--- type: "BlogPosting" title: "GPT-5.3-Codex-Spark: 1,000 Tokens/s on Cerebras Chips" description: "OpenAI launches GPT-5.3-Codex-Spark on Cerebras Wafer Scale Engine 3 — over 1,000 tokens per second, 80% faster roundtrips, and the first non-Nvidia production model. What it means for developers." resource: "https://www.contextstudios.ai/blog/gpt-53-codex-spark-openais-first-model-on-cerebras-chips-delivers-1000-tokens-per-second" language: "en" tags: ["OpenAI", "GPT-5.3", "Codex-Spark", "Cerebras", "AI-Coding", "Wafer-Scale-Engine", "Real-Time-Inference"] generated: by: "process:contextstudios-md/1" at: "2026-10-08T21:01:03.453Z" status: "stable" --- # GPT-5.3-Codex-Spark: 1,000 Tokens/s on Cerebras Chips Published: 2026-02-14 Tags: OpenAI, GPT-5.3, Codex-Spark, Cerebras, AI-Coding, Wafer-Scale-Engine, Real-Time-Inference ![GPT-5.3-Codex-Spark: 1,000 Tokens/s on Cerebras Chips](https://wary-platypus-754.convex.cloud/api/storage/3f20f5f4-10fd-49fa-9d7c-7f206d7dbb8a) # GPT-5.3-Codex-Spark: OpenAI's First Model on Cerebras Chips Delivers 1,000 Tokens Per Second **OpenAI just shipped the fastest coding model in production — and it's not running on Nvidia.** GPT-5.3-Codex-Spark launched on February 12, 2026 as a research preview for ChatGPT Pro users. It's a smaller, speed-optimized version of GPT-5.3-Codex, and the first fruit of OpenAI's partnership with Cerebras Systems. The headline number: over 1,000 tokens per second for real-time coding assistance. For developers who spend their days waiting for AI suggestions, this changes the interaction model entirely. Let's break down what Codex-Spark is, why Cerebras matters, and what this means for AI-native development. ## What Is GPT-5.3-Codex-Spark? Codex-Spark is a lightweight version of GPT-5.3-Codex, purpose-built for real-time interactive coding. While the full GPT-5.3-Codex excels at long-running autonomous tasks — working for hours or days without intervention — Spark is designed for the opposite: fast, iterative collaboration where you're in the driver's seat. **Key specs:** - **Speed:** 1,000+ tokens/second on Cerebras hardware - **Context window:** 128K tokens - **Modality:** Text-only (for now) - **Availability:** Research preview for ChatGPT Pro users - **Platforms:** Codex app, CLI, and VS Code extension - **Rate limits:** Separate from standard limits during preview On SWE-Bench Pro and Terminal-Bench 2.0 — two benchmarks for agentic software engineering — Codex-Spark delivers strong performance while completing tasks in a fraction of the time compared to GPT-5.3-Codex. It also outperforms GPT-5.1-Codex-mini in capability. ## What Is Cerebras and Why Does It Matter? Cerebras Systems builds the largest chips in the world. Their Wafer Scale Engine 3 (WSE-3) is literally the size of a dinner plate, packed with 4 trillion transistors. Unlike conventional GPUs that use many small chips networked together, Cerebras puts everything on a single massive wafer — eliminating the communication bottlenecks that slow down inference. The company has demonstrated up to 3,000 tokens per second on other models. The "relatively modest" 1,000 tok/s for Codex-Spark likely reflects the model's complexity rather than hardware limitations. Cerebras recently raised $1 billion at a $23 billion valuation and is planning an IPO. Their partnership with OpenAI, announced in January 2026, is worth over $10 billion in a multi-year deal. ## The Full Speed Story: Not Just the Chip The 1,000 tok/s headline is only part of the picture. OpenAI also re-engineered its entire inference pipeline: - **80% reduction** in client/server roundtrip overhead - **50% faster** time-to-first-token (TTFT) - **30% reduction** in per-token overhead - **Persistent WebSocket connections** replacing traditional request-response cycles These infrastructure improvements will roll out to all models, not just Codex-Spark. The WebSocket path is enabled by default for Spark and will become standard across the fleet. ## OpenAI's Hardware Diversification Strategy Codex-Spark signals something bigger than one model: OpenAI is systematically reducing its dependence on Nvidia. **The timeline:** - **October 2025:** Multi-year chip deal with AMD - **November 2025:** $38 billion cloud computing agreement with Amazon - **January 2026:** $10B+ partnership with Cerebras announced - **February 2026:** Codex-Spark ships as first non-Nvidia production model - **Ongoing:** Custom AI chip design with TSMC OpenAI isn't abandoning Nvidia — GPUs remain foundational for training and broad inference. But for latency-critical workloads like real-time coding, specialized hardware like Cerebras offers clear advantages. As OpenAI put it: "GPUs and Cerebras can be combined for single workloads to reach the best performance." ## What This Means for Developers ### Real-Time Pair Programming Becomes Real At 1,000 tokens per second, the AI stops feeling like a tool you wait for and starts feeling like a collaborator you think with. You can interrupt, redirect, and iterate with near-instant responses. This is the difference between sending an email and having a conversation. ### Two Modes of AI Coding Codex now supports both paradigms: 1. **Long-running autonomy:** GPT-5.3-Codex