Introducing GPT-5.3-Codex-Spark

Today, we’re releasing a research preview of GPT‑5.3‑Codex‑Spark, a smaller version of GPT‑5.3‑Codex, and our first model designed for real-time coding. Codex-Spark marks the first milestone in our partnership with Cerebras, which we announced in January⁠. Codex-Spark is optimized to feel near-instant when served on ultra-low latency hardware—delivering more than 1000 tokens per second while remaining highly capable for real-world coding tasks.

We’re sharing Codex-Spark on Cerebras as a research preview to ChatGPT Pro users so that developers can start experimenting early while we work with Cerebras to ramp up datacenter capacity, harden the end-to-end user experience, and deploy our larger frontier models.

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.

Speed and intelligence

Coding

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.