OpenAI patches GPT-6 image encoding bug

OpenAI has fixed an image encoding bug that degraded visual understanding in GPT-6 Sol and GPT-6 Luna, two multimodal reasoning models released days before the patch. The server-side update is live in the API and Codex, including computer-use workflows.

OpenAI’s API changelog does not quantify the regression or provide benchmark deltas. Requests still completed, which left developers with plausible answers and lower-than-expected evaluation scores instead of explicit errors. Teams that tested either model with images should rerun those evaluations.

Why plausible answers slipped through

The image encoder converts pixels into internal representations that the model can analyze. A defect at this stage can omit or distort visual details before reasoning begins, causing the model to misread text, objects, layouts, or interface controls while still producing a fluent response.

OpenAI has corrected a similar defect before. A March update fixed a small encoder bug affecting input_image inputs in GPT-5.4, and the company said some image-understanding use cases could improve automatically.

One encoder, several surfaces

• GPT-6 Sol (gpt-6-sol): the higher-capability tier.
• GPT-6 Luna (gpt-6-luna): the lower-cost, high-volume tier.
• Responses and Chat Completions APIs: image requests sent to either model.
• Codex and computer use: workflows that inspect screenshots or other images.

Computer-use agents depend on accurate screenshot interpretation for every action. Distorted visual features can lead an agent to select the wrong control, misread a form field, enter text in the wrong place, or incorrectly decide that a task has finished.

Server-side patch, unchanged prices

The fix requires no code changes or model-version migration. New requests use the corrected path automatically, while outputs generated before the patch remain unchanged. Application-level caches, stored responses, and evaluation records from affected runs require separate review.

Rerun the evidence

The same model IDs now use a corrected image path, so pre-fix and post-fix evaluations reflect different serving behavior. Keep prompts, images, tools, sampling settings, and scoring methods constant when comparing results, and record run timestamps alongside the changelog entry.

1. Repeat vision evaluations. Rerun tests conducted between the models’ launch and the patch, then compare accuracy and failure categories.
2. Retry visual agent failures. Revisit Codex and computer-use runs involving misread controls, screenshots, text, or page state.
3. Review stored results. Inspect application-level caches and saved outputs produced before the fix because the patch cannot revise completed responses.
4. Reconsider model-selection decisions. Collect corrected multimodal results before switching models based on earlier visual performance.