Model Comparison

Gemini 2.0 Flash Thinking vs QwQ-32B-Preview

Gemini 2.0 Flash Thinking significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

Gemini 2.0 Flash Thinking outperforms in 2 benchmarks (AIME 2024, GPQA), while QwQ-32B-Preview is better at 0 benchmarks.

Gemini 2.0 Flash Thinking significantly outperforms across most benchmarks.

Mon May 25 2026 • llm-stats.com

Arena Performance

Human preference votes

Context Window

Maximum input and output token capacity

Only QwQ-32B-Preview specifies input context (32,768 tokens). Only QwQ-32B-Preview specifies output context (32,768 tokens).

Gemini 2.0 Flash Thinking

Input- tokens

Output- tokens

QwQ-32B-Preview

Input32,768 tokens

Output32,768 tokens

Mon May 25 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Gemini 2.0 Flash Thinking supports multimodal inputs, whereas QwQ-32B-Preview does not.

Gemini 2.0 Flash Thinking can handle both text and other forms of data like images, making it suitable for multimodal applications.

Gemini 2.0 Flash Thinking

Text

Images

Audio

Video

QwQ-32B-Preview

Text

Images

Audio

Video

License

Usage and distribution terms

Gemini 2.0 Flash Thinking is licensed under a proprietary license, while QwQ-32B-Preview uses Apache 2.0.

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

Gemini 2.0 Flash Thinking

Proprietary

Closed source

QwQ-32B-Preview

Apache 2.0

Open weights

Release Timeline

When each model was launched

Gemini 2.0 Flash Thinking was released on 2025-01-21, while QwQ-32B-Preview was released on 2024-11-28.

Gemini 2.0 Flash Thinking is 2 months newer than QwQ-32B-Preview.

Gemini 2.0 Flash Thinking

Jan 21, 2025

1.3 years ago

1mo newer

QwQ-32B-Preview

Nov 28, 2024

1.5 years ago

Knowledge Cutoff

When training data ends

Gemini 2.0 Flash Thinking has a knowledge cutoff of 2024-08-01, while QwQ-32B-Preview has a cutoff of 2024-11-28.

QwQ-32B-Preview has more recent training data (up to 2024-11-28), making it potentially better informed about events through that date compared to Gemini 2.0 Flash Thinking (2024-08-01).

Gemini 2.0 Flash Thinking

Aug 2024

QwQ-32B-Preview

Nov 2024

3 mo newer

Outputs Comparison

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Key Takeaways

Gemini 2.0 Flash Thinking

View details

Google

Supports multimodal inputs

Higher AIME 2024 score (73.3% vs 50.0%)

Higher GPQA score (74.2% vs 65.2%)

QwQ-32B-Preview

View details

Alibaba Cloud / Qwen Team

Larger context window (32,768 tokens)

Has open weights

Detailed Comparison

AI Model Comparison Table
Feature	Gemini 2.0 Flash Thinking	QwQ-32B-Preview

FAQ

Common questions about Gemini 2.0 Flash Thinking vs QwQ-32B-Preview.

Which is better, Gemini 2.0 Flash Thinking or QwQ-32B-Preview?

Gemini 2.0 Flash Thinking significantly outperforms across most benchmarks. Gemini 2.0 Flash Thinking is made by Google and QwQ-32B-Preview 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 Thinking compare to QwQ-32B-Preview in benchmarks?

Gemini 2.0 Flash Thinking scores MMMU: 75.4%, GPQA: 74.2%, AIME 2024: 73.3%. QwQ-32B-Preview scores MATH-500: 90.6%, GPQA: 65.2%, AIME 2024: 50.0%, LiveCodeBench: 50.0%.

What are the context window sizes for Gemini 2.0 Flash Thinking and QwQ-32B-Preview?

Gemini 2.0 Flash Thinking supports an unknown number of tokens and QwQ-32B-Preview supports 33K 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 Thinking and QwQ-32B-Preview?

Key differences include multimodal support (yes vs no), licensing (Proprietary vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes Gemini 2.0 Flash Thinking and QwQ-32B-Preview?

Gemini 2.0 Flash Thinking is developed by Google and QwQ-32B-Preview is developed by Alibaba Cloud / Qwen Team.