OpenAI on Thursday launched GPT-5.3-Codex-Spark, a stripped-down coding model engineered for near-instantaneous response times, marking the company’s first significant inference partnership outside its traditional Nvidia-dominated infrastructure. The model runs on hardware from Cerebras Systems, a Sunnyvale-based chipmaker whose wafer-scale processors specialize in low-latency AI workloads.The partnership arrives at a pivotal moment for OpenAI. The company finds itself navigating a frayed relationship with longtime chip supplier Nvidia, mounting criticism over its decision to introduce advertisements into ChatGPT, a newly announced Pentagon contract, and internal organizational upheaval that has seen a safety-focused team disbanded and at least one researcher resign in protest.”GPUs remain foundational across our training and inference pipelines and deliver the most cost effective tokens for broad usage,” an OpenAI spokesperson told VentureBeat. “Cerebras complements that foundation by excelling at workflows that demand extremely low latency, tightening the end-to-end loop so use cases such as real-time coding in Codex feel more responsive as you iterate.”The careful framing — emphasizing that GPUs “remain foundational” while positioning Cerebras as a “complement” — underscores the delicate balance OpenAI must strike as it diversifies its chip suppliers without alienating Nvidia, the dominant force in AI accelerators.Speed gains come with capability tradeoffs that OpenAI says developers will acceptCodex-Spark represents OpenAI’s first model purpose-built for real-time coding collaboration. The company claims the model delivers generation speeds 15 times faster than its predecessor, though it declined to provide specific latency metrics such as time-to-first-token or tokens-per-second figures.”We aren’t able to share specific latency numbers, however Codex-Spark is optimized to feel near-instant—delivering 15x faster generation speeds while remaining highly capable for real-world coding tasks,” the OpenAI spokesperson said.The speed gains come with acknowledged capability tradeoffs. On SWE-Bench Pro and Terminal-Bench 2.0 — two industry benchmarks that evaluate AI systems’ ability to perform complex software engineering tasks autonomously — Codex-Spark underperforms the full GPT-5.3-Codex model. OpenAI positions this as an acceptable exchange: developers get responses fast enough to maintain creative flow, even if the underlying model cannot tackle the most sophisticated multi-step programming challenges.The model launches with a 128,000-token context window and supports text only — no image or multimodal inputs. OpenAI has made it available as …
