OpenAI's fastest model ever is dead. Tibo, the head of Codex, posted that GPT-5.3-Codex-Spark will be retired next week. This model, capable of 1200 tokens per second, was OpenAI's first model to leave NVIDIA, and the first delivery of a 750 megawatt Cerebras order, lived only 7 months from release to shutdown. Tibo's reason was simple: usage has been steadily declining, and they already have significantly better models on hand, "it's time to make room for the future." He also added, "I can't believe we actually released a model with such a long name!!"
From 'future' to marginalization, defeated by 'dumb speed'
Spark was launched on February 12th with great fanfare: it was OpenAI's first model specifically designed for real-time programming, with a 128k context window, generating over 1000 tokens per second, and the official claim of an 80% reduction in round-trip cost and a 50% reduction in first token latency. It was also OpenAI's first production model running outside of the NVIDIA technology stack, with underlying computing power coming from Cerebras' wafer-scale chip WSE-3, marking the first delivery of that 750 megawatt, over $20 billion worth of computing power order.
But the hype faded faster than it rose. Spark's fatal flaw lay in the fact that a single wafer couldn't hold the flagship model; it was essentially a "distilled small model" compromised for extreme speed: in the Terminal-Bench 2.0 test, it scored only 58.4%, far below the full version of GPT-5.3-Codex's 77.3%; SWE-Bench Pro curves showed that although it could compress tasks to 1-2 minutes, its accuracy remained stuck at 47%-51%, while the full version could take 3 minutes to reach 51% and extend to 16 minutes to reach 57%. The claimed "15x speedup" was debunked - under the same accuracy, it was only 1.37 times faster. Developers criticized it for fabricating API endpoints out of nowhere and having unstable JSON formats. Turing College, a training institution, summarized it succinctly: "Speed without intelligence is just failing faster."
The real death sentence for Spark came on August 13th, when Cerebras released the Ultrafast mode: instead of a scaled-down version, the flagship model GPT-5.6 Sol ran directly on the wafer. By splitting the flagship model layer by layer and spreading it across multiple CS-3 nodes to form a pipeline, Ultrafast achieved 750 tokens per second under the premise of equivalent intelligence to the standard version - the same task took an average of 7.7 minutes on the standard version, but only 83 seconds on Ultrafast, achieving a 5.6 times end-to-end speedup. Cerebras CEO Andrew Feldman said, "Speed and intelligence are no longer mutually exclusive."
Speed goes from 'exclusive model' to 'paid tier'
Spark wasn't the only historical burden being cleaned up. Over the past three months, OpenAI's model library has undergone drastic changes: on June 2nd, GPT-5.2 and GPT-5.3-Codex were retired; on August 31st, GPT-5.4 and 5.4 Mini were retired, with users migrating to the 5.6 generation overall; on September 11th, it was GPT-5.3-Codex-Spark's turn. As the old generations left, Codex welcomed a new naming era with Sol, Terra, Luna, and Astra.
