openai
OpenAI: GPT Image 2.6 Sunburst
openai/gpt-image-2.6-sunburst
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
According to OpenRouter's catalog, openai/gpt-image-2.6-sunburst is an OpenAI image model that accepts text and image inputs and returns images. The catalog characterizes it as detail-oriented for dense compositions, layouts containing substantial text, and repeated edits intended to preserve visual details. It also lists a large context window and controls including seed, response_format, structured_outputs, and max_tokens. No independent model card or measured benchmark results were available in this source pack, so these capabilities are source-reported rather than verified.[1]
Strengths
Limitations
Where to use it
- Worth piloting for detailed marketing or editorial visuals with dense layouts and substantial embedded text.[1]
- Consider for iterative image-editing workflows where retaining visual details between revisions matters.[1]
- Evaluate for applications that need image outputs with seed or structured response controls.[1]
Sources
- [1] OpenRouter catalog · OpenAI: GPT Image 2.6 SunburstSources checked: Nov 13, 2026
About this model
Description below is the original text supplied by OpenRouter, shown in the source language.
GPT Image 2.6 Sunburst is OpenAI's image generation and editing model optimized for detail: dense compositions, text-heavy layouts, and edits that keep details consistent from one edit to the next...
Specifications
- Model ID
- openai/gpt-image-2.6-sunburst
- Canonical ID
- openai/gpt-image-2.6-sunburst-20261104
- Creator
- openai
- Added to OpenRouter
- 04.11.2026
- Context length
- 400,000
- Modality
- text+image->image
- Input modalities
- text, image
- Output modalities
- image
- Token pricing
- No per-token price in the source snapshot — see OpenRouter for the billing unit
- Knowledge cutoff
- —
- Expiration date
- —
Tasks and capabilities
A grade appears only where the source supplies a matching measurement for that task, the model is active and the cohort holds at least 10 comparable models. The capability column lists facts the source declares — supported formats, reasoning and tool parameters — which are not measured quality.
| Task | Grade | Source-declared capability |
|---|---|---|
| General quality | No measurement | — |
| Coding | No measurement | — |
| Agents | No measurement | parameters: structured_outputs |
| Writing | No measurement | — |
| Reasoning | No measurement | — |
| Document analysis | No measurement | — |
| Vision | No measurement | input: image |
| Image generation | No measurement | output: image |
| Audio | No measurement | — |
| Video | No measurement | — |
| Embeddings | No measurement | — |
Evaluations
No evaluations are supplied for this model.
How grades work
Each letter grade compares a model only against other models scored by the same source and metric in the current snapshot. S means the top 10%, A the next 20%, B the next 30%, C the rest. Models without a published measurement are shown as not rated — a supported format alone never implies measured quality.
- S — S — top 10% of the cohort
- A — A — next 20% of the cohort
- B — B — next 30% of the cohort
- C — C — below the B band
Providers
Optional live provider statistics from OpenRouter. Missing values are unknown, not zero.
| Provider | Context length | Price in /1M | Price out /1M | Latency p50 (30m, s) | Throughput p50 (30m, tok/s) | Uptime (30m) |
|---|---|---|---|---|---|---|
| OpenAI | 400,000 | — | — | — | — | 100% |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.