GPT-5.5 (xhigh) vs. GPT-5.5 (high): Performance and Efficiency Trade-offs

What the benchmarks show

Both the GPT-5.5 (xhigh) and GPT-5.5 (high) models, released on April 23, 2026, demonstrate high-level capabilities across standardized testing. The (xhigh) variant holds a slight advantage in the Intelligence Index at 60.2 compared to 58.9 for the (high) variant. This trend persists across most benchmarks, with the (xhigh) model scoring 0.935 on GPQA and 0.561 on SciCode, slightly outperforming the (high) model's 0.932 and 0.559, respectively. While the differences are marginal, the (xhigh) model consistently maintains a lead in complex tasks like IFBench and TAU2. Neither model has a reported Math index, meaning users must rely on the provided coding and general intelligence metrics to infer performance for quantitative tasks.

Speed and cost

From a financial perspective, the two models are identical. Both charge $5.00 per 1M input tokens and $30.00 per 1M output tokens, resulting in a blended cost of $11.25 per 1M tokens. Because the cost is uniform, the decision-making process is entirely decoupled from budget considerations and focused strictly on performance metrics.

There is, however, a distinct divergence in operational speed. The (xhigh) model produces output at a rate of 68.227 tokens per second, which is faster than the (high) model’s 61.555 tokens per second. However, this throughput advantage is offset by a substantial difference in latency. The (high) model features a time-to-first-token of 18.517 seconds, whereas the (xhigh) model takes 47.763 seconds to begin generating output. This suggests that while the (xhigh) model is more efficient at sustained generation, the (high) model is significantly more responsive for interactive or conversational applications.

Which model fits which workflow

Selecting the correct model requires an assessment of your specific operational constraints. The GPT-5.5 (xhigh) is designed for heavy-duty, asynchronous tasks. Because of its higher intelligence and coding indices, it is better suited for complex software development, deep research, or data analysis where the user can afford to wait nearly 48 seconds for the process to initiate. The slight increase in accuracy across benchmarks like TerminalBench Hard and TAU2 makes it the preferred choice for high-stakes reasoning.

In contrast, the GPT-5.5 (high) is optimized for workflows that prioritize user experience and responsiveness. The significantly lower time-to-first-token makes it far more suitable for chat interfaces, real-time assistance, or any application where a long delay would disrupt the user's workflow. While it sacrifices a small margin of intelligence and coding capability, it provides a much more fluid interaction cycle.

Decision takeaway

Ultimately, OpenAI has provided two tiers of the same model that cater to different temporal requirements. The (xhigh) model is a precision instrument for complex, non-interactive tasks, while the (high) model serves as a responsive tool for active, iterative work. By evaluating whether your project prioritizes raw reasoning power or immediate responsiveness, you can effectively leverage the strengths of these two variants without incurring additional costs.