OpenAI has started rolling out Images 2.6, the newest version of its image generation model, and the headline feature is one users have asked about for a long time: native GIF creation. For the first time, ChatGPT can produce an animated GIF in a single step, directly from a text description or an uploaded photo.
The mechanics are simple. Ask ChatGPT for "a GIF of a dancing cat" and the model generates the animation, then hands over controls to adjust playback speed, inspect the clip frame by frame and modify individual frames without regenerating the whole thing. Finished GIFs can be shared straight from the interface. Before this update, producing a GIF in ChatGPT required awkward workarounds, such as asking the model to write code that stitched static images together.
Alongside the consumer feature, OpenAI is splitting its API offering into two purpose-built models. GPT-Image-2.6 Flare is tuned for generation speed, aimed at applications where latency matters more than polish, while GPT-Image-2.6 Sunburst targets higher output quality for design- and asset-heavy workflows.
The company also refreshed its usage statistics: users now generate more than 4 billion images per week through ChatGPT and the API, up from the 3 billion weekly figure OpenAI disclosed in September — a jump of roughly a third in under two months.
Images 2.6 is rolling out gradually to all ChatGPT and Codex users on web, desktop and mobile apps.
The update lands in an increasingly crowded market. Google has been pushing its own image generation hard across Gemini, and native animation support is a visible differentiator precisely because most rivals still treat video and GIF output as separate products rather than a single conversational flow.
For OpenAI, the timing is no accident. The company has been leaning on consumer engagement numbers as it prepares for an eventual public listing, and 4 billion weekly images is the kind of statistic that reads well in a growth narrative. Whether Flare and Sunburst can convert that enthusiasm into developer revenue is the harder question — image API pricing has been trending down across the industry, and the speed-versus-quality split looks increasingly like the standard playbook rather than a moat.
Comments (0)
Log in to join the discussion
Log InNo comments yet