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DeepSeek R1 Distill Llama 70B vs Phi-3.5-MoE-instruct
Comprehensive side-by-side LLM comparison
DeepSeek R1 Distill Llama 70B leads with 28.4% higher average benchmark score. Overall, DeepSeek R1 Distill Llama 70B is the stronger choice for coding tasks.
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DeepSeek
DeepSeek-R1-Distill-Llama-70B was created through knowledge distillation from DeepSeek-R1 into a Llama-based architecture, designed to transfer reasoning capabilities to a widely-used open-source foundation. Built to combine DeepSeek's reasoning innovations with Llama's ecosystem compatibility, it enables broader access to advanced reasoning techniques.
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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 Llama 70B
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 Llama 70B
Average Score:65.2%(+28.4%)
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Phi-3.5-MoE-instruct
Average Score:36.8%
Provider Availability & Performance
Available providers and their performance metrics
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DeepSeek R1 Distill Llama 70B
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 Llama 70B
Avg Score:65.2%(+28.4%)
Providers:1
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Phi-3.5-MoE-instruct
Avg Score:36.8%
Providers:0