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CostPerPrompt

Nano Banana 2 (Gemini 3.1 Flash Image Preview) API Pricing

Google · context window 66K · prices updated 2026-09-15

This is a dated/preview alias of Nano Banana 2 (Gemini 3.1 Flash Image) and currently carries identical pricing. The main page is kept up to date — this one exists so the exact model string resolves.

Input per 1M tokens
$0.5
Output per 1M tokens
$3.00
Cached input per 1M
No discounted cached tier on this model

Where Nano Banana 2 (Gemini 3.1 Flash Image Preview) sits on price

At $3.00 per million output tokens, Nano Banana 2 (Gemini 3.1 Flash Image Preview) is a mid tier model — 1.4× the median output price of $2.08, which puts it cheaper than 41% and pricier than 59% of the models we track. It sits 24th cheapest of the 38 Google models we track. Output costs 6.0× more than input here, so anything that makes the model write less — tighter instructions, structured output, lower max_tokens — moves the bill more than trimming the prompt.

Output tokens per $1
333,333
One full context fill
$0.0328
Cheaper than
41% of tracked models

What real workloads cost on Nano Banana 2 (Gemini 3.1 Flash Image Preview)

These three workloads are the ones teams actually run on a mid-priced model — a $3.00/1M model is not bought for the same job as one ten times the price.

Workload Per request Per month
Support chatbot — 500 conversations/day (5K in / 1.4K out) Conversation history re-sent each turn — the classic prompt-caching win. $0.0067 $100
Document pipeline — 10K docs/day (3K in / 500 out) Batch-friendly: no user waiting, so a ~50% batch discount usually applies. $0.003 $900
Small app — 1K requests/day (500 in / 150 out) The baseline most side projects actually run at. $0.0007 $21.00

Model your exact traffic in the API cost calculator — it preloads Nano Banana 2 (Gemini 3.1 Flash Image Preview) with caching and batch options.

Nano Banana 2 (Gemini 3.1 Flash Image Preview) price history

Nano Banana 2 (Gemini 3.1 Flash Image Preview)'s price has not moved since we began tracking it on Aug 2, 2026 — 44 days of stability in a market where 78 of the 334 models we track have repriced over the same period, including 6 of Google's own 38 models.

Change-points from our daily price snapshots (tracking since Aug 2, 2026; intraday moves between snapshots are not captured).

Nano Banana 2 (Gemini 3.1 Flash Image Preview) vs GPT Audio Mini

The closest-priced alternative from another vendor is GPT Audio Mini (OpenAI) — 20% cheaper on output, with $0.1 more per million input tokens. When two models land this close on price, the decision is quality on your own workload, not the price sheet: run 50 real requests through both and compare.

See GPT Audio Mini pricing →

Cheaper alternatives

More Google models

Frequently asked questions

How much does the Nano Banana 2 (Gemini 3.1 Flash Image Preview) API cost?

Nano Banana 2 (Gemini 3.1 Flash Image Preview) costs $0.5 per million input tokens and $3.00 per million output tokens and no discounted cached-input tier. That works out to roughly 333,333 output tokens per dollar.

What does the support chatbot workload cost on Nano Banana 2 (Gemini 3.1 Flash Image Preview)?

Support chatbot — 500 conversations/day (5K in / 1.4K out) costs about $0.0067 per request and $100 per month on Nano Banana 2 (Gemini 3.1 Flash Image Preview). Conversation history re-sent each turn — the classic prompt-caching win.

What does it cost to fill Nano Banana 2 (Gemini 3.1 Flash Image Preview)'s 66K context window?

Sending 66K of input in a single request costs $0.0328 at $0.5 per million tokens — before any output. There is no cached-input tier on this model, so every repeat of that context is billed at full price. This is why large context windows are cheap to advertise and expensive to actually use.

Is Nano Banana 2 (Gemini 3.1 Flash Image Preview) worth the price?

Nano Banana 2 (Gemini 3.1 Flash Image Preview) sits in the middle of the market (24 of 38 by price within Google). The honest test is a routing experiment: send the same 200 real requests to this model and to a tier below, and compare failure rate against the price gap — most teams find a majority of traffic never needed the pricier model.