Google · language model
Nano Banana (Gemini 2.5 Flash Image): pricing, context and benchmarks
Nano Banana (Gemini 2.5 Flash Image) is Google's model released on October 7, 2025. It costs $0.3 per million input tokens and $2.5 per million output tokens, with a 32.8K-token context window. No independent score has been published for it so far.
| Price | 14/30 | |
|---|---|---|
| Context | 1/15 | |
| Features | 3/15 |
What you need to know about Nano Banana (Gemini 2.5 Flash Image)
Google, based in United States, put Nano Banana (Gemini 2.5 Flash Image) online on October 7, 2025. It accepts images and text as input and produces images and text. Its context window reaches 32,768 tokens, larger than 8 % of the models in the registry, and it can write up to 8,192 tokens in one go.
It has been available for about 13 months, long enough for its prices and scores to have settled.
Nano Banana (Gemini 2.5 Flash Image) pricing
On OpenRouter, Nano Banana (Gemini 2.5 Flash Image) costs $0.3 per million input tokens and $2.5 per million output tokens. Counting three tokens read for every token written, its average price is $0.85 per million, more expensive than 53 % of paid models (1.1 times the median of $0.76).
A concrete example: a task that sends 2,000 tokens (about 1,500 words) and receives a 500-token answer costs $0.0019. At 1,000 tasks a day, budget $56 a month. The price calculator redoes this with your own volumes.
Tokens already sent and cached are billed at $0.03 per million.
Performance and benchmarks
Epoch AI does not yet publish a capabilities index for Nano Banana (Gemini 2.5 Flash Image). We therefore show no made-up performance rating: only price, context and features count towards its score.
Features, privacy and local use
- It does not support tool calling: it is not suited to driving an AI agent that has to act.
- It can answer in an enforced JSON format (structured outputs).
- Its knowledge stops at January 2025.
Its weights are not published: it can only be used through the Google API or a broker such as OpenRouter, which means sending your data off your machines.
Other Google models
| Model | Released | Avg. price | ECI |
|---|---|---|---|
| Gemini 4 Argon | Oct 13, 2026 | $4 | 166.5 |
| Nano Banana 2.1 | Oct 6, 2026 | $3 | — |
| Gemini 3.8 Flash | Sep 2, 2026 | $1.65 | 157.4 |
| Gemini 3.7 Flash | Aug 13, 2026 | $1.5 | 158 |
| Gemini 3.6 Flash | Jul 21, 2026 | $1.5 | 154.5 |
| Gemini 3.5 Flash Lite | Jul 21, 2026 | $0.85 | 144.9 |
| Nano Banana 2 Lite (Gemini 3.1 Flash Lite Image) | Jun 30, 2026 | $0.56 | — |
| Nano Banana 2 (Gemini 3.1 Flash Image) | Jun 18, 2026 | $1.13 | — |
Verdict
Computed score: 30/100, from public data (performance, price, context, features).
Limitations
- No tool calling: unusable as an agent's brain.
- No independent score published: performance cannot be verified.
Avoid if…
- your data must not leave your servers;
- you are building an agent that must call tools;
- you analyse long documents in one go.
Alternatives
- Moondream 3 Preview$0.85
- Moondream 3.1$0.85
- GLM 4.5V$0.9
- Qwen2.5 Coder 32B Instruct$0.75
- GPT-3.5 Turbo$0.75
- Qwen2.5 VL 72B Instruct$0.85
Comparisons and tools
Learn more
Frequently asked questions
How much does Nano Banana (Gemini 2.5 Flash Image) cost?
$0.3 per million input tokens and $2.5 per million output tokens on OpenRouter (checked October 25, 2026). A task with 2,000 tokens read and 500 written costs $0.0019.
How big is Nano Banana (Gemini 2.5 Flash Image)'s context window?
32,768 tokens, roughly 25,000 words, with a maximum of 8,192 output tokens.
Can Nano Banana (Gemini 2.5 Flash Image) run locally?
No, Google does not publish its weights: it is API-only.
Is Nano Banana (Gemini 2.5 Flash Image) suitable for an AI agent?
No: without tool calling it cannot act, only answer.
What are the alternatives to Nano Banana (Gemini 2.5 Flash Image)?
The closest models on price and performance are Moondream 3 Preview, Moondream 3.1, and GLM 4.5V.
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Neutrality. No affiliate links. No vendor pays to be reviewed, rated or ranked. Reviews are directed by François Chollet, neutral and factual, and every data point is dated and sourced. How we score