DeepSeek-V3.2 (Non-thinking) vs Qwen3 VL 235B A22B Thinking
Comparing DeepSeek-V3.2 (Non-thinking) and Qwen3 VL 235B A22B Thinking across benchmarks, pricing, and capabilities.
DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
DeepSeek-V3.2 (Non-thinking) and Qwen3 VL 235B A22B Thinking trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
On price, DeepSeek-V3.2 (Non-thinking) is roughly 3.8x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3 VL 235B A22B Thinking also accepts a larger context window (262,144 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-V3.2 (Non-thinking)
- cost matters — it's about 3.8x cheaper per token
- you want the most recent training data — it shipped Dec 2025
Choose Qwen3 VL 235B A22B Thinking
- you process long inputs — it offers a 262,144 token context window
At a glance
The differences that matter most.
Individual benchmarks
0 reported for DeepSeek-V3.2 (Non-thinking) · 67 for Qwen3 VL 235B A22B Thinking
DeepSeek-V3.2 (Non-thinking) and Qwen3 VL 235B A22B Thinkingdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V3.2 (Non-thinking) ($0.28/1M tokens) is 1.6x cheaper than Qwen3 VL 235B A22B Thinking ($0.45/1M tokens).
For output processing, DeepSeek-V3.2 (Non-thinking) ($0.42/1M tokens) is 8.3x cheaper than Qwen3 VL 235B A22B Thinking ($3.49/1M tokens).
In conclusion, Qwen3 VL 235B A22B 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 449.0B more parameters than Qwen3 VL 235B A22B Thinking, making it 190.3% larger.
Context Window
Maximum input and output token capacity
Qwen3 VL 235B A22B Thinking accepts 262,144 input tokens compared to DeepSeek-V3.2 (Non-thinking)'s 131,072 tokens. Qwen3 VL 235B A22B Thinking can generate longer responses up to 262,144 tokens, while DeepSeek-V3.2 (Non-thinking) is limited to 8,192 tokens.
Input capabilities
Documented input modalities across available providers
Qwen3 VL 235B A22B Thinking supports multimodal inputs, whereas DeepSeek-V3.2 (Non-thinking) does not.
Qwen3 VL 235B A22B 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 235B A22B Thinking
License
Usage and distribution terms
DeepSeek-V3.2 (Non-thinking) is licensed under MIT, while Qwen3 VL 235B A22B 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 235B A22B Thinking was released on 2025-09-22.
DeepSeek-V3.2 (Non-thinking) is 2 months newer than Qwen3 VL 235B A22B Thinking.
Dec 1, 2025
9 months ago
2mo newerSep 22, 2025
12 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 235B A22B Thinking is available from DeepInfra, Novita.
DeepSeek-V3.2 (Non-thinking)
Qwen3 VL 235B A22B Thinking
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V3.2 (Non-thinking) and Qwen3 VL 235B A22B Thinking side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V3.2 (Non-thinking) vs Qwen3 VL 235B A22B Thinking.