GPT 5.3 Codex and Claude Opus 4.6 have both just been released, and they show how quickly AI models are moving toward longer, more practical workflows. Instead of focusing solely on answering questions or generating short code snippets, both models are designed to handle real-world tasks that involve planning, iteration, and working across larger contexts.
In this article, I compare GPT-5.3 Codex and Claude Opus 4.6 based on their performance in practice, using two common scenarios: building user interfaces and working with data.
GPT 5.3 Codex is the latest Codex model from OpenAI, built with a clear focus on execution-oriented developer workflows. Rather than stopping at code generation, the model is designed to handle longer tasks that involve planning, tool usage, and iterative progress across real systems.
A key aspect of GPT 5.3 Codex is its agentic design. It can work directly with terminals, files, and build tools, and continue operating across extended sessions while still allowing developers to guide and adjust the process without losing context. This makes it better suited for workflows where multiple steps and follow-up changes are expected.
Its performance reflects this focus. GPT 5.3 Codex achieves 56.8% on SWE Bench Pro, 77.3% on Terminal Bench 2.0, and 64.7% on OSWorld Verified, which evaluates real computer-based task execution. It also runs about 25% faster than earlier Codex versions and tends to be more token-efficient, which improves responsiveness during longer sessions.
In practice, GPT 5.3 Codex fits well into full-stack development, large-scale debugging and refactoring, infrastructure work, and other scenarios where steady progress and execution reliability matter.
Claude Opus 4.6 is the latest flagship model from Anthropic, positioned as its most capable model for reasoning-heavy work, structured outputs, and long-context tasks. While it continues to support coding and agentic workflows, its core focus is on clarity, consistency, and deep reasoning across complex information.
One of the most notable updates in Opus 4.6 is its long-context capability. The model supports a 200K token context window by default, with a 1M token context window available in beta, and shows significantly improved long-context retrieval. On MRCR v2, a needle-in-a-haystack benchmark, it reaches 76% accuracy, indicating much lower context degradation over extended sessions.
Claude Opus 4.6 also introduces adaptive thinking and configurable effort levels, allowing developers to balance reasoning depth, speed, and cost. In practice, this makes the model more deliberate on complex problems while remaining controllable for simpler tasks. It supports 128K output tokens, which is useful for large reports, analyses, and structured documents.
On evaluations, Opus 4.6 leads on several reasoning and knowledge-work benchmarks, including Terminal-Bench 2.0, Humanity’s Last Exam, and GDPval-AA, where it outperforms GPT-5.2 by roughly 144 Elo points. These strengths make it especially well-suited for analyst workflows, document-heavy research, data analysis, and scenarios where accuracy and consistency matter more than rapid execution.
Let us now compare Claude Opus 4.6 and GPT 5.3 Codex on a simple but representative task: building a basic user interface. The goal here is to observe how each one approaches UI construction, code structure, and iteration when given the same prompt.
Prompt Used for Both Models