Nano Banana 2 (Gemini 3.1 Flash Image) vs Qwen 2.5 72B
How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for Nano Banana 2 (Gemini 3.1 Flash Image) and Qwen 2.5 72B.
Nano Banana 2 (Gemini 3.1 Flash Image)
Gemini 3.1 Flash Image, 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 advanced...
Qwen 2.5 72B
Qwen 2.5 72B is Alibaba Cloud's flagship open-weight large language model from the Qwen 2.5 generation, delivering GPT-4-class performance across general reasoning, coding, mathematics, and multilingual tasks with strong Chinese-language superiority. It supports a 131,072-token context window and is available under a permissive Apache 2.0 license for both research and commercial use, making it one of the most popular open-weight alternatives to Llama for bilingual applications.
Technical Specifications
| Specification | Nano Banana 2 (Gemini 3.1 Flash Image) | Qwen 2.5 72B |
|---|---|---|
| Provider | Alibaba | |
| Context Window | 131,072 tokens | 131,072 tokens |
| Agent Suitability | N/A | 88/100 |
| Time to First Token (TTFT) | N/A | 280 ms |
| Deployment Model | managed api | self hostable |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2026-06-18 | 2025-09-19 |
API Pricing Comparison
Input Price per Million Tokens
Nano Banana 2 (Gemini 3.1 Flash Image)
$0.50
Qwen 2.5 72B
$0.40
Output Price per Million Tokens
Nano Banana 2 (Gemini 3.1 Flash Image)
$3.00
Qwen 2.5 72B
$0.80
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Benchmark Performance Metrics
Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.
Nano Banana 2 (Gemini 3.1 Flash Image) Quirks & Gotchas
No developer gotchas reported.
Qwen 2.5 72B Quirks & Gotchas
- ▸Strong bilingual (ZH/EN) performance — best open model for Chinese-language tasks
- ▸Self-hostable via vLLM or Ollama with 4-bit quantization