GPT-Image-2.6 vs Nano Banana 2.1: Complete Comparison and Report on Per-Image Pricing, Quality Rankings, GIF and SVG Output, and Editing Controls
GPT-Image-2.6 is the image generation and editing model OpenAI released to its API on November 4, 2026, in two variants: Sunburst, which OpenAI positions for detailed visual work and consistency across edits, and Flare, which it positions for fast iteration. The same release brought ChatGPT Images 2.6 to the ChatGPT app, with animated GIFs, reusable saved reference images, image markup and SVG export. Nano Banana 2.1 (API ID gemini-nano-banana-2.1) is Google's update to Nano Banana 2, documented by Google DeepMind in a model card dated October 6, 2026, built on Gemini 3.6 Flash and listed as a stable model in the Gemini API, where Google calls it the high-efficiency workhorse for image generation and conversational editing.
The two models charge the same headline rate for generated images, $30 per 1M image output tokens, so the real price gap comes from everything around that number. Google publishes exact token counts per image for Nano Banana 2.1 (1,120 tokens at 1K, 1,680 at 2K and 3,780 at 4K), which puts a standard 1K image at $0.0336 and a 4K image at $0.113, with a Batch API that halves both. OpenAI publishes no per-image token counts for GPT-Image-2.6 and lists no Batch price for it, charges $5 per 1M text input tokens against Google's $1.50, and defaults its new complexity control to the highest setting. On quality, GPT-Image-2.6 had not yet been rated by Artificial Analysis when this comparison was prepared, while its predecessor GPT-Image-2.5 Sunburst at max quality leads Nano Banana 2.1 by 47 Elo points in text-to-image and 48 points in editing on that organization's leaderboards, at a measured cost about 6.3 times higher per 1,000 images.
The practical reading is a split by workload. Nano Banana 2.1 is the better choice for high-volume, budget-planned generation, for 4K output, for multi-reference product and character work with up to 14 reference images, and for images that need live Google Search grounding. GPT-Image-2.6 is the choice for teams that need native animated GIF or SVG output, markup-guided edits, and the GPT Image line whose 2.5 version currently tops both Artificial Analysis image leaderboards, accepting that its per-image cost cannot be budgeted from official documents today. Prices in this comparison are as listed on the official OpenAI and Google pricing pages on November 5, 2026.
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GPT-IMAGE-2.6 AND NANO BANANA 2.1 RELEASE TIMELINE AND MODEL IDENTITY.
OpenAI's API changelog records the release on November 4, 2026 in a single entry: GPT-Image-2.6 Sunburst and GPT-Image-2.6 Flare for image generation and editing, together with adjustable complexity, SVG generation and animated GIF generation. The same entry extends markup-guided editing to both GPT Image 2.5 and 2.6. GPT-Image-2.6 is the third OpenAI image generation in seven months: GPT Image 2 arrived on April 21, 2026, and GPT Image 2.5 Sunburst and Flare followed on September 8, 2026 with new xhigh and max quality settings. The announcement reposted in the Announcements section of the OpenAI Developer Community describes 50% faster image generation and says Flare "generates images in as little as six seconds", without stating the baseline for the speed claim.
Nano Banana 2.1 sits inside Google's Gemini 3 family. The DeepMind model card describes it as based on Gemini 3.6 Flash and defers architecture, training data and safety policy to that model's card. Google's own developer documentation calls it "an update to Nano Banana 2", keeping Flash-level speed and cost while improving visual quality, text rendering, multi-turn consistency and Google Search grounding. Google still sells the more expensive Nano Banana Pro (Gemini 3 Pro Image), which its documentation positions as the premium model for the most complex visual tasks, and the cheaper Nano Banana 2 (Gemini 3.1 Flash Image), which remains the free model in the Gemini app.
