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Compare Gemini Nano Banana 2.1

Gemini Nano Banana 2.1’s pricing, architecture, and capability ratings side by side with up to three other AI image-generation models. Pick models below — your selection is saved in the URL and is shareable. Gemini Nano Banana 2.1 full profile ↗

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Gemini Nano Banana 2.1's pricing, architecture, and capability ratings side by side with up to three other AI image-generation models. Add or remove comparison models with the selector below — the selection is saved in the page URL, so a specific comparison is shareable.

Is Gemini Nano Banana 2.1 or Gemini 3.1 Flash Image cheaper?

Gemini Nano Banana 2.1 is cheaper: Gemini Nano Banana 2.1 is from $0.0336 / image, Gemini 3.1 Flash Image is from $0.045 / image.

How does Gemini Nano Banana 2.1 compare to Gemini 3.1 Flash Image?

Modelglass rates both models across 7 capability dimensions. They rate evenly on the dimensions where both have profile data.

Compare with (up to 3)

	Gemini Nano Banana 2.1 base	Adobe Firefly Image 3	Animagine XL 3.1	DALL·E 3 (deprecated)	FLUX 1.1 [pro]	FLUX.1 [dev]	FLUX.1 [pro]	FLUX.1 [schnell]	FLUX.1 Kontext	Gemini 2.5 Flash Image	Gemini 3 Pro Image	Gemini 3.1 Flash Image (deprecated)	Gemini 3.1 Flash Lite Image	GPT Image 1 (deprecated)	GPT Image 2	Hunyuan-DiT v1.1 (Distilled)	HunyuanImage 3.0	Ideogram 2.0	Ideogram 3.0	Imagen 4 (retired)	Krea-2-Turbo	Leonardo Phoenix	Luma Uni-1.1	Midjourney v7	Recraft V3	Runway Gen-4 Image	Seedream 4.0	Seedream 4.5	Seedream 5.0 Pro	Stable Diffusion 3.5 Large	Stable Diffusion 3.5 Large Turbo	Stable Diffusion 3.5 Medium	Stable Diffusion XL 1.0	Stable Image Core	Stable Image Ultra	Z-Image-Turbo
Price	from $0.0336 / image	$0.02 / image	$0.0037 / image	from $0.04 / image	from $0.04 / megapixel	from $0.025 / megapixel	$0.055 / image	from $0.003 / megapixel	from $0.04 / image	$0.039 / image	from $0.134 / image	from $0.045 / image	$0.0336 / image	from $0.011 / image	from $0.006 / image	$0.000975 / second	$0.10 / megapixel	from $0.05 / image	from $0.06 / image	Retired · last price $0.02 / image	$0.008 / megapixel	$0.00257 / credit	from $0.0404 / image	from $10.00 / month	from $0.04 / image	$0.01 / credit	$0.03 / image	$0.04 / image	from $0.045 / image	$0.065 / image	$0.04 / image	$0.035 / image	$0.0014 / second	$0.03 / image	$0.08 / image	$0.005 / image
Creator	Google DeepMind	Adobe	cagliostrolab	OpenAI	Black Forest Labs	Black Forest Labs	Black Forest Labs	Black Forest Labs	Black Forest Labs	Google DeepMind	Google DeepMind	Google DeepMind	Google DeepMind	OpenAI	OpenAI	Tencent-Hunyuan	Tencent	Ideogram	Ideogram	Google DeepMind	Krea	Leonardo.Ai	Luma AI	Midjourney	Recraft	Runway	ByteDance Seed	ByteDance Seed	ByteDance Seed	Stability AI	Stability AI	Stability AI	Stability AI	Stability AI	Stability AI	Alibaba Tongyi Lab
Released	2026-10	2024-04	2024-03	2023-10	2024-10	2024-08	2024-08	2024-08	2025-05	2025-08	2025-11	2026-02	2026-06	2025-04	2026-04	2024-06	2025-09	2024-08	2025-03	2025-05	2026-06	2024-08	2026-05	2025-04	2024-10	2025-03	2025-09	2025-12	2026-07	2024-10	2024-10	2024-10	2023-07	2024-10	2024-10	2025
Generation	Current	Current	Previous	Current	Current	Current	Previous	Current	Previous	Previous	Current	Current	Current	Previous	Current	Current	Current	Previous	Current	Previous	Current	Current	Current	Current	Current	Current	Previous	Current	Current	Current	Current	Current	Previous	Current	Current	Current
