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DeepSeek R1 Distill Llama 70B vs Phi-4-multimodal-instruct
Comprehensive side-by-side LLM comparison
Both models have similar pricing. Phi-4-multimodal-instruct supports multimodal inputs. Both models have their strengths depending on your specific coding needs.
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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-4 Multimodal was created to handle multiple input modalities including text, images, and potentially other formats. Built to extend Phi-4's efficiency into multimodal applications, it demonstrates that compact models can successfully integrate diverse information types.
12 days newer
DeepSeek R1 Distill Llama 70B
DeepSeek
2025-01-20
Phi-4-multimodal-instruct
Microsoft
2025-02-01
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Pricing Comparison
Cost per million tokens (USD)
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DeepSeek R1 Distill Llama 70B
Input:$0.10
Output:$0.40
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Phi-4-multimodal-instruct
Input:$0.05
Output:$0.10($0.35 cheaper)
Performance Metrics
Context window and performance specifications
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Knowledge Cutoff
Training data recency comparison
Phi-4-multimodal-instruct
2024-06-01
More recent knowledge cutoff means awareness of newer technologies and frameworks
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-4-multimodal-instruct
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DeepSeek R1 Distill Llama 70B
Avg Score:0.0%
Providers:1
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Phi-4-multimodal-instruct
Avg Score:0.0%
Providers:1