Model Comparison
DeepSeek-V3.2 (Non-thinking) vs Qwen3 VL 30B A3B ThinkingWhich is better in 2026?
Comparing DeepSeek-V3.2 (Non-thinking) and Qwen3 VL 30B A3B Thinking across benchmarks, pricing, and capabilities.
Verdict: DeepSeek-V3.2 (Non-thinking) vs Qwen3 VL 30B A3B Thinking — which is better?
DeepSeek-V3.2 (Non-thinking) (by DeepSeek) and Qwen3 VL 30B A3B Thinking (by Alibaba Cloud / Qwen Team) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.
On price, DeepSeek-V3.2 (Non-thinking) is roughly 1.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Choose DeepSeek-V3.2 (Non-thinking) if…
- cost matters — it's about 1.3x cheaper per token
- you want the most recent training data — it shipped Dec 2025
Choose Qwen3 VL 30B A3B Thinking if…
- you want predictable pricing at $0.20/M input and $0.99/M output
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V3.2 (Non-thinking) and Qwen3 VL 30B A3B Thinking don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V3.2 (Non-thinking) ($0.28/1M tokens) is 1.4x more expensive than Qwen3 VL 30B A3B Thinking ($0.20/1M tokens).
For output processing, DeepSeek-V3.2 (Non-thinking) ($0.42/1M tokens) is 2.4x cheaper than Qwen3 VL 30B A3B Thinking ($0.99/1M tokens).
In conclusion, Qwen3 VL 30B A3B Thinking is more expensive than DeepSeek-V3.2 (Non-thinking).*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V3.2 (Non-thinking) has 654.0B more parameters than Qwen3 VL 30B A3B Thinking, making it 2109.7% larger.
Context Window
Maximum input and output token capacity
Both models have the same input context window of 131,072 tokens. Qwen3 VL 30B A3B Thinking can generate longer responses up to 32,768 tokens, while DeepSeek-V3.2 (Non-thinking) is limited to 8,192 tokens.
Input Capabilities
Supported data types and modalities
Qwen3 VL 30B A3B Thinking supports multimodal inputs, whereas DeepSeek-V3.2 (Non-thinking) does not.
Qwen3 VL 30B A3B Thinking can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V3.2 (Non-thinking)
Qwen3 VL 30B A3B Thinking
License
Usage and distribution terms
DeepSeek-V3.2 (Non-thinking) is licensed under MIT, while Qwen3 VL 30B A3B Thinking 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 VL 30B A3B Thinking was released on 2025-09-22.
DeepSeek-V3.2 (Non-thinking) is 2 months newer than Qwen3 VL 30B A3B Thinking.
Dec 1, 2025
6 months ago
2mo newerSep 22, 2025
8 months ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-V3.2 (Non-thinking) is available from DeepSeek. Qwen3 VL 30B A3B Thinking is available from Novita, DeepInfra.
DeepSeek-V3.2 (Non-thinking)
Qwen3 VL 30B A3B Thinking
Outputs Comparison
Key Takeaways
Qwen3 VL 30B A3B Thinking
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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FAQ
Common questions about DeepSeek-V3.2 (Non-thinking) vs Qwen3 VL 30B A3B Thinking.