Images 2.6 vs Nano Banana 2.1: Animation Against a Resolution Ladder

Only one of these two models can produce a moving file, and it is not the one with the better benchmark. Images 2.6, OpenAI's 4 November 2026 release — GPT-Image-2.6 Sunburst for quality, GPT-Image-2.6 Flare for speed — added single-step animated GIF generation, SVG export and markup-guided edits. Nano Banana 2.1, Google's model built on Gemini 3.6 Flash and generally available since 6 October 2026, added none of that. 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 as extreme as 8:1.

The choice between them is therefore not a quality contest. It is a question about what shape your deliverable takes. A team making animated stickers, looping social posts or short explainer frames has one candidate here. A team making print-resolution product visuals or wide banner formats has the other. Only after that does the benchmark matter, and when it does, the numbers are less flattering to the newcomer than the launch coverage implies.

Where the benchmarks actually stand

Artificial Analysis, read on 5 November 2026:

• AA-Image-Editing v2.1 — Nano Banana 2.1 at 1,151 Elo across 6,937 samples; GPT Image 2.5 Sunburst (max) at 1,199 across 18,300. Images 2.6 has no row.

• AA-Image-T2I v2.1 — Nano Banana 2.1 at 1,176 across 6,274 samples, ranked fourth; GPT Image 2.5 Sunburst at 1,224, ranked first, with GPT Image 2 (high) at 1,188 in third. Images 2.6 has no row here either.

• Google's own figures — 1,050 Elo for Nano Banana 2.1 in Thinking mode against 990 for Nano Banana 2 and 935 for Nano Banana Pro. Vendor-reported and on a different scale from the AA boards; do not mix them.

So the two things you can say with confidence are that Nano Banana 2.1 is a mid-pack model on independent editing measures and a strong fourth on text-to-image, and that its predecessor sits 48 points lower on editing. The third thing — whether Images 2.6 is better — is not yet a fact. The closest proxy is that OpenAI's previous generation leads both boards by a margin wider than the confidence intervals, and that 2.6's stated improvement is consistency across a sequence of edits rather than a jump in per-image quality.

The resolution ladder, in detail

This is where Nano Banana 2.1 is structurally ahead, and it is not a marginal advantage.

• Output tiers — 1K, 2K and 4K as named, priced options. Images 2.6 does not sell resolution that way; output size is implied by the token budget, and OpenAI publishes no 2K or 4K tier with a price attached.

• Aspect ratios — up to 8:1 in either direction on Nano Banana 2.1. A 3,200 × 400 leaderboard banner is a first-class request, not a crop. Images 2.6 inherits whatever the 2.5 generation supported, which OpenAI does not document as an explicit range.

• Reference images — up to 14 on Nano Banana 2.1, structured as four characters and ten objects. Images 2.6 adds reusable saved reference images in ChatGPT and accepts image input on both endpoints, but no maximum count is published.

• Context — roughly 131K input and 32K output tokens on Nano Banana 2.1, with search grounding available. Images 2.6 does not publish a context window; its model pages list text and image input and image output and nothing further.

• Editing control — mask-based local edits and three thinking levels on Nano Banana 2.1; markup-guided edits plus the new complexity parameter on Images 2.6. These are close cousins: both let you tell the model how much deliberation to spend and where to apply it.

The practical consequence is that Nano Banana 2.1 is the only one of the two you can specify in a contract. "Produce a 4K image at 16:9, billed at the published 4K rate" is a sentence you can write into a statement of work. The equivalent sentence for Images 2.6 is a token budget and an estimate.

The price comparison has one genuine ambiguity

Nano Banana 2.1 output is billed at $30 per million image tokens, down from $60 for Nano Banana 2 — the halving that made the launch newsworthy. In practice that is $0.0336 at 1K and $0.0504 at 2K. Batch pricing halves both again.

At 4K the sources disagree, and the disagreement is not cosmetic. Some outlets report $0.0756; others, including TechRepublic and heise, report $0.113, on the grounds that the 4K token count rose to roughly 3,780. Both figures appear in reputable write-ups of the same launch. If you are budgeting a 4K pipeline, that is a 50% swing on your largest line item, and it is worth resolving against Google's own rate card rather than any secondary source — including this one.

One thing that did move in the wrong direction: Nano Banana 2.1 raised input pricing to $1.50 per million tokens from $0.50, with text and thinking output at $7.50 per million. For edit-heavy workflows that pass large source images, the output-side cut can be partly eaten by the input-side rise, and the net saving is smaller than the headline.

