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Phi-3.5-mini-instruct vs QwQ-32B
Comprehensive side-by-side LLM comparison
QwQ-32B leads with 34.8% higher average benchmark score. Overall, QwQ-32B is the stronger choice for coding tasks.
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Microsoft
Phi-3.5 Mini was developed by Microsoft as a small language model designed to deliver impressive performance despite its compact size. Built with efficiency in mind, it demonstrates that capable language understanding and generation can be achieved with fewer parameters, making AI more accessible for edge and resource-constrained deployments.
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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.
6 months newer
Phi-3.5-mini-instruct
Microsoft
2024-08-23
QwQ-32B
Alibaba Cloud / Qwen Team
2025-03-05
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Performance Metrics
Context window and performance specifications
Average performance across 1 common benchmarks
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Phi-3.5-mini-instruct
Average Score:30.4%
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QwQ-32B
Average Score:65.2%(+34.8%)
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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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Phi-3.5-mini-instruct
1 providers
Azure
Throughput: 23 tok/s
Latency: 0.52ms
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QwQ-32B
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Phi-3.5-mini-instruct
Avg Score:30.4%
Providers:1
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QwQ-32B
Avg Score:65.2%(+34.8%)
Providers:0