Model Comparison
DeepSeek-V3.2-Exp vs Qwen3 VL 4B ThinkingWhich is better in 2026?
DeepSeek-V3.2-Exp significantly outperforms across most benchmarks. DeepSeek-V3.2-Exp is 1.1x cheaper per token.
Verdict: DeepSeek-V3.2-Exp vs Qwen3 VL 4B Thinking — which is better?
DeepSeek-V3.2-Exp (by DeepSeek) and Qwen3 VL 4B 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.
DeepSeek-V3.2-Exp outperforms in 3 benchmarks (AIME 2025, GPQA, MMLU-Pro), while Qwen3 VL 4B Thinking is better at 0 benchmarks. DeepSeek-V3.2-Exp significantly outperforms across most benchmarks.
On price, DeepSeek-V3.2-Exp is roughly 1.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3 VL 4B Thinking also accepts a larger context window (262,144 input tokens), making it the stronger choice for long documents and large codebases.
Choose DeepSeek-V3.2-Exp if…
- you want the strongest raw capability — it leads on 3 of 3 shared benchmarks
- cost matters — it's about 1.1x cheaper per token
- you want the most recent training data — it shipped Sep 2025
Choose Qwen3 VL 4B Thinking if…
- you process long inputs — it offers a 262,144 token context window
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V3.2-Exp outperforms in 3 benchmarks (AIME 2025, GPQA, MMLU-Pro), while Qwen3 VL 4B Thinking 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
For input processing, DeepSeek-V3.2-Exp ($0.27/1M tokens) is 2.7x more expensive than Qwen3 VL 4B Thinking ($0.10/1M tokens).
For output processing, DeepSeek-V3.2-Exp ($0.41/1M tokens) is 2.4x cheaper than Qwen3 VL 4B Thinking ($1.00/1M tokens).
In conclusion, Qwen3 VL 4B Thinking is more expensive than DeepSeek-V3.2-Exp.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V3.2-Exp has 681.0B more parameters than Qwen3 VL 4B Thinking, making it 17025.0% larger.
Context Window
Maximum input and output token capacity
Qwen3 VL 4B Thinking accepts 262,144 input tokens compared to DeepSeek-V3.2-Exp's 163,840 tokens. Qwen3 VL 4B Thinking can generate longer responses up to 262,144 tokens, while DeepSeek-V3.2-Exp is limited to 65,536 tokens.
Input Capabilities
Supported data types and modalities
Qwen3 VL 4B Thinking supports multimodal inputs, whereas DeepSeek-V3.2-Exp does not.
Qwen3 VL 4B Thinking can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V3.2-Exp
Qwen3 VL 4B Thinking
License
Usage and distribution terms
DeepSeek-V3.2-Exp is licensed under MIT, while Qwen3 VL 4B 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-Exp was released on 2025-09-29, while Qwen3 VL 4B Thinking was released on 2025-09-22.
DeepSeek-V3.2-Exp is 0 month newer than Qwen3 VL 4B Thinking.
Sep 29, 2025
8 months ago
1w 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-Exp is available from Novita. Qwen3 VL 4B Thinking is available from DeepInfra.
DeepSeek-V3.2-Exp
Qwen3 VL 4B Thinking
Outputs Comparison
Key Takeaways
DeepSeek-V3.2-Exp
View detailsDeepSeek
Qwen3 VL 4B Thinking
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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FAQ
Common questions about DeepSeek-V3.2-Exp vs Qwen3 VL 4B Thinking.