GPT-5.5 (low) vs. GPT-5.2 (xhigh): A Comparative Analysis

What the benchmarks show

Evaluating the performance of GPT-5.5 (low) and GPT-5.2 (xhigh) reveals a nuanced landscape of capabilities. GPT-5.2 (xhigh) demonstrates a clear advantage in specialized domains, particularly in mathematics, where it achieves a score of 99 and an AIME 2025 score of 0.99. Its Intelligence index of 51.3 slightly edges out the 50.8 seen in GPT-5.5 (low). Furthermore, GPT-5.2 (xhigh) shows stronger performance in instruction following (IFBench: 0.754) and logical reasoning (LCR: 0.727).

Conversely, GPT-5.5 (low) maintains a competitive edge in coding tasks, with a Coding index of 52.1 compared to 48.7 for the xhigh variant. While GPT-5.5 (low) performs well across several metrics, such as a 0.91 score on GPQA and a 0.839 score on TAU2, it generally trails the xhigh model in broader reasoning benchmarks. Users prioritizing pure mathematical rigor or complex multi-step logical tasks will find the xhigh variant more robust, whereas those focused on software development workflows may find the coding-specific optimizations of the low variant more advantageous.

Speed and cost

Economic and operational efficiency differ significantly between these two iterations. GPT-5.2 (xhigh) is substantially more cost-effective, with a blended price of $4.81 per 1M tokens, compared to $11.25 per 1M tokens for GPT-5.5 (low). If your workflow involves high-volume processing, the xhigh model offers a clear financial benefit.

However, the operational performance profiles present a stark contrast. GPT-5.5 (low) is engineered for rapid interaction, boasting a time-to-first-token of 1.542 seconds. In comparison, GPT-5.2 (xhigh) suffers from a significant latency issue, with a time-to-first-token of 68.593 seconds. While GPT-5.2 (xhigh) maintains a slightly higher output speed of 68.412 tokens per second compared to 63.516 tokens per second for the low variant, the initial delay in the xhigh model makes it unsuitable for real-time, conversational, or latency-sensitive applications.

Which model fits which workflow

Determining the correct model requires balancing the need for immediate feedback against the requirement for deep reasoning. GPT-5.5 (low) is optimized for environments where the user experience depends on near-instantaneous responses. Its faster initiation time makes it ideal for chat interfaces, real-time coding assistants, and interactive tools where a 68-second wait time would be prohibitive.

GPT-5.2 (xhigh) is better suited for batch processing, background analytical tasks, and complex mathematical modeling. Because it is both cheaper and more capable in high-level reasoning, it is the logical choice for non-interactive workloads where the model can process large datasets or complex queries without requiring an immediate, low-latency response from the end user.

Decision takeaway

Ultimately, these models serve different operational niches. GPT-5.5 (low) is a specialized tool for high-frequency, low-latency tasks, sacrificing some mathematical depth and cost efficiency for speed. GPT-5.2 (xhigh) is a powerhouse for intensive reasoning and cost-conscious, high-volume production, provided the application can tolerate its significant initial latency. Aligning your choice with your specific latency tolerance and budget will ensure optimal performance.