GLM-5.1 vs. GPT-5.5: Evaluating Reasoning and Performance

What the Benchmarks Show

When comparing the performance metrics of GLM-5.1 and GPT-5.5, a clear divide emerges in specialized capability. GPT-5.5 (xhigh) consistently outperforms GLM-5.1 (Reasoning) across most standardized benchmarks, reflecting its higher intelligence index of 60.2 compared to GLM-5.1’s 51.4. In coding, GPT-5.5 achieves a 59.1 index, significantly higher than GLM-5.1’s 43.4. This trend continues in technical benchmarks like HLE (0.443 vs 0.28) and TerminalBench Hard (0.606 vs 0.432), suggesting that GPT-5.5 is better equipped for complex software engineering and high-level reasoning tasks.

However, GLM-5.1 holds its own in specific areas. It demonstrates a slight edge in IFBench (0.763 vs 0.759) and notably outperforms GPT-5.5 in the TAU2 benchmark (0.977 vs 0.939). These results indicate that while GPT-5.5 is the more powerful generalist, GLM-5.1 remains highly competitive in instruction following and specific reasoning tasks, proving that a lower intelligence index does not equate to a total lack of utility in specialized workflows.

Speed and Cost

Operational efficiency reveals a stark contrast between the two models. GLM-5.1 is designed for high-velocity applications, boasting a time-to-first-token of just 0.929 seconds. This makes it exceptionally responsive for interactive chat interfaces or real-time systems. GPT-5.5, by contrast, suffers from a significant initial delay, with a time-to-first-token of 47.763 seconds. While GPT-5.5 maintains a faster output speed of 68.227 tokens per second compared to GLM-5.1’s 54.406 tokens per second, the initial latency makes it less suitable for applications requiring immediate feedback.

Financial considerations further widen the gap. GLM-5.1 is significantly more affordable, with a blended cost of $2.15 per million tokens, compared to GPT-5.5’s $11.25 per million tokens. The output cost for GPT-5.5 is particularly high at $30.00 per million tokens, nearly seven times the cost of GLM-5.1. For high-volume production environments, these pricing differences will likely dictate the choice of model as much as the performance benchmarks.

Which model fits which workflow

Determining the right model requires aligning these technical trade-offs with your specific project needs. GLM-5.1 is the clear winner for developers building latency-sensitive applications or those operating on a strict budget. Its rapid response time ensures a fluid user experience, and its lower cost structure allows for greater scalability without excessive overhead. It is particularly well-suited for tasks where speed is a primary requirement and the complexity of the prompt is manageable.

GPT-5.5 is best reserved for workflows where the highest possible reasoning and coding accuracy is required, regardless of the cost or initial delay. If your project involves complex debugging, advanced algorithmic generation, or deep research, the performance gains provided by GPT-5.5 justify its premium pricing. The model’s ability to handle more difficult technical challenges makes it a powerful tool for backend processing where the time-to-first-token is less critical than the quality of the final output.

Decision takeaway

Ultimately, the decision rests on the nature of your workload. If you prioritize agility and cost-effectiveness, GLM-5.1 provides a robust, highly responsive solution. If your priority is maximum capability for difficult, logic-heavy tasks, GPT-5.5 provides the superior intelligence necessary to tackle those challenges, provided you can accommodate its higher costs and slower startup time.