OpenAI GPT-5.5: Medium vs. XHigh Performance Analysis

Understanding the Benchmark Landscape

Both the Medium and XHigh versions of GPT-5.5 represent the latest iteration of OpenAI’s architecture, released simultaneously on April 23, 2026. When evaluating their performance, the data reveals a consistent, albeit incremental, advantage for the XHigh model. The XHigh variant achieves an intelligence index of 60.2 compared to the Medium’s 56.7, and a coding index of 59.1 versus 56.2. This trend holds across standardized testing: the XHigh model scores higher in GPQA (0.935 vs 0.926), HLE (0.443 vs 0.406), and SciCode (0.561 vs 0.535). While the Math index remains unknown for both, the performance delta in logical reasoning and instruction following—as evidenced by the IFBench scores of 0.758 for XHigh and 0.709 for Medium—suggests that the XHigh model is better equipped for nuanced, multi-step problem solving.

Speed and Cost Trade-offs

Perhaps the most striking divergence between these two models is not in their intelligence, but in their operational efficiency. Both models share an identical pricing structure, costing $5.00 per 1M input tokens and $30.00 per 1M output tokens, resulting in a blended rate of $11.25 per 1M tokens. Despite this parity in cost, their performance profiles are vastly different. The Medium model is optimized for speed, delivering an output rate of 64.654 tokens per second with a highly responsive time-to-first-token of just 3.958 seconds.

In contrast, the XHigh model is significantly slower to initiate, with a time-to-first-token of 47.763 seconds. While the XHigh model does boast a slightly higher output speed of 68.227 tokens per second once generation begins, the initial latency penalty is substantial. Users must decide if the marginal gains in reasoning accuracy provided by the XHigh model are worth the nearly 44-second delay in response initiation compared to the Medium model.

Aligning Workflows to Model Capabilities

Selecting the correct model requires an honest assessment of your specific application requirements. The Medium model is engineered for interactive environments where user experience is tied to low latency. Its ability to begin generating responses almost immediately makes it suitable for chat interfaces, real-time coding assistants, and rapid prototyping. The performance metrics suggest that for tasks requiring high throughput and quick turnarounds, the Medium model provides a more fluid experience without sacrificing significant capability.

Conversely, the XHigh model is designed for deep-work scenarios. Its superior scores in TerminalBench Hard (0.606 vs 0.575) and TAU2 (0.938 vs 0.918) indicate that it is better suited for complex, long-form reasoning tasks, automated agentic workflows, or batch processing where the system can afford to wait for a more accurate, high-fidelity output. In these contexts, the initial latency is a negligible cost compared to the value of the increased reasoning depth provided by the XHigh architecture.