Architecture	—	Latent diffusion	Latent diffusion	Latent diffusion	Flow matching / rectified flow	Flow matching / rectified flow	Flow matching / rectified flow	Flow matching / rectified flow	Flow matching / rectified flow	Diffusion transformer (DiT)	Diffusion transformer (DiT)	Diffusion transformer (DiT)	—	Autoregressive	Autoregressive	Diffusion transformer (DiT)	Autoregressive	Latent diffusion	Diffusion transformer (DiT)	Latent diffusion	Diffusion transformer (DiT)	Latent diffusion	Autoregressive	Latent diffusion	Latent diffusion	Diffusion transformer (DiT)	Diffusion transformer (DiT)	Diffusion transformer (DiT)	Diffusion transformer (DiT)	Flow matching / rectified flow	Flow matching / rectified flow	Flow matching / rectified flow	Latent diffusion	—	—	Diffusion transformer (DiT)
	—	Strong	Moderate	Strong	Strong	Strong	Strong	Moderate	Strong	Unknown	Strong	Strong	—	Strong	Strong	Moderate	Strong	Strong	Strong	Strong	Unknown	Strong	Strong	Moderate	Strong	Moderate	Unknown	Strong	Strong	Strong	Moderate	Moderate	Moderate	—	—	Moderate
How faithfully the image reflects everything the prompt asked for — objects, attributes, relationships, and intent. The single most important axis for most production use, and what alignment benchmarks (GenEval, DPG-Bench, CLIPScore) try to measure. What each rating means here Strong Reliably produces outputs that match complex, specific prompts — including spatial relationships, counts, and fine detail. Moderate Handles straightforward prompts well, but drops or reinterprets details as requests get longer or more specific. Weak Captures the general idea but frequently ignores or invents elements; expect to fight the prompt. Variable Adherence swings with prompt phrasing, guidance settings, or seed — sometimes precise, sometimes loose.
	—	Strong	Weak	Moderate	Strong	Strong	Strong	Moderate	Unknown	Unknown	Unknown	Unknown	—	Strong	Strong	Moderate	Strong	Moderate	Strong	Strong	Unknown	Strong	Unknown	Strong	Moderate	Strong	Unknown	Unknown	Strong	Strong	Strong	Moderate	Strong	—	—	Strong
How convincingly the model renders real-world scenes, lighting, skin, and materials. Distinct from "looks nice" — a model can be highly aesthetic but stylised rather than photoreal. What each rating means here Strong Renders convincing real-world scenes — believable lighting, skin, and materials that hold up to scrutiny. Moderate Looks realistic at a glance but shows tells on close inspection (hands, textures, reflections). Weak Outputs read as obviously synthetic or stylised rather than photographic. Variable Realism depends heavily on the subject and prompt — strong for some scenes, artificial for others.
	—	Strong	Strong	Strong	Strong	Strong	Strong	Moderate	Unknown	Unknown	Unknown	Unknown	—	Strong	Strong	—	Strong	Strong	Strong	Strong	Moderate	Strong	Unknown	Strong	Strong	Strong	Unknown	Unknown	Unknown	Strong	Strong	—	Strong	—	—	Moderate
The breadth of styles a model can produce (illustration, painting, 3D, anime, graphic design) and how well it follows style instructions. A wide ecosystem of fine-tunes/LoRAs effectively extends this axis. What each rating means here Strong Fluently spans many styles (illustration, painting, 3D, anime, graphic design) and follows style direction closely. Moderate Covers common styles competently but has a clear default look it tends to fall back to. Weak Locked to a narrow aesthetic; style instructions have limited effect. Variable Range expands sharply with fine-tunes/LoRAs — base model is narrower than the wider ecosystem.
