GPT-5.2 (xhigh) vs. GLM-5.1 (Reasoning): A Comparative Analysis

What the Benchmarks Show

The performance landscape between GPT-5.2 (xhigh) and GLM-5.1 (Reasoning) reveals a clear divergence in specialization. GPT-5.2 demonstrates a commanding lead in technical domains, particularly in mathematics, where it achieves a 0.99 index score and an AIME 2025 result of 0.99. Its coding capabilities are equally robust, reflected in a 0.889 score on LiveCodeBench and a 0.469 in TerminalBench Hard. These figures suggest that GPT-5.2 is optimized for high-stakes problem solving where precision is the primary constraint.

Conversely, GLM-5.1 (Reasoning) shows a different strength profile. While it trails GPT-5.2 in coding and general scientific benchmarks like SciCode (0.438 vs 0.521), it outperforms its counterpart in the TAU2 benchmark with a score of 0.976 compared to GPT-5.2’s 0.847. This indicates that while GLM-5.1 may lack the raw mathematical depth of the OpenAI model, it possesses a highly refined capacity for complex task execution and reasoning, as evidenced by its competitive IFBench score of 0.762.

Speed and Cost

Operational efficiency highlights a distinct trade-off between the two models. GPT-5.2 (xhigh) is priced at a blended rate of $4.81 per million tokens, with output costs reaching $14.00 per million. This premium pricing is paired with a notably high time-to-first-token of 68.593 seconds, which may introduce significant friction in real-time applications. While its output speed of 68.412 tokens per second is respectable, the initial latency is a critical factor for developers to consider.

GLM-5.1 (Reasoning) presents a more economical and responsive alternative. With a blended cost of $2.15 per million tokens and an output cost of only $4.40 per million, it is substantially cheaper for large-scale deployments. More importantly, its time-to-first-token is a mere 0.929 seconds. Although its output speed is lower at 54.406 tokens per second, the near-instantaneous start time makes it far better suited for interactive interfaces and conversational agents where user experience is paramount.

Which model fits which workflow

Selecting the right model requires aligning these performance characteristics with your specific project needs. GPT-5.2 (xhigh) is best utilized in asynchronous or batch-processing environments where the model has time to initialize and perform deep, multi-step calculations. It is the clear choice for researchers, engineers, and data scientists who require the highest possible ceiling for mathematical and coding tasks and can tolerate higher latency and costs.

GLM-5.1 (Reasoning) is better suited for production-grade applications that prioritize user engagement and cost management. Its low latency makes it ideal for chatbots, real-time assistants, and iterative coding tools where the user expects an immediate response. By choosing GLM-5.1, organizations can maintain a high volume of requests without the overhead associated with the more expensive and slower-starting GPT-5.2.

Decision takeaway

Ultimately, the distinction between these models is defined by the balance of latency versus depth. GPT-5.2 (xhigh) is a specialized engine for complex, high-accuracy tasks, while GLM-5.1 (Reasoning) is a versatile, high-velocity tool designed for efficiency and responsiveness. Understanding these operational trade-offs is essential for integrating the right intelligence into your specific technical stack.