DeepSeek-V3.1 vs Qwen3 VL 30B A3B Thinking
DeepSeek-V3.1 significantly outperforms across most benchmarks. Qwen3 VL 30B A3B Thinking is 1.1x cheaper per token.
DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
DeepSeek-V3.1 outperforms in 4 benchmarks (GPQA, MMLU-Pro, MMLU-Redux, SimpleQA), while Qwen3 VL 30B A3B Thinking is better at 1 benchmark (AIME 2025). DeepSeek-V3.1 significantly outperforms across most benchmarks.
On price, Qwen3 VL 30B A3B Thinking is roughly 1.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V3.1 also accepts a larger context window (163,840 input tokens), making it the stronger choice for long documents and large codebases.
Based on current benchmark, pricing, and model metadata for 2026.
Choose DeepSeek-V3.1
- you want the strongest raw capability — it leads on 4 of 5 shared benchmarks
- you process long inputs — it offers a 163,840 token context window
Choose Qwen3 VL 30B A3B Thinking
- cost matters — it's about 1.1x cheaper per token
- you want the most recent training data — it shipped Sep 2025
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V3.1 outperforms in 4 benchmarks (GPQA, MMLU-Pro, MMLU-Redux, SimpleQA), while Qwen3 VL 30B A3B Thinking is better at 1 benchmark (AIME 2025).
DeepSeek-V3.1 significantly outperforms across most benchmarks.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V3.1 ($0.27/1M tokens) is 1.4x more expensive than Qwen3 VL 30B A3B Thinking ($0.20/1M tokens).
For output processing, DeepSeek-V3.1 ($1.00/1M tokens) is 1.0x more expensive than Qwen3 VL 30B A3B Thinking ($0.99/1M tokens).
In conclusion, DeepSeek-V3.1 is more expensive than Qwen3 VL 30B A3B Thinking.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V3.1 has 640.0B more parameters than Qwen3 VL 30B A3B Thinking, making it 2064.5% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V3.1 accepts 163,840 input tokens compared to Qwen3 VL 30B A3B Thinking's 131,072 tokens. DeepSeek-V3.1 can generate longer responses up to 163,840 tokens, while Qwen3 VL 30B A3B Thinking is limited to 32,768 tokens.
Input Capabilities
Supported data types and modalities
Qwen3 VL 30B A3B Thinking supports multimodal inputs, whereas DeepSeek-V3.1 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-V3.1
Qwen3 VL 30B A3B Thinking
License
Usage and distribution terms
DeepSeek-V3.1 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-V3.1 was released on 2025-01-10, while Qwen3 VL 30B A3B Thinking was released on 2025-09-22.
Qwen3 VL 30B A3B Thinking is 9 months newer than DeepSeek-V3.1.
Jan 10, 2025
1.6 years ago
Sep 22, 2025
11 months ago
8mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-V3.1 is available from DeepInfra, Novita. Qwen3 VL 30B A3B Thinking is available from Novita, DeepInfra.
DeepSeek-V3.1
Qwen3 VL 30B A3B Thinking
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V3.1 and Qwen3 VL 30B A3B Thinking side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V3.1 vs Qwen3 VL 30B A3B Thinking.