Gemini 3 Pro Image (Nano Banana Pro) is Google DeepMind's flagship image model for teams that need legible on-image text and factual accuracy over pure painterly style. It replaces manual infographic design work with Search-grounded generation, runs on a 65,536-token context window, and holds up to 5 subjects consistent in one scene.
Nano Banana Pro, officially Gemini 3 Pro Image, is Google DeepMind's flagship AI image model, combining Gemini 3's multimodal reasoning with a 32,768-token maximum output for graphics generation. It renders true 4K images, holds identity consistent across 5 named subjects per scene, and grounds infographics in live Google Search results, a capability most rival image models lack.
Where it sits
- $4.50/M$ per 1M tokensBlended price (3:1)Lower is better#44 / 64peer median $1.70/Mvendor price, checked by HokAI
- --tokens/sOutput speedHigher is better-- / 39peer median 90 tok/scited: Artificial Analysis
- --% solvedSWE-bench VerifiedHigher is better-- / 28peer median 78.3%per source, see benchmark scores
- --% correctGPQA DiamondHigher is better-- / 44peer median 88.3%per source, see benchmark scores
Pricier than 68% of the 64 GA models with a published price, and one of 65 whose vendor states it does not train on customer data.
Ranks are against GA models on HokAI that publish the same figure; ties share a rank.
Provider: Google DeepMind · Family: Gemini 3
More about Google DeepMind on HokAI
Context window: 65,536 tokens · Max output: 32,768
Input modalities: text, image · Output: image, text
About Gemini 3 Pro Image
Gemini 3 Pro Image, nicknamed Nano Banana Pro internally at Google DeepMind, released in November 2025, folds Gemini 3's multimodal reasoning directly into its image pipeline rather than bolting a diffusion head onto a separate model. It followed the original Nano Banana (Gemini 2.5 Flash Image) by several months and was positioned as Google's flagship answer to OpenAI's GPT Image line. Where the first Nano Banana targeted speed and cost, Pro targets accuracy: legible long-form text in generated graphics, factual grounding via live Google Search, and consistent multi-subject identity across a shoot rather than a single frame.
On independent blind-vote testing, Nano Banana Pro trails the market leader on pure text rendering while ranking above Midjourney, alongside GPT Image 2 and FLUX.2, on prompt-accuracy dimensions such as color specification, spatial relationships, object counts, and attribute bindings. Reviewers rate it ahead of Midjourney on factual accuracy and Google Workspace integration, while Midjourney keeps the edge on pure aesthetic taste.
The model runs on a 65,536 token context window with a maximum output of 32,768 tokens, per Google's live API documentation, small next to Gemini 3 Pro's 1M-token text context because the image path consumes tokens differently than plain text. Integrators porting code from a text-only Gemini 3 integration need to raise the output limit explicitly to reach full output resolution, since the API's own default sits well under that ceiling.
Nano Banana Pro's defining feature is identity preservation. An internal identity-latent mechanism encodes facial markers, jawline, eye spacing, distinguishing marks, into a stable representation so the same character can be regenerated in new poses without drifting, holding consistency across up to 5 named subjects in one scene.
Pricing follows Gemini 3 Pro's token-metered structure rather than a flat per-image fee, and image output tokens carry a much higher per-token rate than text output. A Batch API tier cuts image cost roughly in half for asynchronous workloads that can tolerate a delayed response.
Deployment options span the Gemini API for developer access, Google AI Studio for prototyping, and Vertex AI for enterprise integration with IAM-based auth and regional deployment; a single Vertex project supports up to 30,000 online inference requests per minute per region, though the image-specific images-per-minute quota is a separate, lower ceiling than the general request quota, commonly cited in the 10-100 images/minute range depending on billing tier. SDKs are available for Python, JavaScript, Java, and Go.
Every image Nano Banana Pro generates is watermarked with SynthID, a signal designed to survive common transformations like compression, resizing, and format conversion, and Google additionally embeds C2PA content-credential metadata on images produced through the Gemini app, Vertex AI, and Google Ads, so downstream tools can flag the image as AI-generated. Built-in safety filters run at multiple stages of the generation pipeline; Google has not published a dedicated public system card specific to this image model separate from the general Gemini 3 model documentation.
Nano Banana Pro fits teams doing graphic design, marketing asset production, product mockups, and data-driven infographics where text legibility and factual grounding matter more than raw painterly aesthetics. Teams whose top priority is perfect long-form text rendering, dense UI mockups, multi-line signage, code screenshots, get more accurate results from GPT Image 2. Teams optimizing purely for artistic taste and mood still tend to prefer Midjourney's output, and any project running more than 5 named characters in a single scene will hit the identity-latent mechanism's documented failure mode, where traits blend and faces generalize.
Pricing
Text input is billed at $2.00 per million tokens, text and thinking output at $12.00 per million. Image output tokens are billed separately at $120 per million, about $0.134 for a 1K/2K image (1,120 tokens) and about $0.24 for a 4K image (2,000 tokens). The Batch API cuts image cost roughly in half, to about $0.067 per 1K/2K image, for asynchronous jobs that can wait up to 24 hours.
