Model Comparison

Gemini 2.0 Flash vs Qwen2.5 VL 7B Instruct

Gemini 2.0 Flash significantly outperforms across most benchmarks.

Performance Benchmarks

Comparative analysis across standard metrics

1 benchmarks

Gemini 2.0 Flash outperforms in 1 benchmarks (MMMU), while Qwen2.5 VL 7B Instruct is better at 0 benchmarks.

Gemini 2.0 Flash significantly outperforms across most benchmarks.

Thu Apr 30 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Thu Apr 30 2026 • llm-stats.com
Google
Gemini 2.0 Flash
Input tokens$0.10
Output tokens$0.40
Best providerGoogle
Alibaba Cloud / Qwen Team
Qwen2.5 VL 7B Instruct
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Notice missing or incorrect data?Start an Issue

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).

Google
Gemini 2.0 Flash
Input1,048,576 tokens
Output8,192 tokens
Alibaba Cloud / Qwen Team
Qwen2.5 VL 7B Instruct
Input- tokens
Output- tokens
Thu Apr 30 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both Gemini 2.0 Flash and Qwen2.5 VL 7B 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 7B Instruct

Text
Images
Audio
Video

License

Usage and distribution terms

Gemini 2.0 Flash is licensed under a proprietary license, while Qwen2.5 VL 7B Instruct uses Apache 2.0.

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 7B Instruct

Apache 2.0

Open weights

Release Timeline

When each model was launched

Gemini 2.0 Flash was released on 2024-12-01, while Qwen2.5 VL 7B Instruct was released on 2025-01-26.

Qwen2.5 VL 7B Instruct is 2 months newer than Gemini 2.0 Flash.

Gemini 2.0 Flash

Dec 1, 2024

1.4 years ago

Qwen2.5 VL 7B 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 7B 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 7B Instruct's cutoff date.

Gemini 2.0 Flash

Aug 2024

Qwen2.5 VL 7B Instruct

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion

Key Takeaways

Larger context window (1,048,576 tokens)
Higher MMMU score (70.7% vs 58.6%)
Alibaba Cloud / Qwen Team

Qwen2.5 VL 7B Instruct

View details

Alibaba Cloud / Qwen Team

Has open weights
GoogleGemini 2.0 Flash
Alibaba Cloud / Qwen TeamQwen2.5 VL 7B Instruct

Detailed Comparison

AI Model Comparison Table
Feature
Google
Gemini 2.0 Flash
Alibaba Cloud / Qwen Team
Qwen2.5 VL 7B Instruct

FAQ

Common questions about Gemini 2.0 Flash vs Qwen2.5 VL 7B Instruct

Gemini 2.0 Flash significantly outperforms across most benchmarks. Gemini 2.0 Flash is made by Google and Qwen2.5 VL 7B Instruct is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
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 7B Instruct scores DocVQA: 95.7%, Android Control Low_EM: 91.4%, MobileMiniWob++_SR: 91.4%, ChartQA: 87.3%, OCRBench: 86.4%.
Gemini 2.0 Flash supports 1.0M tokens and Qwen2.5 VL 7B Instruct supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include licensing (Proprietary vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.
Gemini 2.0 Flash is developed by Google and Qwen2.5 VL 7B Instruct is developed by Alibaba Cloud / Qwen Team.