As it battles rival Anthropic for the enterprise market, OpenAI introduced a new coding model powered by an advanced AI chip from startup Cerebras.
OpenAI released the new coding model, GPT-5.3-Codex-Spark, in research preview on Feb. 12. The generative AI vendor designed the model for real-time coding as a smaller version of GPT-5.3-Codex, which OpenAI released earlier this month for computer use and coding tasks.
Codex-Spark is the first OpenAI model not to use Nvidia’s hardware, running solely on Cerebras Wafer-Scale Engine 3 chips.
The release of Codex-Sparks comes as both Cerebras and OpenAI are trying to prove to enterprises their worth over their competitors.
For Cerebras, a specialized semiconductor and cloud computing startup, Codex-Spark’s effectiveness could show potential customers that its large AI chips and wafer-scale design can be just as valuable as Nvidia’s market-dominating GPUs. Meanwhile, for OpenAI, improving its coding models could boost its credibility in a market dominated by Anthropic, especially given that the creator of the popular Claude model just raised another $30 billion and is putting $20 million into a new super PAC to counter OpenAI’s super PAC.
Benefits and Opportunities
GPT-5.3-Codex-Spark focuses heavily on real-time coding. OpenAI said the model works with Codex for tasks such as targeted edits, reshaping logic, and getting work done in the moment.
As a small model, Codex-Spark is easier to support and more cost-efficient for developers looking for immediate support; however, it is limited in some features, said Lian Jye Su, an analyst at Omdia, a division of Informa TechTarget. For example, the context window is only 128k and supports text-only prompts.
“For some use cases, it’s probably sufficient, and it’s really cleverly designed to target a specific segment of the coding population,” Su said. He added that beginner coders or those seeking real-time coding assistance might find Codex-Spark most appealing.
Moreover, OpenAI’s use of Cerebras’ wafer-scale engine infrastructure could represent an opportunity for other AI hardware vendors, such as Grok or Tenstorrent, that specialize in application-specific integrated circuits or ASICs.
“By making these services available and running on AI ASICs, particularly the ones that are specifically designed for inference, creates this business model for similar players in the market,” Su said. He added that, with Cerebras’ high-throughput inference chips, Codex-Sparks supports low-latency, real-time inference.
Challenges and Enterprises
It is clear, however, that much of the engineering still needs to be configured on the backend for OpenAI’s use of Cerebras GPUs rather than Nvidia’s. Su added that the shift from GPU to the wafer-scale engine will require significant reconfiguration, porting, and codebase conversion outside Nvidia’s architecture.
However, if OpenAI succeeds with Cerebras, other AI model makers could decide to try other hardware options in the future, Su added.
For enterprises, the hardware behind the system matters less than whether the product functions effectively.
“They only care about whether this works for them or not, the accuracy, the responsiveness, whether it is really as they said, like really low latency,” he said.