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Item | GPT-Image-2.6 | Nano Banana 2.1 |
Developer | OpenAI | Google (Google DeepMind) |
API model IDs | gpt-image-2.6-sunburst, gpt-image-2.6-flare | gemini-nano-banana-2.1 |
Release date | November 4, 2026 (API changelog) | Model card dated October 6, 2026 |
Status | Released in the API | Stable in the Gemini API |
Base model | Not disclosed | Gemini 3.6 Flash |
Variants | Sunburst (detailed work), Flare (fast iteration) | One model, three thinking levels |
Direct predecessor | GPT-Image-2.5 (September 8, 2026) | Nano Banana 2 (Gemini 3.1 Flash Image) |
Consumer product | ChatGPT Images 2.6 | Gemini app "Create images" |
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The naming makes like-for-like matching harder than it looks. OpenAI ships two variants at an identical price and separates them by purpose, while Google ships a single model and exposes a thinking setting with minimal, medium and high levels. Neither company maps its variants onto the other's, so this comparison treats Sunburst and Flare as one priced product and notes where the variant matters.
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GPT-IMAGE-2.6 VS NANO BANANA 2.1 API PRICING PER 1M TOKENS.
Both companies bill image generation by tokens. On November 5, 2026 the OpenAI API pricing page and both GPT-Image-2.6 model pages list the same rates for Sunburst and Flare, unchanged from GPT-Image-2.5 at standard rates. The Gemini API pricing page lists Nano Banana 2.1 with no free tier in the API and a Batch tier at half price.
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Price per 1M tokens (USD) | GPT-Image-2.6 Sunburst / Flare | Nano Banana 2.1 standard | Nano Banana 2.1 Batch |
Text input | $5.00 | $1.50 | $0.75 |
Cached text input | $1.25 | Caching not supported | Caching not supported |
Image input | $8.00 | $1.50 | $0.75 |
Cached image input | $2.00 | Caching not supported | Caching not supported |
Text and thinking output | Not applicable (image output only) | $7.50 | $3.75 |
Image output | $30.00 | $30.00 | $15.00 |
Batch rate | No Batch price listed for 2.6 | Not applicable | Listed above |
API free tier | No | No | No |
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The input gap is wide in relative terms and small in absolute terms. A 250-token text prompt costs $0.00125 on GPT-Image-2.6 and $0.000375 on Nano Banana 2.1, a fraction of a cent either way, so prompt length rarely decides the bill. Reference images are a different matter for editing workloads: OpenAI charges $8 per 1M image input tokens against Google's $1.50, more than five times as much, although OpenAI's $2 cached image input rate narrows that gap when the same reference is reused and Google lists no caching for this model.
Two Gemini-specific charges sit on top of image output. Thinking cannot be disabled for Nano Banana 2.1 in the API, and thinking tokens are billed at the $7.50 text-and-thinking output rate; Google's documentation adds that up to two interim "thought images" are generated and not charged. OpenAI's complexity setting, by contrast, carries no separate fee according to its image generation guide, though higher complexity can raise latency.
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PER-IMAGE COST OF NANO BANANA 2.1 AND WHAT OPENAI DOES NOT PUBLISH FOR GPT-IMAGE-2.6.
Google converts its token prices into per-image figures in a footnote on the pricing page. OpenAI's pricing page instead points readers to a calculator in the image generation guide, and the GPT-Image-2.6 model pages list per-1M-token prices only, with no token counts by size or quality. The table shows what each company publishes.
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Output size | Nano Banana 2.1 tokens per image | Nano Banana 2.1 standard | Nano Banana 2.1 Batch | GPT-Image-2.6 per image |
1K | 1,120 | $0.0336 | $0.0168 | Token count not published |
2K | 1,680 | $0.0504 | $0.0252 | Token count not published |
4K | 3,780 | $0.113 | $0.0567 | Token count not published |
0.5K (512 px) | Not supported on 2.1 | Not supported | Not supported | Token count not published |
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Because the image output rate is identical, a GPT-Image-2.6 image costs the same as a Nano Banana 2.1 image only when it uses almost the same number of output tokens. With a 250-token prompt on both sides, the arithmetic gives a break-even of about 1,091 output tokens per GPT-Image-2.6 image against a standard 1K Nano Banana 2.1 image, about 1,651 tokens against 2K and about 3,751 tokens against 4K. Against Nano Banana 2.1 Batch pricing the break-even halves. Any GPT-Image-2.6 image that uses more output tokens than those thresholds costs more than the Google image of the same resolution class.
