Tibo Sottiaux, head of Codex, announced that GPT-5.3-Codex-Spark will be retired next week due to a continuous decline in usage, and the company has significantly stronger models that need space for future products.

The model was launched on February 12 as a research preview, positioned as a smaller version of GPT-5.3-Codex, focusing on real-time coding. It was the first milestone of OpenAI's collaboration with Cerebras, aiming for near-instant feedback on wafer-level chips, with official claims of inference speeds exceeding 1,000 tokens per second, an order of magnitude faster than the flagship coding model.

Initially available only to ChatGPT Pro users, it covered Codex applications, command line, and VS Code extensions, with a context window of 128,000 tokens, and was pure text. The preview period had separate usage limits that did not count against the regular Codex quota; there could be queues during peak times. When cleaning the cluster in May, Tibo explicitly stated that Spark would not be retired at that time, only removing the entry points for GPT-5.2 and GPT-5.3-Codex in the ChatGPT login path.

The subsequent coding mainline has shifted towards larger context and higher capability tiers, including GPT-5.5 by default and GPT-6 Astra. While Spark's speed advantage remains, developers have concentrated long tasks and agent workflows on the updated models, leading to a decline in usage for this small model designed for low-latency interaction, with cluster management prioritized over retaining experimental models.

Tibo is also responsible for the core product platforms of Codex and ChatGPT, frequently announcing quota resets and model retirements. The Codex command line is written in Rust and open-sourced, with internal use of coding agents in code reviews, maintenance, and system restructuring; the product strategy is to merge ChatGPT and Codex into the same workflow rather than maintaining multiple generations of coding models in parallel.

From a market mechanism perspective, this is a reallocation of computing power rather than a disappearance of demand: buyers are shifting from immediate code modification scenarios for Pro users to new flagships capable of running long tasks; sellers are OpenAI reclaiming low-usage checkpoints from Cerebras' low-latency positions and GPU clusters. The event is driven by product line simplification, with funding and chip resources flowing towards higher-demand models like Astra. Beneficiaries include new model training and inference quotas, while workflows relying on Spark's feel for rapid iteration and Cerebras' non-NVIDIA real-time coding showcase are under pressure.

The collaboration with Cerebras made Spark the first production coding model fully running on non-NVIDIA hardware, emphasizing that GPUs and wafer chips can share the same workload. As flagship contexts expand to the million-token level, the 128k, pure text, Pro-limited preview tier loses its default entry position.

Source: Public Information