Only one of the two image models can produce an animated file, and it is not the one with the better benchmark. OpenAI’s Images 2.6, released November 4, 2026, added one-step generation of animated GIFs, SVG export and markup-driven edits, while Google’s Nano Banana 2.1, available since October 6, 2026, added none of those. What it added was a price cut and a resolution ladder: named output tiers at 1K, 2K and 4K, up to 14 reference images and aspect ratios up to 8:1. The choice between the two is therefore not a quality competition; it is a question of what form your deliverable takes.
A team making animated stickers, looping social posts or short explainer frames has one candidate here. A team producing print-resolution product visuals or wide banner formats has the other. Only after that does the benchmark count — and when it does, the numbers for the newcomer are less flattering than launch coverage implies.
Where the benchmarks actually stand
Artificial Analysis, read on November 5, 2026, puts Nano Banana 2.1 at 1,151 Elo over 6,937 samples on AA-Image-Editing v2.1, while GPT Image 2.5 Sunburst (max) sits at 1,199 over 18,300 — and Images 2.6 has no row at all. On AA-Image-T2I v2.1, Nano Banana 2.1 scores 1,176 over 6,274 samples for fourth place, against GPT Image 2.5 Sunburst at 1,224 in first and GPT Image 2 (high) at 1,188 in third; Images 2.6 again has no row. Google’s own figures — 1,050 Elo for Nano Banana 2.1 in Thinking mode versus 990 for Nano Banana 2 and 935 for Nano Banana Pro — are manufacturer-stated and on a different scale from the AA boards, so the two should not be mixed.
The resolution ladder in detail
Here Nano Banana 2.1 is structurally ahead, and not marginally. It offers 1K, 2K and 4K as named, priced output options, while Images 2.6 does not offer resolution that way: output size derives from the token budget, and OpenAI publishes no 2K or 4K tier with a stated price. Nano Banana 2.1 supports aspect ratios up to 8:1 in both directions, so a 3,200 × 400 leaderboard banner is a full-fledged request rather than a crop; Images 2.6 inherits whatever the 2.5 generation supported, which OpenAI does not document as an explicit range. On reference images, Nano Banana 2.1 allows up to 14, structured as four characters and ten objects, while Images 2.6 adds reusable stored reference images in ChatGPT and accepts image inputs at both endpoints but publishes no maximum. Nano Banana 2.1 runs roughly 131K input and 32K output tokens with Search Grounding available; Images 2.6 publishes no context window. Editing control is closely related on both: mask-based local edits and three thinking levels on one side, markup-driven edits plus a new complexity parameter on the other.
What can be said with certainty is that Nano Banana 2.1 is a mid-field model on independent image-editing measurements and a strong fourth on text-to-image, and that its predecessor sits 48 points lower on image editing. Whether Images 2.6 is better is not yet a fact: the best available proxy is that OpenAI’s previous generation leads both rankings by a margin wider than the confidence intervals, and that the stated improvement of 2.6 lies in consistency across a sequence of edits rather than a per-image quality jump. Neither company has published head-to-head results for Images 2.6 on the independent boards, so that question remains open.