The only independent per-image measurement available covers the predecessor. Artificial Analysis lists GPT Image 2.5 Sunburst (max) and GPT Image 2.5 Flare (max) at $210.7 per 1,000 images and Nano Banana 2.1 at $33.6 per 1,000 images, both measured at 1024x1024 with the organization's default settings. The Nano Banana figure matches Google's own $0.0336 per 1K image exactly. The GPT Image 2.5 figure reflects the max quality setting used for the leaderboard entries and cannot be carried over to GPT-Image-2.6, whose token use OpenAI has not documented.
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GPT-IMAGE-2.6 AND NANO BANANA 2.1 COST FOR THREE TYPICAL WORKLOADS.
The workloads below use only official figures: Google's per-image token counts and prices, OpenAI's per-1M-token prices, a 250-token text prompt per image, no reference images, and no thinking-token charges, which Google bills separately and which vary by request. For GPT-Image-2.6 the table gives the cost of each 1,000 output tokens per image across the whole job, and the break-even token count at which it matches Google, since a total cannot be stated without an official token count.
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Workload | Nano Banana 2.1 standard | Nano Banana 2.1 Batch | GPT-Image-2.6: cost per 1,000 output tokens per image | GPT-Image-2.6 break-even vs Google standard / Batch |
Product catalog: 10,000 images at 1K | $339.75 | $169.88 | $300.00 (plus $12.50 prompts) | 1,091 / 525 tokens per image |
Social campaign: 5,000 images at 2K | $253.88 | $126.94 | $150.00 (plus $6.25 prompts) | 1,651 / 805 tokens per image |
Print and display: 2,000 images at 4K | $227.55 | $113.78 | $60.00 (plus $2.50 prompts) | 3,751 / 1,855 tokens per image |
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Two conclusions follow from the arithmetic without any estimate of OpenAI's token use. First, Batch is decisive for jobs that can wait: Nano Banana 2.1 halves every row, and OpenAI lists no Batch rate for GPT-Image-2.6 even though it listed one for GPT-Image-2.5, so any pipeline that previously relied on OpenAI's Batch discount loses it by moving to 2.6. Second, for GPT-Image-2.6 to beat Nano Banana 2.1 Batch on a 1K catalog job, each image would need to come in under about 525 output tokens. Teams that need a firm number before switching should run a short test and read the token usage that the API returns for their own prompts, sizes and complexity settings.
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GPT-IMAGE-2.6 VS NANO BANANA 2.1 LIMITS, RESOLUTIONS AND GENERATION CONTROLS.
The two models expose different controls. OpenAI adds a complexity parameter with low, medium and high levels, defaulting to high, separate from the existing quality setting that GPT Image 2.5 extended with xhigh and max. Google exposes a thinking level and resolution tiers, and documents limits more completely.
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Specification | GPT-Image-2.6 | Nano Banana 2.1 |
Inputs | Text and images | Text, images, video and PDF |
Outputs | Images (plus SVG and animated GIF) | Images and text |
Input token limit | Not published on the model pages | 131,072 in the Gemini API (model card states up to 1M) |
Output token limit | Not published | 32,768 in the Gemini API |
Resolutions | Not listed on the 2.6 model pages | 1K, 2K, 4K (512 px not supported) |
Reference images | Not published for 2.6 | Up to 14 (10 objects, 4 characters) |
Generation control | Complexity: low, medium, high (default high); quality setting | Thinking: minimal, medium (default), high; cannot be disabled |
Search grounding | Not offered | Google Web Search and Google Image Search |
Caching | Cached input prices listed | Not supported |
Batch | No 2.6 Batch price listed | Supported at 50% |
Function calling, structured outputs | Not applicable to the Image API | Not supported |
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Google's documentation shows one internal inconsistency that buyers should know about: the Gemini API model page lists a 131,072-token input limit, while the DeepMind model card describes inputs with a context window of up to 1M tokens. The API page governs what a request will accept. On the OpenAI side, the GPT-Image-2.6 model pages state that rate limits depend on the organization's usage tier and publish no figures, and they list no supported sizes, so resolution limits have to be checked in the API reference before a production rollout.