What a real job costs
| Job | Input | Output | Total |
|---|---|---|---|
| Summarise a 20-page PDF | $0.060 | $0.012 | $0.072 |
| Support reply | $0.0040 | $0.0036 | $0.0076 |
| One coding agent run | $0.400 | $0.240 | $0.640 |
Budgets: 20-page PDF = 30k in / 1k out · Support reply = 2k in / 300 out · Coding agent run = 200k in / 20k out. Computed from the vendor's per-token prices at render time; cached-input discounts are not applied.
Key Features
- 5-Subject Identity Preservation: An identity-latent mechanism encodes facial markers into a stable representation so up to 5 named characters stay visually consistent across a full generation session.
- 4K Output: Generates images at true 4K resolution (4096x4096), with image output tokens priced per generated token rather than a flat per-image fee.
- Google Search Grounding: Pulls live factual data via Google Search to produce accurate infographics, diagrams, and data visualizations rather than relying only on training-time knowledge.
- SynthID + C2PA Provenance: The model embeds an imperceptible SynthID watermark in every output that survives compression and resizing, and adds C2PA content-credential metadata on select first-party surfaces for provenance tracking.
- Localized Partial-Denoising Edits: Mask a specific region of an image, an outfit color, a prop, and regenerate only that area while the rest of the image, including the identity fingerprint, stays untouched.
Pros
- Beats Midjourney and matches GPT Image 2 on Artificial Analysis prompt-accuracy testing for color, spatial relationships, object counts, and attribute bindings.
- Only major image model with native Google Search grounding for factually current infographics and data visuals.
- True 4K output with automatic SynthID watermarking and C2PA provenance metadata built in, no separate tooling required.
Cons
- Trails GPT Image 2 on text-rendering accuracy in the same July 2026 blind-vote Artificial Analysis testing.
- Identity preservation degrades past 5 named subjects in one scene, a hard architectural ceiling, not a prompting fix.
- Image output tokens cost far more than text output tokens, making high-volume 4K generation pricier than it first looks from text-model pricing.
Benchmarks
- Text Rendering Accuracy Pct: 94 vendor-reported · 01 Jul 2026
- AA Image Arena Elo: 1290 cited: Artificial Analysis
A benchmark is an exam, not the job. Scores transfer unevenly between tasks, so weigh the one closest to your workload and read every figure with its source.
Frequently Asked Questions
How much do you pay for Gemini 3 Pro Image?
Gemini 3 Pro Image charges $2.00 per million text-input tokens and $12.00 per million for text or thinking output, matching Gemini 3 Pro's standard rates. Generated image tokens cost far more, $120 per million, which lands around $0.134 for a 1K/2K image and $0.24 for a 4K image. Running requests through the async Batch API cuts that roughly in half. A limited free tier is available through the Gemini app and Google AI Studio, though Google has not published an exact daily quota.
Is Gemini 3 Pro Image free to use?
Yes, in a limited form. The Gemini app and Google AI Studio both include a capped number of free daily image generations, though Google has not published the exact quota and it varies by account tier. Heavier or production use moves to the metered Gemini API or Vertex AI, billed per token rather than a flat monthly fee. There is no standalone free tier on the Vertex AI enterprise path.
What should you use instead of Gemini 3 Pro Image?
GPT Image 2 from OpenAI is the closest rival, and it wins on pure text-rendering accuracy and multilingual scripts like CJK and Arabic. FLUX.2 from Black Forest Labs leads independent photorealism rankings and offers open-weight variants for self-hosted deployment, an option Gemini 3 Pro Image does not have. Midjourney remains the pick for reviewers who prioritize painterly aesthetic taste over factual accuracy or legible text.
Is Gemini 3 Pro Image better than GPT Image 2?
On the Artificial Analysis Image Arena as of July 2026, GPT Image 2 held the top blind-vote Elo (1,339) and led text-rendering accuracy at about 99% versus Gemini 3 Pro Image's 94%. Gemini 3 Pro Image's advantage is native Google Search grounding, which lets it pull live facts into infographics and data visuals, a capability GPT Image 2 lacks. For dense, multi-line text like UI mockups or signage, GPT Image 2 is the more accurate choice; for factually current, Search-grounded graphics, Gemini 3 Pro Image wins.
How long does it take to get going with Gemini 3 Pro Image?
Grab an API key from Google AI Studio, or set up a Vertex AI project with IAM credentials for enterprise use, then call the model through the Python, JavaScript, Java, or Go SDK. Explicitly raise maxOutputTokens toward the 32K output ceiling; the API defaults to 8,192, which silently truncates full-resolution image output. Google AI Studio is the fastest no-code way to test a prompt before wiring up the API.
Top Alternatives
- GPT Image 2: Pick GPT Image 2 for pixel-perfect dense text and multilingual CJK or Arabic rendering; pick Gemini 3 Pro Image for Search-grounded factual graphics and 5-subject identity consistency.
- FLUX.2: Pick FLUX.2 for photorealistic product shots and self-hosted open-weight deployment; pick Gemini 3 Pro Image for Search-grounded infographics and native Google Workspace integration.