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Gemini 2.0 Flash-Lite vs QwQ-32B
Comprehensive side-by-side LLM comparison
Gemini 2.0 Flash-Lite leads with 12.9% higher average benchmark score. Gemini 2.0 Flash-Lite supports multimodal inputs. Overall, Gemini 2.0 Flash-Lite is the stronger choice for coding tasks.
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Gemini 2.0 Flash-Lite is a multimodal language model developed by Google. The model shows competitive results across 13 benchmarks. It excels particularly in MATH (86.8%), FACTS Grounding (83.6%), Global-MMLU-Lite (78.2%). With a 1.1M token context window, it can handle extensive documents and complex multi-turn conversations. The model is available through 1 API provider. As a multimodal model, it can process and understand text, images, and other input formats seamlessly. Released in 2025, it represents Google's latest advancement in AI technology.
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Alibaba Cloud / Qwen Team
QwQ-32B is a language model developed by Alibaba Cloud / Qwen Team. It achieves strong performance with an average score of 74.6% across 7 benchmarks. It excels particularly in MATH-500 (90.6%), IFEval (83.9%), AIME 2024 (79.5%). It's licensed for commercial use, making it suitable for enterprise applications. Released in 2025, it represents Alibaba Cloud / Qwen Team's latest advancement in AI technology.
28 days newer
Gemini 2.0 Flash-Lite
2025-02-05
QwQ-32B
Alibaba Cloud / Qwen Team
2025-03-05
Performance Metrics
Context window and performance specifications
Average performance across 19 common benchmarks
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Gemini 2.0 Flash-Lite
Average Score:40.3%(+12.9%)
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QwQ-32B
Average Score:27.5%
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Knowledge Cutoff
Training data recency comparison
Gemini 2.0 Flash-Lite
2024-06-01
QwQ-32B
2024-11-28
More recent knowledge cutoff means awareness of newer technologies and frameworks
Provider Availability & Performance
Available providers and their performance metrics
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Gemini 2.0 Flash-Lite
1 providers
Throughput: 85 tok/s
Latency: 0.7ms
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QwQ-32B
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Gemini 2.0 Flash-Lite
Avg Score:40.3%(+12.9%)
Providers:1
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QwQ-32B
Avg Score:27.5%
Providers:0