GPT-5.4 (xhigh) vs. GPT-5.5 (medium): A Comparative Analysis

What the benchmarks show

The performance landscape between GPT-5.4 (xhigh) and GPT-5.5 (medium) reveals a nuanced distribution of capabilities. GPT-5.4 (xhigh) maintains a slight edge in general intelligence and coding proficiency, with an intelligence index of 56.8 and a coding index of 57.2, compared to the 56.7 and 56.2 scores of the GPT-5.5 (medium). In specialized benchmarks, the models trade blows: GPT-5.4 (xhigh) outperforms in HLE (0.416 vs 0.406), SciCode (0.566 vs 0.535), and IFBench (0.739 vs 0.709). However, GPT-5.5 (medium) demonstrates superior reasoning in the TAU2 benchmark (0.918 vs 0.871) and a marginally higher GPQA score (0.926 vs 0.92). Both models share identical performance in the TerminalBench Hard benchmark, suggesting that for complex terminal-based tasks, the underlying logic remains consistent regardless of the model version.

Speed and cost

Operational efficiency varies significantly between the two models. GPT-5.4 (xhigh) is the more economical option, with a blended pricing of $5.63 per million tokens, exactly half the cost of the GPT-5.5 (medium) at $11.25 per million tokens. This makes the xhigh variant a more sustainable choice for large-scale data processing or long-running background tasks. However, this cost-effectiveness comes at the expense of latency. GPT-5.4 (xhigh) exhibits a notably high time-to-first-token of 186.304 seconds, which renders it unsuitable for real-time conversational interfaces. In contrast, GPT-5.5 (medium) offers a highly responsive time-to-first-token of 3.958 seconds, though its output speed of 64.654 tokens per second is slower than the 78.88 tokens per second achieved by the xhigh model.

Which model fits which workflow

Selecting the appropriate model requires an assessment of the specific application requirements. GPT-5.4 (xhigh) is engineered for throughput. Its lower cost and higher coding index suggest it is best utilized for automated code generation, batch document analysis, and offline research tasks where the initial delay is negligible compared to the total processing time. The model’s higher output speed ensures that once the generation begins, it completes tasks more efficiently than its counterpart.

GPT-5.5 (medium) is purpose-built for interaction. The significant reduction in time-to-first-token makes it the only viable choice for user-facing applications, such as chatbots, real-time coding assistants, or any workflow requiring immediate feedback. While the higher cost per million tokens and lower coding index are trade-offs, the performance gains in TAU2 and the near-instantaneous start time provide a superior user experience for interactive sessions.

Decision takeaway

Ultimately, the distinction between these two OpenAI models is defined by the requirements of the deployment environment. If your workflow prioritizes cost-efficiency and high-volume output, GPT-5.4 (xhigh) is the clear winner. If your priority is user-facing responsiveness and low-latency interaction, the GPT-5.5 (medium) is the necessary choice despite the higher cost. Neither model holds a universal advantage, as their performance profiles are tailored to fundamentally different operational needs.