You can try GPT 5.3 Codex through the official Codex experiences OpenAI ships (for example, the Codex app) and through supported integrations where GPT 5.3 Codex is available as a selectable model. In practice, “try it” can mean a few different things: using an interactive app, using a CLI, using an IDE extension, or using it through an API-style interface (depending on what your plan or environment supports). If your goal is quick evaluation, the lowest-friction path is usually an app or IDE integration because you can point it at a real project and see how it performs on your actual tasks.
When evaluating, don’t just ask it to write a function in a vacuum. Give it a realistic slice of work: a failing test, a minimal reproduction, or a small ticket with acceptance criteria. Then judge it on outcomes: did it fix the bug without introducing new ones, did it follow your style rules, and did it explain the change clearly? If you’re trying it in an IDE setting, pay attention to how it handles multi-file edits and whether it keeps changes coherent across modules. If you’re trying it via a CLI or app, watch for how it breaks down tasks and whether it asks good clarifying questions when requirements are underspecified.
If your use case is documentation Q&A or codebase-aware assistance, set up a small RAG demo alongside your trial. Index a handful of docs and code snippets into Milvus or managed Zilliz Cloud, retrieve top-k context for each query, and provide that context to GPT 5.3 Codex. This lets you test the behavior you actually care about in production: grounded answers, fewer invented APIs, and consistent output structure. It also reveals practical issues early—like whether your chunking is too large, whether metadata filters are missing, or whether the model needs a stricter template to reliably cite or use retrieved context.