Model Comparison
DeepSeek-V3.2-Exp vs Qwen2.5 14B Instruct
DeepSeek-V3.2-Exp significantly outperforms across most benchmarks.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V3.2-Exp outperforms in 2 benchmarks (GPQA, MMLU-Pro), while Qwen2.5 14B Instruct is better at 0 benchmarks.
DeepSeek-V3.2-Exp significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
Cost data unavailable.
Model Size
Parameter count comparison
DeepSeek-V3.2-Exp has 670.3B more parameters than Qwen2.5 14B Instruct, making it 4559.9% larger.
Context Window
Maximum input and output token capacity
Only DeepSeek-V3.2-Exp specifies input context (163,840 tokens). Only DeepSeek-V3.2-Exp specifies output context (65,536 tokens).
License
Usage and distribution terms
DeepSeek-V3.2-Exp is licensed under MIT, while Qwen2.5 14B Instruct uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
DeepSeek-V3.2-Exp was released on 2025-09-29, while Qwen2.5 14B Instruct was released on 2024-09-19.
DeepSeek-V3.2-Exp is 13 months newer than Qwen2.5 14B Instruct.
Sep 29, 2025
6 months ago
1.0yr newerSep 19, 2024
1.6 years ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Outputs Comparison
Key Takeaways
DeepSeek-V3.2-Exp
View detailsDeepSeek
Qwen2.5 14B Instruct
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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FAQ
Common questions about DeepSeek-V3.2-Exp vs Qwen2.5 14B Instruct