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GIF, SVG AND MARKUP EDITING IN GPT-IMAGE-2.6 COMPARED WITH NANO BANANA 2.1 EDITING.
The clearest functional difference is output format. GPT-Image-2.6 can return an animated GIF or an SVG file directly from the Image API, according to OpenAI's changelog and model pages, and the ChatGPT announcement describes GIFs that can be built from a prompt or an existing image, with adjustable speed, frame-by-frame editing and looping. SVG output gives designers a vector file that scales without loss, which Google does not list for Nano Banana 2.1. Markup-guided editing lets a user draw on an image to show where a change should go; OpenAI added it for both GPT Image 2.5 and 2.6.
Nano Banana 2.1 answers with a different set of strengths. It accepts up to 14 reference images in one request, split between up to 10 object images and up to 4 character images, which suits product photography, catalogs and recurring characters. It supports multi-turn conversational editing chained through previous interactions, can generate images from video inputs, and is the only one of the two that can ground an image in live Google Web Search and Google Image Search results, which Google pitches for infographics and diagrams that depend on current facts.
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Editing and output capability | GPT-Image-2.6 | Nano Banana 2.1 |
Animated GIF output | Yes (Image API and ChatGPT) | No |
SVG (vector) output | Yes | No |
Markup or ink-guided edits | Markup-guided edits | Mask and ink-based editing evaluated in model card |
Multi-reference composition | Saved reference images in ChatGPT; API count not published | Up to 14 reference images |
Conversational multi-turn editing | Not documented specifically for 2.6 | Documented in the Gemini API |
Web-grounded images | No | Web and Image Search grounding |
Provenance marking | Not documented on the 2.6 pages | SynthID on all images; visible watermark in the Gemini app |
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HOW IMAGE QUALITY IS MEASURED FOR GPT-IMAGE-2.6 AND NANO BANANA 2.1.
Image models have no shared vendor benchmark comparable to the coding or reasoning suites used for language models, so the most reliable quality evidence comes from blind human preference arenas. Artificial Analysis runs text-to-image and image-editing arenas (version 2.1 of each) in which voters compare two anonymous outputs for the same prompt, producing Elo ratings with 95% confidence intervals and sample counts. Its figures were read on November 5, 2026.
Vendor evidence is uneven. Google's model card for Nano Banana 2.1 publishes side-by-side human evaluation Elo scores, but only against Google's own models: Nano Banana 2 and Nano Banana Pro. OpenAI published no quality benchmark figures for GPT-Image-2.6 in the materials that could be read for this comparison; the developer announcement shows sample images only, and the OpenAI announcement page and the ChatGPT Images 2.6 system card could not be retrieved during research. No published evaluation from either company compares GPT-Image-2.6 with Nano Banana 2.1 directly.
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INDEPENDENT ARENA RANKINGS FOR NANO BANANA 2.1 AND THE GPT IMAGE FAMILY.
GPT-Image-2.6 did not appear on either Artificial Analysis leaderboard on November 5, 2026, one day after release. The table therefore places Nano Banana 2.1 against the OpenAI models that are rated, which are the direct predecessors of GPT-Image-2.6 and are still sold through the API.
