GPT-5.3 Codex vs. GPT-5.5: Evaluating OpenAI’s High-Performance Models

What the Benchmarks Show

The transition from GPT-5.3 Codex to GPT-5.5 represents a clear upward trajectory in model intelligence and specialized capability. Across the board, GPT-5.5 demonstrates higher proficiency, with an Intelligence Index of 60.2 compared to the 53.6 of GPT-5.3. This performance gap is mirrored in the coding domain, where GPT-5.5 achieves a Coding Index of 59.1 against 53.1.

Looking at specific benchmarks, the improvements are consistent. GPT-5.5 shows a notable lead in TerminalBench Hard (0.606 vs 0.530) and TAU2 (0.938 vs 0.859), suggesting that the newer model is better equipped for complex, multi-step agentic tasks and technical problem-solving. While both models perform similarly on IFBench, the incremental gains in GPQA and SciCode confirm that GPT-5.5 is the more robust choice for research-heavy or logic-intensive applications where precision is non-negotiable.

Speed and Cost

The performance gains of GPT-5.5 come at a significant cost in both capital and latency. GPT-5.5 is priced at a blended rate of $11.25 per million tokens, more than double the $4.81 blended rate of GPT-5.3 Codex. This pricing structure reflects the higher computational overhead required for the newer model’s increased intelligence.

Latency metrics further distinguish the two. GPT-5.3 Codex offers a faster output speed of 92.337 tokens per second, making it better suited for applications requiring rapid text generation. While GPT-5.5 actually improves upon the time-to-first-token (47.763s vs 54.938s), its overall generation speed is slower at 68.227 tokens per second. Users must weigh whether the faster initial response of the 5.5 model justifies the slower sustained output and the higher financial investment.

Which model fits which workflow

Determining the right model requires an assessment of your specific operational constraints. GPT-5.3 Codex is optimized for high-volume environments where cost-efficiency and rapid throughput are the primary drivers. It is an ideal candidate for automated code documentation, large-scale data processing, or any application where the marginal gain in reasoning provided by GPT-5.5 does not offset the increased cost and slower generation speed.

Conversely, GPT-5.5 is built for high-complexity workflows. Its superior performance in benchmarks like TAU2 and TerminalBench Hard indicates that it is better suited for autonomous agents, complex software engineering tasks, and deep scientific analysis. If your workflow involves tasks that frequently trip up smaller models or require a higher degree of logical consistency, the premium paid for GPT-5.5 is a necessary expense for improved reliability.

Decision takeaway

Ultimately, the choice between these two models is a classic trade-off between efficiency and capability. OpenAI has positioned GPT-5.3 Codex as a workhorse for developers who need speed and budget control, while GPT-5.5 serves as the flagship for tasks requiring the highest available intelligence. By mapping your project’s sensitivity to latency and cost against the necessity for advanced reasoning, you can select the model that aligns best with your technical requirements.