- - 🏢 OpenAI image generation and editing model, released November 2026.
- - 🆕 Successor to GPT Image 2.5 Flare in the Flare line.
- - 👁️ Accepts both text and image inputs to produce images.
- - 🔧 Supports a complexity setting and markup-guided edits.
- - 🆕 Can output SVG and animated GIF formats.
- - 💬 Documented for interactive generation and editing workflows.
- - 🎯 Paired with a Sunburst tier in the same 2.6 generation.
- - 🔒 Closed-weights, API-only via OpenAI's images endpoints.
OpenAI is an American artificial intelligence research organization headquartered in San Francisco, structured as both a for-profit public benefit corporation and a nonprofit foundation. The lab developed the GPT family of large language models, the DALL-E image generation…
Explore 33 more models by OpenAI →GPT Image 2.6 Flare is OpenAI's November 2026 image model, part of the GPT Image 2.6 generation that also includes GPT Image 2.6 Sunburst. According to OpenAI's API documentation, it generates and edits images from text and image inputs, and is intended for interactive image generation and editing workflows. Dedicated editing entries, GPT Image 2.6 Flare Edit and GPT Image 2.6 Sunburst Edit, expose the same family for inpainting-style tasks.
Relative to its direct predecessor GPT Image 2.5 Flare — and the earlier GPT Image 2 and GPT Image 1.5 — the clearest documented changes are in controllability and output formats. OpenAI's docs for this model highlight a complexity setting, markup-guided edits, SVG output and animated GIF output, controls that were not part of the documented surface for earlier GPT Image releases. Markup-guided editing in particular lets a user annotate a reference image rather than describing every change in prose.
Within the 2.6 generation, Flare and Sunburst are offered as two distinct model identifiers through OpenAI's image generation and edit endpoints, so applications can pick a tier per workload rather than switching families. Because the weights are closed and OpenAI has not published head-to-head quality figures for this release, no benchmark numbers are quoted here; capability claims above are limited to the provider's own documentation.
This About section is AI-generated from public sources via VeniceStats + Venice inference, with no human editing. It may contain inaccuracies.
Daily usage · GPT Image 2.6 Flare · Organic sellers
| Seller | Health | Rep↓ | Sales | Images | Routing | $ / img | Save |
|---|---|---|---|---|---|---|---|
| ▲ Apex Ant 0x73b4…e736 | — | 81.71 | — | — | #2 | $0.045 | −50% |
| Edith AI 0xb269…b1a6 | — | 0.00 | — | — | gated | $0.0054 | −94% |
| Venice.ai Proxy 0x1f22…18c9 | failingupstream error · 3.7 h · stale | 86.84 | $0.05 | 1 | #1 | $0.0055 | −94% |
"Best price" and the seller table are live Antseed catalog data (advertised $/1M tokens — or $ per generated image for unit-billed image models — not settled amounts). Reputation = buyer trust score (0-100, the Antseed SDK's own formula). "Routing" = the SDK's default buyer routing (what the VPR desktop app ships with): a trust ≥ 60 gate on the effective reputation, then cheapest-first among routable sellers; live failover state (per-peer cooldowns) is buyer-side runtime and not included. Model knowledge (TLDR, provider, About) via the VeniceStats enrichment layer. Advertised catalog, not the model used in any specific purchase. "Usage on Antseed" counts only settlements whose buyers share the per-model split on-chain (metadata v2/v3, opt-in), so every usage figure is a lower bound.