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Phi 4 Mini Reasoning vs QwQ-32B
Comprehensive side-by-side LLM comparison
QwQ-32B leads with 4.6% higher average benchmark score. Both models have their strengths depending on your specific coding needs.
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Microsoft
Phi-4 Mini Reasoning was developed to incorporate extended thinking capabilities into the ultra-compact Phi-4 Mini architecture. Built to demonstrate that reasoning enhancements can be applied even to very small models, it brings analytical depth to resource-constrained environments.
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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.
1 month newer
QwQ-32B
Alibaba Cloud / Qwen Team
2025-03-05
Phi 4 Mini Reasoning
Microsoft
2025-04-30
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Average performance across 2 common benchmarks
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Phi 4 Mini Reasoning
Average Score:73.3%
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QwQ-32B
Average Score:77.9%(+4.6%)
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Knowledge Cutoff
Training data recency comparison
QwQ-32B
2024-11-28
Phi 4 Mini Reasoning
2025-02-01
More recent knowledge cutoff means awareness of newer technologies and frameworks
Provider Availability & Performance
Available providers and their performance metrics
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Phi 4 Mini Reasoning
0 providers
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QwQ-32B
0 providers
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Phi 4 Mini Reasoning
Avg Score:73.3%
Providers:0
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QwQ-32B
Avg Score:77.9%(+4.6%)
Providers:0