Comparing Claude Opus 5.5: Max Effort vs. Xhigh Effort

Understanding the Benchmark Landscape

The Claude Opus 5.5 series introduces two distinct operational configurations: Max Effort and Xhigh Effort. When evaluating these models through the lens of the HLE, SciCode, and LCR benchmarks, clear performance variations emerge. The Max Effort configuration achieves an intelligence index of 57.6, outperforming the Xhigh Effort configuration’s index of 56. This trend continues in the HLE benchmark, where Max Effort scores 0.614 compared to 0.575, and in the SciCode benchmark, where it scores 0.669 against 0.65. Interestingly, both configurations perform identically on the LCR benchmark, yielding a score of approximately 0.847. These results suggest that the Max Effort configuration is tuned for more complex reasoning tasks, whereas the Xhigh Effort configuration offers a slightly more streamlined approach.

Speed and Cost Considerations

From a financial perspective, the two models are indistinguishable. Both configurations are priced at $4.00 per million tokens for input and $20.00 per million tokens for output, resulting in a blended cost of $8.00 per million tokens. Because the cost structure is identical, the primary differentiator becomes performance efficiency. The Xhigh Effort configuration provides concrete operational data, boasting an output speed of 84.874 tokens per second and a time to first token of 63.684 seconds. Conversely, the Max Effort configuration currently lacks public data regarding output speed and time to first token. Users must weigh the benefit of the higher intelligence index in the Max Effort model against the known, measurable latency of the Xhigh Effort model.

Aligning Models with Workflows

Selecting the appropriate model requires an assessment of your specific project requirements. The Max Effort configuration is designed for high-stakes reasoning where the marginal gain in intelligence scores—as seen in the HLE and SciCode benchmarks—justifies the lack of transparent speed metrics. This makes it suitable for research-heavy tasks or complex problem-solving where accuracy is the primary constraint.

In contrast, the Xhigh Effort configuration is better suited for production environments where latency is a critical factor. Because this model provides defined output speeds, developers can better predict the performance of integrated applications. While it sacrifices a small percentage of intelligence capacity, the trade-off provides the reliability necessary for high-volume or time-sensitive workflows.

Decision Takeaway

Ultimately, the distinction between these two configurations is a matter of optimization. Anthropic has positioned these models to serve different stages of the development lifecycle. By utilizing the provided benchmark data, users can align their choice with the specific demands of their workload. Whether you prioritize the peak reasoning capabilities of the Max Effort configuration or the predictable, measurable performance of the Xhigh Effort configuration, both models represent the latest advancements in Anthropic’s flagship Opus 5.5 series.