Compare GPT‑5.3‑Codex‑Spark vs. OpenAI Dots in 2026

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Description

GPT-5.3-Codex-Spark is OpenAI’s first model purpose-built for real-time coding within the Codex ecosystem. Engineered for ultra-low latency, it can generate more than 1000 tokens per second when running on Cerebras’ Wafer Scale Engine hardware. Unlike larger frontier models designed for long-running autonomous tasks, Codex-Spark specializes in rapid iteration, targeted edits, and immediate feedback loops. Developers can interrupt, redirect, and refine outputs interactively, making it ideal for collaborative coding sessions. The model features a 128k context window and is currently text-only during its research preview phase. End-to-end latency improvements—including WebSocket streaming and inference stack optimizations—reduce time-to-first-token by 50% and overall roundtrip overhead by up to 80%. Codex-Spark performs strongly on benchmarks such as SWE-Bench Pro and Terminal-Bench 2.0 while completing tasks significantly faster than its larger counterpart. It is available to ChatGPT Pro users in the Codex app, CLI, and VS Code extension with separate rate limits during preview. The model maintains OpenAI’s standard safety training and evaluation protocols. Codex-Spark represents the beginning of a dual-mode Codex future that blends real-time interaction with long-horizon reasoning capabilities.

Description

OpenAI Dots are persistent AI agents powered by GPT-6 Astra that can independently make progress on projects while remaining under the user's direction. A dot functions as an extension of its user by learning how they work, monitoring what requires attention, and using its own cloud computer to carry out tasks between conversations. It can begin with context from ChatGPT memory and use Codex and connected tools to work with relevant applications, documents, data, and workflows. Users can give a dot responsibility for a large project or a smaller recurring workflow and allow it to proactively determine appropriate next steps. Dots can analyze new data, refresh reports and presentations, investigate unexpected results, prepare code and tests, monitor feedback, and draft supporting materials for review. They can also maintain continuity across related tasks, such as updating an investor presentation when new financial data arrives or tracking an API migration until remaining dependencies are resolved. Users can communicate with their dots through ChatGPT on web, mobile, and desktop to provide feedback, change direction, or review work in progress. Permissions and Custom Rules determine which applications a dot can access, which actions it can perform independently, and which activities require explicit approval. Potentially consequential actions are subject to additional review against the user's instructions, configured rules, and applicable safety requirements.