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DeepSeek-V3 vs QwQ-32B
Comprehensive side-by-side LLM comparison
QwQ-32B leads with 14.1% higher average benchmark score. Overall, QwQ-32B is the stronger choice for coding tasks.
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DeepSeek
DeepSeek-V3 was introduced as a major architectural advancement, developed with 671B mixture-of-experts parameters and trained on 14.8 trillion tokens. Built to be three times faster than V2 while maintaining open-source availability, it demonstrates competitive performance against frontier closed-source models and represents a significant leap in efficient large-scale model design.
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Alibaba Cloud / Qwen Team
QwQ 32B was developed as a reasoning-focused model, designed to emphasize analytical thinking and problem-solving capabilities. Built with 32 billion parameters optimized for step-by-step reasoning, it demonstrates Qwen's exploration into models that prioritize deliberate analytical processing.
2 months newer
DeepSeek-V3
DeepSeek
2024-12-25
QwQ-32B
Alibaba Cloud / Qwen Team
2025-03-05
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Performance Metrics
Context window and performance specifications
Average performance across 5 common benchmarks
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DeepSeek-V3
Average Score:62.4%
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QwQ-32B
Average Score:76.5%(+14.1%)
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Knowledge Cutoff
Training data recency comparison
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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DeepSeek-V3
1 providers
DeepSeek
Throughput: 100 tok/s
Latency: 0.5ms
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QwQ-32B
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DeepSeek-V3
Avg Score:62.4%
Providers:1
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QwQ-32B
Avg Score:76.5%(+14.1%)
Providers:0