Images 2.6, for comparison, is $5 per million text input, $8 per million image input and $30 per million image output. Batch rates are half those figures, but the 2.6 model pages list /v1/batch as not supported, so greenfield batch pricing is not available for this model yet. Per image the estimates land near $0.006 at low quality, $0.013 at medium, $0.053 at high and $0.21 at max — derived from token budgets carried over from the 2.5 generation, not published 2.6 figures.

Stack the two up at the settings that matter and the picture is not the one the launch coverage painted. At 2K, Nano Banana 2.1 costs $0.0504. Images 2.6 at high quality costs about $0.053. Those are within five percent of each other. Google's price cut did not make Nano Banana 2.1 dramatically cheaper than a token-metered competitor; it made it roughly equal to one running at high quality, and cheaper only against xhigh and max.

That equality is the argument for keeping both reachable from one place. OrcaRouter passes provider list price through at 0% markup, which means a vendor price cut is live on our side the day it lands upstream rather than whenever a reseller reprices — and a routing rule can send traffic to whichever of two near-identical costs is healthy. Google has already deprecated one image model this quarter; the ability to move a model string rather than rebuild an integration is worth more than a fraction of a cent.

The migration Google actually recommends

Something subtler than a shutdown is happening on the Google side. The live image-generation documentation, read on 5 November 2026, lists five models in the Nano Banana family — Nano Banana 2.1 (gemini-nano-banana-2.1), Nano Banana 2 Lite (gemini-3.1-flash-lite-image), Nano Banana 2 (gemini-3.1-flash-image), Nano Banana Pro (gemini-3-pro-image) and the legacy Gemini 2.5 Flash Image model. Nano Banana 2 is still listed and still callable, with no shutdown date against it; the documentation's guidance is that all new projects should use Nano Banana 2.1, which is a recommendation rather than a deprecation.

Two details make that recommendation less clean than it looks. Nano Banana 2 Lite is now the fastest and cheapest tier, described as intended for velocity and scale rather than multiple references or multi-turn editing — so "migrate to 2.1" is not automatically the cost-optimal move for high-volume simple generation. And Google's own deprecation page is explicit that the dates it publishes are "the earliest possible dates on which a model might be retired," with notice promised before an actual shutdown. A model with no date at all is not a model on its way out.

The commercial wrinkle is real, though. In the Gemini app, free users still get Nano Banana 2 while 2.1 requires an AI Pro, Plus or Ultra plan, and there is no free tier for 2.1 on the API. A team that prototyped on the free tier and is now moving to production will find its test environment and its production environment are no longer the same model — a reliable source of "it worked in staging" failures.

What the documentation says, and what it does not

Worth stating plainly, because it is the difference between a product and an announcement.

Images 2.6: the model pages for gpt-image-2.6-sunburst are live on OpenAI's developer documentation, with pricing, supported endpoints — image generation and image edits only, not chat completions or responses — and modality declarations. The pages point to the image guide for "the complexity setting, markup-guided edits, SVG output, and animated GIF output." They do not state resolution options, a reference-image maximum, GIF length, frame rate or file size limits.

Nano Banana 2.1: fully documented, with named tiers, published per-image prices, a reference-image budget, an aspect-ratio range and a context window. It is a spec sheet you can design against.

The gap matters for the GIF feature specifically. Animated GIF generation is shipped and working in ChatGPT. Whether the /v1/images/generations endpoint returns an animated file is unconfirmed by any public test, and OpenAI's own documentation describes the feature in a guide rather than in the endpoint reference. If animation is the reason you are considering Images 2.6, verify that on a real API call before you design around it.

Choosing between them

Take Nano Banana 2.1 if your deliverables are print-resolution or unusually proportioned, if you need more than a dozen references in a single call, if you need a documented spec sheet to write into a contract, or if you are migrating off the deprecated Nano Banana 2 and want the shortest path — it is the sanctioned target and the pricing is published at every tier.

Take Images 2.6 if your deliverables move. The GIF pipeline is the one capability in this comparison with no counterpart on the Google side, and it is not a novelty: the ability to animate an uploaded photograph with per-frame editing and speed control replaces a video-generation step for a whole class of short-form work. SVG export is the second capability with no counterpart, and for icon and logo systems it is decisive. Markup-guided editing is a third, and it is a genuinely better interaction than drawing a mask for some kinds of change.

If neither description fits — if you are producing static 2K images at volume and just want the better model — the answer is that the evidence does not exist yet. GPT Image 2.5 Sunburst leads Nano Banana 2.1 by 48 points on editing and 48 on text-to-image, both outside the confidence intervals, and Images 2.6 has not been scored. Waiting two weeks for an Elo row costs nothing and is likely to cost you nothing in opportunity either, because neither model is going to be withdrawn before then.