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DeepSeek-V2.5 vs Qwen2.5 VL 32B Instruct
Comprehensive side-by-side LLM comparison
Qwen2.5 VL 32B Instruct leads with 2.7% higher average benchmark score. Qwen2.5 VL 32B Instruct supports multimodal inputs. DeepSeek-V2.5 is available on 3 providers. Both models have their strengths depending on your specific coding needs.
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DeepSeek
DeepSeek-V2.5 was developed as an enhanced iteration of the DeepSeek-V2 architecture, designed to incorporate improvements in model quality and efficiency. Built to advance the DeepSeek foundation model series, it provides refined capabilities for general-purpose language understanding and generation tasks.
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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.
9 months newer
DeepSeek-V2.5
DeepSeek
2024-05-08
Qwen2.5 VL 32B Instruct
Alibaba Cloud / Qwen Team
2025-02-28
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Performance Metrics
Context window and performance specifications
Average performance across 3 common benchmarks
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DeepSeek-V2.5
Average Score:81.4%
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Qwen2.5 VL 32B Instruct
Average Score:84.0%(+2.7%)
Provider Availability & Performance
Available providers and their performance metrics
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DeepSeek-V2.5
3 providers
DeepInfra
Throughput: 63 tok/s
Latency: 0.5ms
DeepSeek
Throughput: 100 tok/s
Latency: 0.5ms
Hyperbolic
Throughput: 100 tok/s
Latency: 0.5ms
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DeepSeek-V2.5
Avg Score:81.4%
Providers:3
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Qwen2.5 VL 32B Instruct
Avg Score:84.0%(+2.7%)
Providers:0