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DeepSeek R1 Distill Qwen 1.5B vs Phi-3.5-MoE-instruct
Comprehensive side-by-side LLM comparison
Phi-3.5-MoE-instruct leads with 3.0% 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 MoE was created using a mixture-of-experts architecture, designed to provide enhanced capabilities while maintaining efficiency through sparse activation. Built to combine the benefits of larger models with practical computational requirements, it represents Microsoft's exploration of efficient scaling techniques.
5 months newer
Phi-3.5-MoE-instruct
Microsoft
2024-08-23
DeepSeek R1 Distill Qwen 1.5B
DeepSeek
2025-01-20
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Average performance across 1 common benchmarks
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DeepSeek R1 Distill Qwen 1.5B
Average Score:33.8%
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Phi-3.5-MoE-instruct
Average Score:36.8%(+3.0%)
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-MoE-instruct
0 providers
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DeepSeek R1 Distill Qwen 1.5B
Avg Score:33.8%
Providers:0
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Phi-3.5-MoE-instruct
Avg Score:36.8%(+3.0%)
Providers:0