handles complex, multi-hour tasks 2. **Real-time iteration:** Codex-Spark handles rapid prototyping and targeted edits OpenAI's vision: these modes will eventually blend, with Codex keeping you in a tight interactive loop while delegating longer work to sub-agents in the background. ### The Speed Competition Intensifies With [Anthropic's](https://www.contextstudios.ai/blog/claude-opus-46-anthropics-new-flagship-with-1m-context-and-agent-teams "[Claude Opus 4.6](/blog/claude-opus-46-anthropics-new-flagship-with-1m-context-and-agent-teams) — Anthropic's New Flagship with 1M Context and [Agent Teams](/blog/claude-code-agent-teams-a-builders-guide-to-parallel-ai-coding)") Claude Opus 4.6 (February 2026) pushing agent teams and [multi-agent](https://www.contextstudios.ai/blog/github-agent-hq-the-multi-agent-future-of-coding-is-here "[GitHub Agent HQ](/blog/github-agent-hq-the-multi-agent-future-of-coding-is-here): The Multi-Agent Future of Coding Is Here") coding, and Google doubling AI investment, the coding AI race is accelerating. Speed is becoming the differentiator — a model that codes faster lets developers iterate faster. ## The Context Studios Take From our Berlin studio, we see Codex-Spark as validation of a thesis we've been building on: **the future of development isn't about AI replacing developers — it's about AI matching developer speed of thought.** The best AI coding tools disappear into the workflow. When inference takes seconds, you're forced to context-switch. When it takes milliseconds, you stay in flow. Codex-Spark, combined with tools like Claude Code 2.1 and GitHub Agent HQ, points toward a development experience where the bottleneck shifts from "waiting for the AI" to "knowing what to ask." For teams building AI-native applications — which is increasingly every team — this means: - **Faster prototyping cycles:** Test ideas in seconds, not minutes - **Lower cost of experimentation:** When iteration is cheap, you try more things - **New interaction patterns:** Real-time steering replaces batch-and-wait ## Availability and Pricing Codex-Spark is currently available as a research preview for **ChatGPT Pro users** ($200/month). It works in: - The Codex app (latest version) - The Codex CLI - The VS Code extension API access is rolling out to a small set of design partners first, with broader access coming in the weeks ahead. During the preview, usage has separate rate limits that may adjust based on demand. ## What's Next Codex-Spark is explicitly the "first in a family of ultra-fast models." OpenAI has announced plans for: - Larger models on Cerebras hardware - Longer context windows - Multimodal input support - Blended autonomous + real-time workflows As Sean Lie, Cerebras CTO and co-founder, put it: "What excites us most about GPT-5.3-Codex-Spark is partnering with OpenAI and the developer community to discover what fast inference makes possible — new interaction patterns, new use cases, and a fundamentally different model experience." The inference speed race is just getting started. And for developers, that's unambiguously good news. --- *Context Studios is an AI development studio based in Berlin, building AI-native applications and sharing insights on the tools shaping modern [software development](https://www.contextstudios.ai/blog/spotify-built-an-ai-coding-agent-that-replaced-writing-code-heres-what-developers-need-to-know "Spotify Built an [AI Coding Agent](/blog/spotify-built-an-ai-coding-agent-that-replaced-writing-code-heres-what-developers-need-to-know) That Replaced Writing Code — Here's What Developers Need to Know").* --- ## Frequently Asked Questions ### What is GPT-5.3-Codex-Spark and how does it differ from GPT-5.3-Codex? Codex-Spark is a lightweight, speed-optimized version of GPT-5.3-Codex designed for real-time interactive coding. While the full Codex excels at long-running autonomous tasks, Spark delivers over 1,000 tokens per second for fast, iterative collaboration. ### Why is Codex-Spark running on Cerebras chips instead of Nvidia GPUs? Cerebras Wafer Scale Engine 3 chips are purpose-built for inference speed. This is the first production model from OpenAI's partnership with Cerebras Systems, representing a strategic diversification away from sole reliance on Nvidia hardware. ### Who can access GPT-5.3-Codex-Spark? It launched as a research preview for ChatGPT Pro users, available through the Codex app, CLI, and VS Code extension with separate rate limits during the preview period. ### What is the context window for Codex-Spark? Codex-Spark supports a 128K token context window, sufficient for most interactive coding sessions though smaller than some competing models. ### How does Codex-Spark perform on coding benchmarks? It delivers competitive scores on SWE-Bench Pro and Terminal-Bench 2.0 — two benchmarks for agentic software engineering — while maintaining its significant speed advantage over larger models. ## Related - [LLM Development](https://www.contextstudios.ai/llm-development.md) - [AI Software Development](https://www.contextstudios.ai/ai-software-development.md) - [LLM Integration](https://www.contextstudios.ai/llm-integration.md) - [AI Consulting](https://www.contextstudios.ai/ai-consulting.md)