Model Comparison
DeepSeek-V3.2 (Non-thinking) vs Qwen3-Coder 480B A35B Instruct
Comparing DeepSeek-V3.2 (Non-thinking) and Qwen3-Coder 480B A35B Instruct across benchmarks, pricing, and capabilities.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V3.2 (Non-thinking) and Qwen3-Coder 480B A35B Instruct don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Arena Performance
Human preference votes
Model Size
Parameter count comparison
DeepSeek-V3.2 (Non-thinking) has 205.0B more parameters than Qwen3-Coder 480B A35B Instruct, making it 42.7% larger.
Context Window
Maximum input and output token capacity
Only DeepSeek-V3.2 (Non-thinking) specifies input context (131,072 tokens). Only DeepSeek-V3.2 (Non-thinking) specifies output context (8,192 tokens).
License
Usage and distribution terms
DeepSeek-V3.2 (Non-thinking) is licensed under MIT, while Qwen3-Coder 480B A35B 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 (Non-thinking) was released on 2025-12-01, while Qwen3-Coder 480B A35B Instruct was released on 2025-01-31.
DeepSeek-V3.2 (Non-thinking) is 10 months newer than Qwen3-Coder 480B A35B Instruct.
Dec 1, 2025
5 months ago
10mo newerJan 31, 2025
1.3 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
Qwen3-Coder 480B A35B Instruct
View detailsAlibaba Cloud / Qwen Team
No standout differentiators in the data we have for this pair.
Detailed Comparison
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FAQ
Common questions about DeepSeek-V3.2 (Non-thinking) vs Qwen3-Coder 480B A35B Instruct.