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Model (Artificial Analysis, read Nov 5, 2026) | Text-to-image Elo (rank) | Image editing Elo (rank) | Price per 1,000 images |
GPT Image 2.5 Sunburst (max) | 1223 (1) | 1199 (1) | $210.7 |
GPT Image 2.5 Flare (max) | 1207 (2) | 1183 (3) | $210.7 |
GPT Image 2 (high) | 1189 (3) | 1137 (8) | $211.0 |
Nano Banana 2.1 | 1176 (4) | 1151 (5) | $33.6 |
Nano Banana 2 (Gemini 3.1 Flash Image) | 1140 (7) | 1124 (13) | $67.0 |
Nano Banana Pro (Gemini 3 Pro Image) | 1118 (13) | 1109 (18) | $134.0 |
GPT-Image-2.6 Sunburst / Flare | Not yet rated | Not yet rated | Not measured |
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The confidence intervals matter for reading these gaps. In text-to-image, GPT Image 2.5 Sunburst's interval (1213 to 1233) does not overlap Nano Banana 2.1's (1164 to 1188), so its 47-point lead is a measured difference, and Flare's 31-point lead is also outside the overlap at 1197 to 1217. GPT Image 2 (high), with an interval of 1178 to 1200, overlaps Nano Banana 2.1's range, so its 13-point gap is not a clear separation. In editing, Sunburst's 1188 to 1210 interval clears Nano Banana 2.1's 1138 to 1164. Nano Banana 2.1 delivers that fourth-place text-to-image score at about one sixth of the measured price of the GPT Image 2.5 max entries, and it ranks above both of Google's own older image models, including the more expensive Nano Banana Pro.
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GOOGLE'S OWN NANO BANANA 2.1 EVALUATIONS AND THE MISSING OPENAI FIGURES.
Google's model card adds detail on where Nano Banana 2.1 improved. The scores are side-by-side human preference Elo ratings from Google's internal raters, so they compare Google models with each other and cannot be set against Artificial Analysis Elo numbers. Infographic factuality is scored with an automated rater.
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Google evaluation (model card, Oct 6, 2026) | Nano Banana 2.1 (thinking) | Nano Banana 2.1 (no thinking) | Nano Banana 2 | Nano Banana Pro |
Text-to-image overall preference | 1050 ± 14 | 1015 ± 13 | 990 ± 7 | 935 ± 8 |
Infographic design | 1048 ± 17 | 1001 ± 17 | 961 ± 12 | 912 ± 12 |
Infographic factuality (AutoRater) | 0.521 | 0.328 | 0.179 | 0.265 |
General editing | 1026 ± 12 | 980 ± 15 | 938 ± 11 | 939 ± 10 |
Multi-character consistency | 1106 ± 14 | 1068 ± 14 | 978 ± 10 | 1011 ± 10 |
Mask and ink-based editing | 1049 ± 15 | 1042 ± 16 | 965 ± 12 | 927 ± 12 |
Multi-reference editing | 1066 ± 22 | 1041 ± 20 | 988 ± 13 | 989 ± 12 |
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The card shows thinking doing real work: the infographic factuality score rises from 0.328 without thinking to 0.521 with it, and the overall preference score rises by 35 points. That supports Google's choice to keep thinking on in the API, at the cost of billed thinking tokens. The card also lists known limitations that buyers should weigh: hallucinations, occasional slowness and timeouts, weak rendering of small text, long paragraphs and full-page text, imperfect character consistency, and occasional left-right confusion. OpenAI has published no equivalent list of limitations or evaluation scores for GPT-Image-2.6 in the pages available for this comparison.
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CHATGPT IMAGES 2.6 AND THE GEMINI APP: ACCESS BY PLAN.
Most people will meet these models in an app rather than the API. In the Gemini app, Google's image generation page says free users get Nano Banana 2, while Nano Banana 2.1, which Google calls "our best image generation and editing model", requires a Google AI plan and brings higher generation limits; paid subscribers can also redo an image with Nano Banana Pro. Google's help center gives no daily image counts, describing limits as multiples: Google AI Plus at twice the standard limits, Google AI Pro at four times, and Google AI Ultra at 5 or 20 times the Pro limits depending on the tier.
ChatGPT's pricing page describes image creation per plan in words rather than numbers: limited on Free, more on Go, "more complex and accurate image creation" on Plus with image generation in Thinking mode, and "unlimited and faster image creation" on Pro. OpenAI's materials read for this comparison do not state on which plans ChatGPT Images 2.6 rolled out first. For a fuller breakdown of OpenAI's image features and plan limits, see our guide to ChatGPT image generation, Images 2.6, plan limits and API prices.
