+
+
+
+
+
+
+
+
VS
VS
VS
VS
VS
VS
DeepSeek VL2 vs Phi-3.5-MoE-instruct
Comprehensive side-by-side LLM comparison
DeepSeek VL2 supports multimodal inputs. Both models have their strengths depending on your specific coding needs.
+
+
+
+
DeepSeek
DeepSeek-VL2 was developed as a vision-language model, designed to handle both visual and textual inputs for multimodal understanding tasks. Built to extend DeepSeek's capabilities beyond text-only processing, it enables applications requiring integrated analysis of images and language.
+
+
+
+
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.
3 months newer
Phi-3.5-MoE-instruct
Microsoft
2024-08-23
DeepSeek VL2
DeepSeek
2024-12-13
+
+
+
+
+
+
+
+
+
+
+
+
Performance Metrics
Context window and performance specifications
Provider Availability & Performance
Available providers and their performance metrics
+
+
+
+
DeepSeek VL2
1 providers
Replicate
Throughput: 22 tok/s
Latency: 0.5ms
+
+
+
+
Phi-3.5-MoE-instruct
+
+
+
+
DeepSeek VL2
Avg Score:0.0%
Providers:1
+
+
+
+
Phi-3.5-MoE-instruct
Avg Score:0.0%
Providers:0