GPT-5.4 vs. GPT-5.5: Evaluating OpenAI’s Latest Iterations

What the benchmarks show

The transition from GPT-5.4 to GPT-5.5 represents a measurable step forward in model capability. GPT-5.5 achieves an intelligence index of 60.2, surpassing the 56.8 recorded by GPT-5.4. This trend of improvement holds steady across most evaluation metrics. In the GPQA benchmark, GPT-5.5 scores 0.935 compared to 0.92, and it shows a notable lead in the TAU2 benchmark, scoring 0.938 versus 0.871. Coding performance also sees an uptick, with GPT-5.5 reaching a coding index of 59.1 against GPT-5.4’s 57.2.

However, these gains are not universal. In the SciCode benchmark, GPT-5.4 actually maintains a slight edge with a score of 0.566 compared to 0.561 for GPT-5.5. While the differences in LCR and IFBench are marginal, the consistent performance in TerminalBench Hard—where GPT-5.5 scores 0.606 compared to 0.575—suggests that the newer model is better equipped for complex, multi-step technical tasks. Both models share an unknown math index, leaving a gap in our understanding of their comparative quantitative reasoning.

Speed and cost

Choosing between these models requires a careful look at the economic and technical trade-offs. GPT-5.5 is significantly more expensive to operate, with a blended cost of $11.25 per million tokens, exactly double the $5.63 per million tokens required for GPT-5.4. This pricing structure reflects the higher resource intensity of the newer model, which may impact long-term scalability for high-volume applications.

Performance metrics present a nuanced picture. GPT-5.5 offers a much faster time to first token, clocking in at 47.763 seconds compared to the 186.304 seconds required by GPT-5.4. This makes GPT-5.5 feel more responsive in interactive environments. Conversely, GPT-5.4 maintains a higher output speed, generating 78.88 tokens per second compared to the 68.227 tokens per second of GPT-5.5. If your workflow involves generating large documents or extensive codebases, the higher throughput of GPT-5.4 may be more advantageous than the rapid initial response of the newer model.

Which model fits which workflow

The choice between these models should be dictated by the specific requirements of your project. GPT-5.5 is optimized for tasks where reasoning accuracy and latency are the primary constraints. Its improved performance in benchmarks like TAU2 and TerminalBench Hard makes it the preferred tool for complex problem-solving, debugging, and high-level analytical work where the cost per token is secondary to the quality of the output.

GPT-5.4, by contrast, is better suited for high-volume, cost-sensitive operations. Its lower pricing and superior raw output speed make it an efficient choice for batch processing, content generation at scale, or applications where the model is integrated into a pipeline that can tolerate a slower initial response time. By leveraging GPT-5.4 for routine tasks, organizations can maintain high throughput while keeping operational expenditures under control, reserving the more expensive GPT-5.5 for tasks that demand its higher intelligence index.

Decision takeaway

Ultimately, the decision rests on the balance between precision and price. GPT-5.5 is a more capable model, but it is not a strictly superior one in every dimension. By understanding the specific strengths of each—the raw speed and economy of GPT-5.4 versus the refined reasoning and responsiveness of GPT-5.5—users can deploy the right tool for their specific technical and financial constraints.