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DeepSeek R1 Distill Qwen 1.5B vs Phi-3.5-mini-instruct
Comprehensive side-by-side LLM comparison
DeepSeek R1 Distill Qwen 1.5B leads with 3.4% higher average benchmark score. Both models have their strengths depending on your specific coding needs.
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DeepSeek
DeepSeek-R1-Distill-Qwen-1.5B was created through distillation into an ultra-compact Qwen architecture, designed to enable reasoning capabilities on resource-constrained devices. Built with just 1.5 billion parameters, it brings advanced analytical techniques to edge computing and mobile scenarios.
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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.
5 months newer
Phi-3.5-mini-instruct
Microsoft
2024-08-23
DeepSeek R1 Distill Qwen 1.5B
DeepSeek
2025-01-20
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Performance Metrics
Context window and performance specifications
Average performance across 1 common benchmarks
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DeepSeek R1 Distill Qwen 1.5B
Average Score:33.8%(+3.4%)
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Phi-3.5-mini-instruct
Average Score:30.4%
Provider Availability & Performance
Available providers and their performance metrics
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DeepSeek R1 Distill Qwen 1.5B
0 providers
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Phi-3.5-mini-instruct
1 providers
Azure
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DeepSeek R1 Distill Qwen 1.5B
Avg Score:33.8%(+3.4%)
Providers:0
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Phi-3.5-mini-instruct
Avg Score:30.4%
Providers:1