API cost decision in 10 seconds

Nano Banana 2 (Gemini 3.1 Flash Image Preview) vs Qwen3 235B A22B

Pick Qwen3 235B A22B when budget is the priority.

Page updated: 2026-05-26T15:08:08Z Data confirmed: 2026-05-26T20:08:05Z Prices normalized to USD per 1M tokens Sample workload: 1M input + 500K output

Budget verdict

Pick Qwen3 235B A22B when budget is the priority.

On the standard 1M input plus 500K output workload, Qwen3 235B A22B is estimated at $1.36 vs $2 for Nano Banana 2 (Gemini 3.1 Flash Image Preview), saving $0.64 (31.8% lower).

Cost-first pickQwen3 235B A22B

Context-first pickBoth models

Sample savings$0.6431.8%

10x traffic gap$6.35

The reported context window is tied, so cost and provider fit carry more weight. At 10x that traffic, the same price gap is about $6.35. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

Qwen3 235B A22B stays cheaper across input-heavy, balanced, and output-heavy sample workloads.

Workload shape	Token mix	Better pick	Nano Banana 2 (Gemini 3.1 Flash Image Preview)	Qwen3 235B A22B
Input-heavy / RAG	5M input + 500K output	Qwen3 235B A22B	$4	$3.19
Balanced workload	1M input + 1M output	Qwen3 235B A22B	$3.5	$2.27
Output-heavy chatbot	1M input + 5M output	Qwen3 235B A22B	$15.5	$9.55

Cheaper input Qwen3 235B A22B $0.5 vs $0.455 / 1M

Qwen3 235B A22B is $0.04 cheaper per 1M input tokens (9% lower; 1.1x difference).

Cheaper output Qwen3 235B A22B $3 vs $1.82 / 1M

Qwen3 235B A22B is $1.18 cheaper per 1M output tokens (39.3% lower; 1.65x difference).

Larger context Tie 131.07K vs 131.07K

Both models report the same context window at 131.07K tokens.

Sample workload Qwen3 235B A22B $2 vs $1.36

Qwen3 235B A22B is $0.64 cheaper on the standard workload (31.8% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.

Monthly input tokens Monthly output tokens

Nano Banana 2 (Gemini 3.1 Flash Image Preview) Calculating… Estimated API cost

Qwen3 235B A22B Calculating… Estimated API cost

Cheaper for this workload Calculating… Difference: calculating…

This estimate uses normalized public API pricing per 1M tokens. It is a planning aid, not a billing quote. Verify provider pricing, limits, and terms before production use.

Quick Decision

Verdict

Qwen3 235B A22B has the lower input price; Qwen3 235B A22B has the lower output price; both models report the same context window. For the 1M input plus 500K output sample, Qwen3 235B A22B is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $2 for Nano Banana 2 (Gemini 3.1 Flash Image Preview) and $1.36 for Qwen3 235B A22B.

Best Fit

Choose Nano Banana 2 (Gemini 3.1 Flash Image Preview) when its provider, model quality, latency, or availability is more important than the numeric price/context winner.

Choose Qwen3 235B A22B when you care most about lower input-token price, and lower output-token price.

Decision Notes

On the standard 1M input plus 500K output workload, Qwen3 235B A22B is estimated at $1.36 vs $2 for Nano Banana 2 (Gemini 3.1 Flash Image Preview), saving $0.64 (31.8% lower).
Qwen3 235B A22B is $0.64 cheaper on the standard workload (31.8% lower).
Qwen3 235B A22B is $0.04 cheaper per 1M input tokens (9% lower; 1.1x difference).
Qwen3 235B A22B is $1.18 cheaper per 1M output tokens (39.3% lower; 1.65x difference).
Both models report the same context window at 131.07K tokens.

Head-to-Head Specs

Feature	Nano Banana 2 (Gemini 3.1 Flash Image Preview) (Google)	Qwen3 235B A22B (Qwen)
Input Price prompt tokens per 1M	$0.5	$0.455
Completion Price per 1M tokens	$3	$1.82
Sample Workload Cost 1M input + 500K output	$2	$1.36
Context Window	131.07K	131.07K
Release Date	2026-02-26	2025-04-28
Popularity	#129	#131

Use-Case Decision Matrix

Use case	Better pick	Why
Budget-constrained production	Qwen3 235B A22B	On the standard 1M input plus 500K output workload, Qwen3 235B A22B is estimated at $1.36 vs $2 for Nano Banana 2 (Gemini 3.1 Flash Image Preview), saving $0.64 (31.8% lower).
High-volume input processing	Qwen3 235B A22B	Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbots	Qwen3 235B A22B	Lower output-token price matters most when assistants generate many completion tokens.
RAG or long-document work	Tie	A larger context window leaves more room for retrieved passages, conversation history, or source files.

Related Alternatives

Same-provider lower-cost swaps

Gemma 4 31B (free) can replace Nano Banana 2 (Gemini 3.1 Flash Image Preview) when lower sample workload cost matters most: $0.
Gemma 4 26B A4B (free) can replace Nano Banana 2 (Gemini 3.1 Flash Image Preview) when lower sample workload cost matters most: $0.
Lyria 3 Clip Preview can replace Nano Banana 2 (Gemini 3.1 Flash Image Preview) when lower sample workload cost matters most: $0.
Lyria 3 Pro Preview can replace Nano Banana 2 (Gemini 3.1 Flash Image Preview) when lower sample workload cost matters most: $0.

Larger context near this budget

Llama 4 Scout offers 10M context with $0.23 sample workload cost.
Grok 4.20 offers 2M context with $2.5 sample workload cost.
Owl Alpha offers 1.05M context with $0 sample workload cost.
DeepSeek V4 Flash offers 1.05M context with $0.2 sample workload cost.

Popular competitors

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Hy3 preview · Tencent · #2
Claude Opus 4.7 · Anthropic · #3
Claude Sonnet 4.6 · Anthropic · #4

Cheaper alternatives

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Larger context alternatives

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Provider catalogs

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Google catalog

Review all tracked Google models before deciding whether this matchup is the right shortlist.

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Qwen catalog

Check other Qwen models with comparable pricing, context, or release timing.

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Nano Banana 2 (Gemini 3.1 Flash Image Preview)

Gemini 3.1 Flash Image Preview, a.k.a. "Nano Banana 2," is Google’s latest state of the art image generation and editing model, delivering Pro-level visual quality at Flash speed. It combines...

Qwen3 235B A22B

Qwen3-235B-A22B is a 235B parameter mixture-of-experts (MoE) model developed by Qwen, activating 22B parameters per forward pass. It supports seamless switching between a "thinking" mode for complex reasoning, math, and...