Our latest frontier models have shown particular strengths in their ability to do long-running tasks, working autonomously for hours, days or weeks without intervention. Codex-Spark is our first model designed specifically for working with Codex in real-time—making targeted edits, reshaping logic, or refining interfaces and seeing results immediately. With Codex-Spark, Codex now supports both long-running, ambitious tasks and getting work done in the moment. We hope to learn from how developers use it and incorporate feedback as we continue to expand access.

At launch, Codex-Spark has a 128k context window and is text-only. During the research preview, Codex-Spark will have its own rate limits and usage will not count towards standard rate limits. However, when demand is high, you may see limited access or temporary queuing as we balance reliability across users.

Codex-Spark is optimized for interactive work where latency matters as much as intelligence. You can collaborate with the model in real time, interrupting or redirecting it as it works, and rapidly iterate with near-instant responses. Because it’s tuned for speed, Codex-Spark keeps its default working style lightweight: it makes minimal, targeted edits and doesn’t automatically run tests unless you ask it to.

Codex-Spark is a highly capable small model optimized for fast inference. On SWE-Bench Pro and Terminal-Bench 2.0, two benchmarks evaluating agentic software engineering capability, GPT‑5.3‑Codex‑Spark demonstrates strong performance while accomplishing the tasks in a fraction of the time compared to GPT‑5.3‑Codex.