	—	Moderate	Weak	Strong	Strong	Moderate	Strong	Moderate	Strong	Unknown	Strong	Strong	—	Strong	Strong	Strong	Strong	Strong	Strong	Strong	Unknown	Moderate	Unknown	Moderate	Strong	Moderate	Strong	Strong	Strong	Strong	Moderate	Moderate	Weak	—	—	Strong
The ability to render legible, correctly-spelled text inside the image (signs, logos, labels). A long-standing weak spot for diffusion models; newer transformer-backbone models are markedly better. What each rating means here Strong Renders legible, correctly-spelled text — short signs, logos, and labels come out clean. Moderate Manages short words but garbles longer strings, spacing, or unusual fonts. Weak Text is typically misspelled or illegible; treat it as decorative only. Variable Quality drops fast as the string gets longer — fine for a word, unreliable for a sentence.
	—	Moderate	Moderate	Strong	Strong	Strong	Strong	Moderate	Strong	Unknown	Moderate	Unknown	—	Strong	Strong	—	Strong	Moderate	Moderate	Strong	Unknown	Moderate	Strong	Moderate	Moderate	Moderate	Strong	Strong	Strong	Strong	Moderate	Moderate	Moderate	—	—	Moderate
Getting multi-object scenes right: correct counts, spatial relationships ("A on top of B"), and binding the right attribute to the right object (the "red cube, blue sphere" problem). Measured by GenEval and T2I-CompBench. What each rating means here Strong Gets multi-object scenes right: correct counts, spatial relationships, and the right attribute bound to the right object. Moderate Handles two or three elements but slips on counting, ordering, or attribute binding as scenes grow. Weak Struggles with anything beyond a single subject; objects, counts, and positions blur together. Variable Accuracy depends on scene complexity — dependable for simple layouts, shaky for crowded ones.
	—	Strong	Moderate	Moderate	Strong	Moderate	Strong	Moderate	Unknown	Weak	Strong	Strong	—	Moderate	Strong	—	Moderate	Moderate	—	Strong	Unknown	Moderate	Moderate	Strong	Strong	—	Strong	Unknown	Unknown	Moderate	Moderate	Moderate	Moderate	—	—	Moderate
The largest / highest-quality native output the model produces before needing upscaling, and the set of aspect ratios it supports well. Matters for hero and print assets. What each rating means here Strong Produces large, high-quality native output across many aspect ratios before any upscaling is needed. Moderate Solid at standard sizes; needs upscaling for hero or print-scale assets. Weak Limited to smaller native outputs; pushing the resolution degrades quality. Variable Usable resolution depends on the host/endpoint and chosen settings rather than the model alone.
	—	Moderate	Strong	Weak	Moderate	Moderate	Moderate	Strong	Strong	Strong	Unknown	Strong	—	Weak	Moderate	Strong	Weak	Moderate	—	Moderate	Strong	Moderate	Moderate	Moderate	Moderate	—	Strong	Unknown	Unknown	Moderate	Strong	Strong	Moderate	—	—	Strong
How quickly the model produces an image, driven mainly by step count and backbone size. Directly tied to per-image cost on compute-billed hosts and to user-facing latency. What each rating means here Strong Generates images quickly — low step count or a distilled backbone keeps latency and per-image cost down. Moderate Typical generation time; fine for interactive use but not the fastest option. Weak Slow to generate, raising both latency and compute-billed cost per image. Variable Speed scales with step count and resolution — fast in a few-step mode, slow at maximum quality.

Capability ratings are an expert synthesis across benchmarks, community evaluations, and provider documentation. “—” means no profile data for that dimension.