+
+
+
+
+
+
+
+
VS
VS
VS
VS
VS
VS
DeepSeek-V3 vs Qwen2.5 VL 32B Instruct
Comprehensive side-by-side LLM comparison
DeepSeek-V3 leads with 10.1% higher average benchmark score. Qwen2.5 VL 32B Instruct supports multimodal inputs. Overall, DeepSeek-V3 is the stronger choice for coding tasks.
+
+
+
+
DeepSeek
DeepSeek-V3 was introduced as a major architectural advancement, developed with 671B mixture-of-experts parameters and trained on 14.8 trillion tokens. Built to be three times faster than V2 while maintaining open-source availability, it demonstrates competitive performance against frontier closed-source models and represents a significant leap in efficient large-scale model design.
+
+
+
+
Alibaba Cloud / Qwen Team
Qwen2.5-VL 32B was developed as a mid-sized vision-language model, designed to balance multimodal capability with practical deployment considerations. Built with 32 billion parameters for vision and language integration, it serves applications requiring strong visual understanding without flagship-scale resources.
2 months newer
DeepSeek-V3
DeepSeek
2024-12-25
Qwen2.5 VL 32B Instruct
Alibaba Cloud / Qwen Team
2025-02-28
+
+
+
+
+
+
+
+
+
+
+
+
Performance Metrics
Context window and performance specifications
Average performance across 3 common benchmarks
+
+
+
+
DeepSeek-V3
Average Score:74.5%(+10.1%)
+
+
+
+
Qwen2.5 VL 32B Instruct
Average Score:64.4%
Provider Availability & Performance
Available providers and their performance metrics
+
+
+
+
DeepSeek-V3
1 providers
DeepSeek
Throughput: 100 tok/s
Latency: 0.5ms
+
+
+
+
Qwen2.5 VL 32B Instruct
+
+
+
+
DeepSeek-V3
Avg Score:74.5%(+10.1%)
Providers:1
+
+
+
+
Qwen2.5 VL 32B Instruct
Avg Score:64.4%
Providers:0