Claude Opus 4.6 vs. GPT-5.5: A Comparative Analysis

What the Benchmarks Show

When evaluating the raw capabilities of these two models, GPT-5.5 (xhigh) consistently outperforms Claude Opus 4.6 across the provided benchmark suite. With an intelligence index of 60.2 compared to Claude’s 52.9, GPT-5.5 demonstrates a higher ceiling for complex problem-solving. This trend is mirrored in the coding index, where GPT-5.5 scores 59.1 against Claude’s 48.1.

Specific performance metrics further illustrate this gap. GPT-5.5 achieves higher scores in GPQA (0.935 vs. 0.896) and HLE (0.443 vs. 0.367), suggesting a more robust grasp of specialized knowledge and high-level reasoning. The most significant discrepancy appears in IFBench, where GPT-5.5 scores 0.758 compared to Claude’s 0.531, indicating that the OpenAI model is more reliable when following complex, multi-layered instructions. While both models show strong performance in TAU2—with GPT-5.5 at 0.938 and Claude at 0.921—the data suggests that GPT-5.5 is the more capable engine for tasks requiring high precision and strict adherence to constraints.

Speed and Cost

Operational efficiency reveals a distinct trade-off between throughput and latency. GPT-5.5 is the faster model in terms of raw output speed, generating 68.227 tokens per second compared to Claude Opus 4.6’s 45.825 tokens per second. However, this high-speed output comes at the cost of a significantly longer time-to-first-token. GPT-5.5 requires 47.763 seconds to begin generating a response, whereas Claude Opus 4.6 starts in just 15.732 seconds.

From a pricing perspective, the models are competitively positioned but differ in their cost structures. Claude Opus 4.6 has a lower blended cost of $10.94 per million tokens, compared to $11.25 for GPT-5.5. While Claude is cheaper on a blended basis, its output is priced lower ($25.00/1M) than GPT-5.5 ($30.00/1M), though GPT-5.5 offers a more affordable input rate at $5.00/1M compared to Claude’s $6.25/1M. The optimal financial choice depends on whether your specific workflow is input-heavy or output-heavy.

Which model fits which workflow

The performance profile of Claude Opus 4.6 makes it an ideal candidate for interactive applications. Because the time-to-first-token is nearly three times faster than that of GPT-5.5, it is better suited for chat interfaces or tools where the user expects immediate feedback. The lower output cost also benefits workflows that require generating large amounts of text or code.

Conversely, GPT-5.5 is better suited for batch processing, complex reasoning tasks, and high-stakes coding projects where the quality of the output is more critical than the initial wait time. Its superior scores in coding and instruction following suggest that it will require fewer revisions and less prompt engineering to achieve the desired result, potentially offsetting the higher output costs through increased efficiency in task completion.

Decision takeaway

Ultimately, the decision between these models is a matter of prioritizing latency versus capability. If your primary goal is to minimize wait times for interactive user experiences, Claude Opus 4.6 is the more responsive tool. If your priority is maximizing the accuracy and reasoning depth of your outputs, GPT-5.5 is the more powerful, albeit slower-to-start, alternative.