deepseek
Nano Banana 2 (Gemini 3.1 Flash Image Preview) vs DeepSeek V4 Pro 0423
71.6 overall · $1.08 / 1M
Nano Banana 2 (Gemini 3.1 Flash Image Preview)
google/gemini-3.1-flash-image-preview
reasoningimage input
Nano Banana 2 (Gemini 3.1 Flash Image Preview) costs $0.500 per 1M input tokens and $3.00 per 1M output tokens with a 66K-token context window.
A reasoning model from google, released Feb 26, 2026. LiveBench hasn't published a run for it, so this page carries measured price and specs only — no benchmark numbers.
- Input / 1M
- $0.500
- Output / 1M
- $3.00
- Context
- 66K
- LiveBench overall
- Not evaluated
Specs and pricing
| Input price USD per 1M prompt tokens | $0.500 |
|---|---|
| Output price USD per 1M completion tokens, reasoning included | $3.00 |
| Blended price 3:1 input:output — #124 most expensive of 343 listed models | $1.13 |
| Cached input read Blank means not priced separately — not free | — |
| Cache write | — |
| Context window | 65,536 tokens |
| Max output | 58,982 tokens |
| Input modalities | image, text |
| Tool calling | No |
| Extended reasoning | Yes |
| Knowledge cutoff | — |
| Listed | Feb 26, 2026 |
What Nano Banana 2 (Gemini 3.1 Flash Image Preview) costs to run
Monthly list cost across five workload shapes, using the published cached-input rate where there is one. A floor, not a quote — batch discounts and cache writes aren't included.
| Workload | Per month |
|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $360.00/mo |
| RAG assistant 8K in / 600 out × 100K requests | $580.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $640.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $725.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $1,550/mo |
Compare Nano Banana 2 (Gemini 3.1 Flash Image Preview) with
Benchmarked models at a similar price.
qwen
Nano Banana 2 (Gemini 3.1 Flash Image Preview) vs Qwen3.8 27B
75.3 overall · $1.06 / 1M
nvidia
Nano Banana 2 (Gemini 3.1 Flash Image Preview) vs Nemotron 3 Ultra
67.4 overall · $1.05 / 1M
x-ai
Nano Banana 2 (Gemini 3.1 Flash Image Preview) vs SpaceXAI: Grok Build 0.1
67.8 overall · $1.25 / 1M
z-ai
Nano Banana 2 (Gemini 3.1 Flash Image Preview) vs GLM 5.2
73.2 overall · $0.998 / 1M
deepseek
Nano Banana 2 (Gemini 3.1 Flash Image Preview) vs DeepSeek V4 Pro 0813
77.4 overall · $0.990 / 1M
Nano Banana 2 (Gemini 3.1 Flash Image Preview) FAQ
How much does Nano Banana 2 (Gemini 3.1 Flash Image Preview) cost?
Nano Banana 2 (Gemini 3.1 Flash Image Preview) lists at $0.500 per 1M input tokens and $3.00 per 1M output tokens. A RAG assistant handling 100K requests a month (8K tokens in, 600 out) comes to about $580.00 at list price.
What is the context window of Nano Banana 2 (Gemini 3.1 Flash Image Preview)?
Nano Banana 2 (Gemini 3.1 Flash Image Preview) accepts up to 65,536 tokens of context and can return up to 58,982 output tokens per response.
Does Nano Banana 2 (Gemini 3.1 Flash Image Preview) support tool calling?
Nano Banana 2 (Gemini 3.1 Flash Image Preview) does not list tool (function) calling support and exposes extended reasoning. It accepts image, text input.
How does Nano Banana 2 (Gemini 3.1 Flash Image Preview) score on benchmarks?
LiveBench has not published a run for Nano Banana 2 (Gemini 3.1 Flash Image Preview) yet. Until it does, compare it on price and specs, or run your own evaluation on your own tasks.
What is the API model ID for Nano Banana 2 (Gemini 3.1 Flash Image Preview)?
On OpenRouter the model ID is "google/gemini-3.1-flash-image-preview". It was listed on Feb 26, 2026.
More from google
How these numbers are produced
- Price and specs — provider list data from OpenRouter, refreshed every 15 minutes. “Blended” is a 3:1 input:output mix.
- Scores and ranks — LiveBench release 2026-06-25. Ranks count only models that have a published run; a blank means “not evaluated”, never “bad”.
- Cost per point — the measured dollars LiveBench spent on the run, divided by the score it earned.
Published benchmarks rank models on someone else's tasks. Before committing, see LLM & agent evaluation for building an eval on your own.