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Plan (US price per month) | Image generation as described by the vendor |
ChatGPT Free ($0) | Limited image creation |
ChatGPT Go ($8) | More image creation |
ChatGPT Plus ($20) | More complex and accurate image creation, image generation with Thinking |
ChatGPT Pro (from $100; $100, $200 or $500 tiers) | Unlimited and faster image creation |
Gemini app, no plan ($0) | Nano Banana 2, standard limits |
Google AI Plus ($4.99) | Nano Banana 2.1, 2x standard limits, redo with Nano Banana Pro |
Google AI Pro ($19.99) | Nano Banana 2.1, 4x standard limits, redo with Nano Banana Pro |
Google AI Ultra ($99.99 or $199.99) | Nano Banana 2.1, 5x or 20x the AI Pro limits |
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The cheapest paid route to the newer model is therefore Google AI Plus at $4.99 a month, against ChatGPT Go at $8. At the $20 tier, ChatGPT Plus and Google AI Pro both include their company's current image model, and the deciding factor is whether the user wants GIFs and SVG files from ChatGPT or 4K-capable, search-grounded images and higher multipliers from Gemini. The ChatGPT Plus vs Claude Pro vs Google AI Pro comparison covers the rest of what those $20 plans include.
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SAFETY, PROVENANCE AND EVIDENCE GAPS IN BOTH IMAGE MODELS.
Google documents provenance clearly: every image generated through the Gemini API carries a SynthID watermark, and images made in the Gemini app also carry a visible watermark, with users able to ask the app whether Google AI generated a given image. The DeepMind model card reports human red teaming by specialist teams outside model development, says the model met child safety launch thresholds, and states that content safety performance was similar to or better than Gemini 3 Flash.
OpenAI's safety documentation for this release is the ChatGPT Images 2.6 system card on its Deployment Safety Hub, which could not be retrieved during research, so this comparison does not report its contents. The gaps on the OpenAI side are specific: output tokens per image by size and quality, supported resolutions, the maximum number of reference images in the API, a Batch price, benchmark scores, and the measured latency behind the "50% faster" and "six seconds" claims. On the Google side, the gaps are smaller: the per-request thinking-token count, which affects the bill, and a reconciled input limit between the API page and the model card.
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WHICH TO CHOOSE: GPT-IMAGE-2.6 OR NANO BANANA 2.1 BY WORKLOAD.
The verified facts point to clear choices for most buyers, with the caveat that GPT-Image-2.6's own quality rating and per-image cost are still unpublished. For OpenAI's launch details and the full GPT-Image-2.6 price list, see our report on GPT-Image-2.6 pricing and the end of the Batch discount.
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Workload | Recommended | Reason |
Large catalogs and overnight jobs | Nano Banana 2.1 Batch | $0.0168 per 1K image; no 2.6 Batch price listed |
4K print and display assets | Nano Banana 2.1 | 4K documented at $0.113 per image standard |
Animated GIFs for social and product demos | GPT-Image-2.6 Flare | Native GIF output; Flare built for fast iteration |
Logos, icons and scalable graphics | GPT-Image-2.6 | Only model of the two with SVG output |
Precise edits on existing images | GPT-Image-2.6 Sunburst | Markup-guided edits, positioned for editing precision |
Product shots with many references | Nano Banana 2.1 | Up to 10 object and 4 character references |
Infographics needing current facts | Nano Banana 2.1 | Web and Image Search grounding, thinking raises factuality |
Highest rated image quality today | GPT Image 2.5 Sunburst (max) | Rank 1 on both Artificial Analysis arenas pending a 2.6 rating |
Lowest-cost paid consumer plan | Google AI Plus ($4.99) | Includes Nano Banana 2.1 |
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Developers who can budget per image and run work in batches will spend far less on Nano Banana 2.1, which ranks fourth in Artificial Analysis text-to-image voting at $33.6 per 1,000 images and costs half that through Batch. Teams whose output has to be a GIF, a vector file or a carefully marked-up edit have only one option of the two in GPT-Image-2.6, and they should test Sunburst for edit precision and Flare for speed while logging the output tokens each image consumes, because that number, not the shared $30 rate, decides what GPT-Image-2.6 costs.
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