Theophile Sautory

GPT-5.1-Codex-Max is our best agentic coding model, and is now available in our API. It's a great model, but it’s also easier than ever to integrate. I’ve spent the last two weeks experimenting with our top customers - here's how to get the most out of it 🧵

I've been loving GPT-4.1, and it's now my daily driver. We've spent hundreds of hours experimenting to get it performing at its best, and I wanted to share some key things we learned to help you get the most out of these models from day 1. Agentic workflows - This is your new starting point. Just add tools and prompt to make it plan, execute, and verify iteratively until producing a correct answer. Only add multi-agent frameworks once you've squeezed as much as you can here. - Coding agents will be particularly good with this model, especially when paired with our recommended diff output format and diff apply tool. Chain of thought - Prompting for chain of thought can help to break down the problem and improve overall intelligence. - Start with a simple instruction at the end of your prompt, as the model is already quite good at thinking through problems. Then, observe correct and incorrect eval samples to direct further changes to the thinking instructions. - Often, asking the model to spend more time understanding user intent and gathering context can help. Long context - The full context window (now 1M token in put) is much more performant/usable than before. As task complexity increases, performance might still improve by decreasing context size. - Check the guide for suggested delimiters. Improving instruction following - Performance is much better with this model, and you can add significant detail for what to do/not do in different circumstances. - Many model performance issues actually come from inconsistent, conflicting, or underspecified instructions (including conflicting information in examples as well). - Be careful with telling the model to "always" do something, you might not actually want that and it can induce hallucinations, for example if it needs to call a tool with insufficient context. - I find it's helpful to ask ChatGPT Canvas or Cursor for help iterating on prompts cohesively (e.g. asking for conflicting instructions, edge cases, or adding an instruction and updating examples at the same time) Prompt structure - tl;dr check out the recommended template (attached) - Context at or near the bottom seems to be best (and repeating instructions both above and below very long context can actually help too). - Markdown and XML both work great