI has delivered a model that excels in mathematical rigor and cost-effective scaling, whereas Anthropic has prioritized reasoning depth and responsiveness. Organizations should audit their specific latency requirements and budget constraints before committing to one architecture, as the performance delta in reasoning tasks versus mathematical tasks is significant enough to impact project outcomes.