GPT-5.5 vs. Claude Opus 4.7: A Comparative Analysis

What the benchmarks show

When evaluating these two models, the data suggests a nuanced trade-off between general intelligence and specialized technical proficiency. Claude Opus 4.7 (Adaptive Reasoning, Max Effort) holds a slight lead in the overall Intelligence Index at 57.3 compared to GPT-5.5’s 56.7. This marginal advantage is reflected in its performance on the SciCode benchmark, where Claude scores 0.545 against GPT-5.5’s 0.535. However, the narrative shifts when looking at coding and instruction following. GPT-5.5 demonstrates a clear lead in the Coding Index with a score of 56.2, outperforming Claude’s 52.5. Furthermore, GPT-5.5 shows stronger results in instruction following (IFBench) and complex reasoning tasks like TAU2 and TerminalBench Hard, suggesting it is better optimized for following intricate, multi-step prompts.

Speed and cost

Operational efficiency is a significant differentiator between these two models. GPT-5.5 is substantially faster, delivering an output speed of 64.654 tokens per second, which is nearly 35% faster than Claude Opus 4.7’s 48.002 tokens per second. The difference in time to first token is even more pronounced; GPT-5.5 begins generating responses in just 3.958 seconds, whereas Claude Opus 4.7 requires 21.112 seconds to initiate. This makes GPT-5.5 far more suitable for interactive, real-time applications.

Regarding cost, the models are competitively priced but utilize different structures. GPT-5.5 carries a higher output cost at $30.00 per million tokens compared to Claude’s $25.00, but it is cheaper on the input side at $5.00 versus $6.25. When looking at the blended rate, Claude Opus 4.7 is slightly more economical at $10.94 per million tokens, compared to the $11.25 blended rate of GPT-5.5. Users with heavy input-to-output ratios may find the cost differences negligible, but those with output-heavy workflows will see higher expenses with GPT-5.5.

Which model fits which workflow

Selecting the right model requires aligning these technical profiles with specific project needs. GPT-5.5 is the clear choice for developers and engineers who prioritize coding accuracy and low-latency responses. Its superior performance in TerminalBench Hard and its rapid time-to-first-token make it ideal for integrated development environments or chat interfaces where user experience is tied to speed. The model’s strength in instruction following also makes it a reliable partner for complex, multi-step automation tasks.

Claude Opus 4.7 is better suited for deep, analytical research and scientific tasks where the absolute highest intelligence index is required and latency is not a primary concern. Because it excels in the SciCode benchmark and maintains a higher overall intelligence score, it is well-positioned for academic or scientific reasoning workflows where the model has the time to process complex, multi-layered queries without the need for immediate, real-time output.

Decision takeaway

Ultimately, the decision rests on whether your workflow demands speed and coding reliability or maximum reasoning depth. GPT-5.5 offers a more responsive, developer-friendly experience with a slight edge in technical execution. Claude Opus 4.7 provides a more deliberate, highly intelligent reasoning engine that may be better suited for non-interactive, high-complexity scientific analysis.