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DeepSeek R1 Distill Qwen 32B vs Phi-3.5-MoE-instruct
Comprehensive side-by-side LLM comparison
Phi-3.5-MoE-instruct leads with 51.0% higher average benchmark score. Overall, Phi-3.5-MoE-instruct is the stronger choice for coding tasks.
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DeepSeek
DeepSeek R1 Distill Qwen 32B is a language model developed by DeepSeek. It achieves strong performance with an average score of 74.2% across 4 benchmarks. It excels particularly in MATH-500 (94.3%), AIME 2024 (83.3%), GPQA (62.1%). It supports a 256K token context window for handling large documents. The model is available through 1 API provider. It's licensed for commercial use, making it suitable for enterprise applications. Released in 2025, it represents DeepSeek's latest advancement in AI technology.
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Microsoft
Phi-3.5-MoE-instruct is a language model developed by Microsoft. It achieves strong performance with an average score of 65.6% across 31 benchmarks. It excels particularly in ARC-C (91.0%), OpenBookQA (89.6%), GSM8k (88.7%). It's licensed for commercial use, making it suitable for enterprise applications. Released in 2024, it represents Microsoft's latest advancement in AI technology.
5 months newer
Phi-3.5-MoE-instruct
Microsoft
2024-08-23
DeepSeek R1 Distill Qwen 32B
DeepSeek
2025-01-20
Performance Metrics
Context window and performance specifications
Average performance across 34 common benchmarks
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DeepSeek R1 Distill Qwen 32B
Average Score:8.7%
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Phi-3.5-MoE-instruct
Average Score:59.8%(+51.0%)
Provider Availability & Performance
Available providers and their performance metrics
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DeepSeek R1 Distill Qwen 32B
1 providers
DeepInfra
Throughput: 37 tok/s
Latency: 0.65ms
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Phi-3.5-MoE-instruct
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DeepSeek R1 Distill Qwen 32B
Avg Score:8.7%
Providers:1
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Phi-3.5-MoE-instruct
Avg Score:59.8%(+51.0%)
Providers:0