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

What the benchmarks show

Both models, released on April 23, 2026, represent different tiers of the same architecture. When analyzing the benchmark data, GPT-5.5 (xhigh) consistently outperforms the (low) variant across all measured metrics. The (xhigh) model achieves an intelligence index of 60.2 compared to 50.8, and a coding index of 59.1 versus 52.1. This trend continues in specialized testing: the (xhigh) model scores 0.935 on GPQA and 0.938 on TAU2, notably higher than the (low) model’s 0.91 and 0.839, respectively. While both models lack a defined math index, the (xhigh) variant demonstrates a clear advantage in instruction following and technical reasoning, evidenced by its 0.758 score on IFBench compared to the 0.643 achieved by the (low) model.

Speed and cost

Interestingly, the pricing structure for both models is identical. Users pay $5.00 per 1M input tokens and $30.00 per 1M output tokens, resulting in a blended cost of $11.25 per 1M tokens regardless of the model tier selected. This removes financial considerations from the decision-making process, allowing users to focus entirely on performance characteristics.

Performance, however, reveals a stark trade-off. GPT-5.5 (low) is optimized for speed, delivering an output rate of 63.516 tokens per second with a time-to-first-token (TTFT) of only 1.542 seconds. In contrast, GPT-5.5 (xhigh) is slightly faster in raw output speed at 68.227 tokens per second, but it suffers from a significant latency penalty. The TTFT for the (xhigh) model is 47.763 seconds, making it unsuitable for applications requiring immediate feedback.

Which model fits which workflow

Determining the appropriate model requires an assessment of your specific operational needs. The (low) variant is designed for high-frequency, interactive environments where the user expects a fluid, conversational experience. Because the TTFT is negligible, it is the superior choice for chatbots, real-time assistance, or any interface where waiting nearly a minute for a response would disrupt the user experience.

Conversely, the (xhigh) variant is built for heavy-duty computational tasks. The substantial delay in receiving the first token suggests a more intensive internal reasoning process. This model is best suited for asynchronous workflows, such as batch processing large codebases, complex data analysis, or generating detailed reports where the quality of the reasoning is more important than the speed of the initial response. Users should treat the (xhigh) model as a tool for "deep work" rather than real-time interaction.

Decision takeaway

Ultimately, the distinction between these two models is a classic trade-off between latency and depth. OpenAI has provided a uniform pricing model that encourages users to select the tool that best fits their technical requirements rather than their budget. By weighing the 46-second latency gap against the measurable improvements in intelligence and coding accuracy, developers can align their choice with the specific demands of their application architecture.