Model Comparison

Gemini 2.0 Flash vs Qwen2.5 VL 72B Instruct

Both models are evenly matched across the benchmarks.

Want to compare interactively?Try the playground

Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

Gemini 2.0 Flash outperforms in 1 benchmarks (MMMU), while Qwen2.5 VL 72B Instruct is better at 1 benchmark (EgoSchema).

Both models are evenly matched across the benchmarks.

Sat May 02 2026 • llm-stats.com

Arena Performance

Human preference votes

Context Window

Maximum input and output token capacity

Only Gemini 2.0 Flash specifies input context (1,048,576 tokens). Only Gemini 2.0 Flash specifies output context (8,192 tokens).

Gemini 2.0 Flash

Input1,048,576 tokens

Output8,192 tokens

Qwen2.5 VL 72B Instruct

Input- tokens

Output- tokens

Sat May 02 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both Gemini 2.0 Flash and Qwen2.5 VL 72B Instruct support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

Gemini 2.0 Flash

Text

Images

Audio

Video

Qwen2.5 VL 72B Instruct

Text

Images

Audio

Video

License

Usage and distribution terms

Gemini 2.0 Flash is licensed under a proprietary license, while Qwen2.5 VL 72B Instruct uses tongyi-qianwen.

License differences may affect how you can use these models in commercial or open-source projects.

Gemini 2.0 Flash

Proprietary

Closed source

Qwen2.5 VL 72B Instruct

tongyi-qianwen

Open weights

Release Timeline

When each model was launched

Gemini 2.0 Flash was released on 2024-12-01, while Qwen2.5 VL 72B Instruct was released on 2025-01-26.

Qwen2.5 VL 72B Instruct is 2 months newer than Gemini 2.0 Flash.

Gemini 2.0 Flash

Dec 1, 2024

1.4 years ago

Qwen2.5 VL 72B Instruct

Jan 26, 2025

1.3 years ago

1mo newer

Knowledge Cutoff

When training data ends

Gemini 2.0 Flash has a documented knowledge cutoff of 2024-08-01, while Qwen2.5 VL 72B Instruct's cutoff date is not specified.

We can confirm Gemini 2.0 Flash's training data extends to 2024-08-01, but cannot make a direct comparison without Qwen2.5 VL 72B Instruct's cutoff date.

Gemini 2.0 Flash

Aug 2024

Qwen2.5 VL 72B Instruct

—

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Gemini 2.0 Flash

View details

Google

Larger context window (1,048,576 tokens)

Higher MMMU score (70.7% vs 70.2%)

Qwen2.5 VL 72B Instruct

View details

Alibaba Cloud / Qwen Team

Has open weights

Higher EgoSchema score (76.2% vs 71.5%)

Gemini 2.0 Flash

Qwen2.5 VL 72B Instruct

Compare in Playground

Detailed Comparison

AI Model Comparison Table
Feature	Gemini 2.0 Flash	Qwen2.5 VL 72B Instruct

FAQ

Common questions about Gemini 2.0 Flash vs Qwen2.5 VL 72B Instruct.

Which is better, Gemini 2.0 Flash or Qwen2.5 VL 72B Instruct?

Both models are evenly matched across the benchmarks. Gemini 2.0 Flash is made by Google and Qwen2.5 VL 72B Instruct is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does Gemini 2.0 Flash compare to Qwen2.5 VL 72B Instruct in benchmarks?

Gemini 2.0 Flash scores Natural2Code: 92.9%, MATH: 89.7%, FACTS Grounding: 83.6%, MMLU-Pro: 76.4%, EgoSchema: 71.5%. Qwen2.5 VL 72B Instruct scores DocVQA: 96.4%, Android Control Low_EM: 93.7%, ChartQA: 89.5%, OCRBench: 88.5%, AI2D: 88.4%.

What are the context window sizes for Gemini 2.0 Flash and Qwen2.5 VL 72B Instruct?

Gemini 2.0 Flash supports 1.0M tokens and Qwen2.5 VL 72B Instruct supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Gemini 2.0 Flash and Qwen2.5 VL 72B Instruct?

Key differences include licensing (Proprietary vs tongyi-qianwen). See the full comparison above for benchmark-by-benchmark results.

Who makes Gemini 2.0 Flash and Qwen2.5 VL 72B Instruct?

Gemini 2.0 Flash is developed by Google and Qwen2.5 VL 72B Instruct is developed by Alibaba Cloud / Qwen Team.