Model Comparison

Gemini 2.0 Flash-Lite vs Qwen2.5-Omni-7B

Gemini 2.0 Flash-Lite significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

5 benchmarks

Gemini 2.0 Flash-Lite outperforms in 4 benchmarks (GPQA, MATH, MMLU-Pro, MMMU), while Qwen2.5-Omni-7B is better at 1 benchmark (EgoSchema).

Gemini 2.0 Flash-Lite 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-Lite
Input tokens$0.07
Output tokens$0.30
Best providerGoogle
Alibaba Cloud / Qwen Team
Qwen2.5-Omni-7B
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
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Context Window

Maximum input and output token capacity

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

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

Input Capabilities

Supported data types and modalities

Both Gemini 2.0 Flash-Lite and Qwen2.5-Omni-7B support multimodal inputs.

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

Gemini 2.0 Flash-Lite

Text
Images
Audio
Video

Qwen2.5-Omni-7B

Text
Images
Audio
Video

License

Usage and distribution terms

Gemini 2.0 Flash-Lite is licensed under a proprietary license, while Qwen2.5-Omni-7B uses Apache 2.0.

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

Gemini 2.0 Flash-Lite

Proprietary

Closed source

Qwen2.5-Omni-7B

Apache 2.0

Open weights

Release Timeline

When each model was launched

Gemini 2.0 Flash-Lite was released on 2025-02-05, while Qwen2.5-Omni-7B was released on 2025-03-27.

Qwen2.5-Omni-7B is 2 months newer than Gemini 2.0 Flash-Lite.

Gemini 2.0 Flash-Lite

Feb 5, 2025

1.2 years ago

Qwen2.5-Omni-7B

Mar 27, 2025

1.1 years ago

1mo newer

Knowledge Cutoff

When training data ends

Gemini 2.0 Flash-Lite has a documented knowledge cutoff of 2024-06-01, while Qwen2.5-Omni-7B's cutoff date is not specified.

We can confirm Gemini 2.0 Flash-Lite's training data extends to 2024-06-01, but cannot make a direct comparison without Qwen2.5-Omni-7B's cutoff date.

Gemini 2.0 Flash-Lite

Jun 2024

Qwen2.5-Omni-7B

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Outputs Comparison

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Key Takeaways

Google
Larger context window (1,048,576 tokens)
Higher GPQA score (51.5% vs 30.8%)
Higher MATH score (86.8% vs 71.5%)
Higher MMLU-Pro score (71.6% vs 47.0%)
Higher MMMU score (68.0% vs 59.2%)
Alibaba Cloud / Qwen Team

Qwen2.5-Omni-7B

View details

Alibaba Cloud / Qwen Team

Has open weights
Higher EgoSchema score (68.6% vs 67.2%)

Detailed Comparison

AI Model Comparison Table
Feature
Google
Gemini 2.0 Flash-Lite
Alibaba Cloud / Qwen Team
Qwen2.5-Omni-7B

FAQ

Common questions about Gemini 2.0 Flash-Lite vs Qwen2.5-Omni-7B

Gemini 2.0 Flash-Lite significantly outperforms across most benchmarks. Gemini 2.0 Flash-Lite is made by Google and Qwen2.5-Omni-7B 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-Lite scores MATH: 86.8%, FACTS Grounding: 83.6%, Global-MMLU-Lite: 78.2%, MMLU-Pro: 71.6%, MMMU: 68.0%. Qwen2.5-Omni-7B scores DocVQA: 95.2%, VocalSound: 93.9%, GSM8k: 88.7%, GiantSteps Tempo: 88.0%, ChartQA: 85.3%.
Gemini 2.0 Flash-Lite supports 1.0M tokens and Qwen2.5-Omni-7B 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-Lite is developed by Google and Qwen2.5-Omni-7B is developed by Alibaba Cloud / Qwen Team.