+
+
+
+
+
+
+
+
VS
VS
VS
VS
VS
VS
DeepSeek VL2 vs Qwen2.5 14B 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.
+
+
+
+
Alibaba Cloud / Qwen Team
Qwen 2.5 14B was developed as a mid-sized instruction-tuned model, designed to balance capability and efficiency for diverse language tasks. Built with 14 billion parameters, it provides strong performance for applications requiring reliable instruction-following without the resource demands of larger models.
2 months newer
Qwen2.5 14B Instruct
Alibaba Cloud / Qwen Team
2024-09-19
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
+
+
+
+
Qwen2.5 14B Instruct
+
+
+
+
DeepSeek VL2
Avg Score:0.0%
Providers:1
+
+
+
+
Qwen2.5 14B Instruct
Avg Score:0.0%
Providers:0