DeepSeek-V4-Pro-0813 vs Qwen3 VL 4B Thinking
Comparing DeepSeek-V4-Pro-0813 and Qwen3 VL 4B Thinking across benchmarks, pricing, and capabilities.
DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
DeepSeek-V4-Pro-0813 and Qwen3 VL 4B Thinking trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
On price, Qwen3 VL 4B Thinking is roughly 1.7x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4-Pro-0813 also accepts a larger context window (1,048,576 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-V4-Pro-0813
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Aug 2026
Choose Qwen3 VL 4B Thinking
- cost matters — it's about 1.7x cheaper per token
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V4-Pro-0813 and Qwen3 VL 4B Thinkingdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4-Pro-0813 ($0.43/1M tokens) is 4.3x more expensive than Qwen3 VL 4B Thinking ($0.10/1M tokens).
For output processing, DeepSeek-V4-Pro-0813 ($0.87/1M tokens) is 1.1x cheaper than Qwen3 VL 4B Thinking ($1.00/1M tokens).
In conclusion, DeepSeek-V4-Pro-0813 is more expensive than Qwen3 VL 4B Thinking.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4-Pro-0813 has 1596.0B more parameters than Qwen3 VL 4B Thinking, making it 39900.0% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4-Pro-0813 accepts 1,048,576 input tokens compared to Qwen3 VL 4B Thinking's 262,144 tokens. DeepSeek-V4-Pro-0813 can generate longer responses up to 393,216 tokens, while Qwen3 VL 4B Thinking is limited to 262,144 tokens.
Input Capabilities
Supported data types and modalities
Qwen3 VL 4B Thinking supports multimodal inputs, whereas DeepSeek-V4-Pro-0813 does not.
Qwen3 VL 4B Thinking can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4-Pro-0813
Qwen3 VL 4B Thinking
License
Usage and distribution terms
DeepSeek-V4-Pro-0813 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-V4-Pro-0813 was released on 2026-08-13, while Qwen3 VL 4B Thinking was released on 2025-09-22.
DeepSeek-V4-Pro-0813 is 11 months newer than Qwen3 VL 4B Thinking.
Aug 13, 2026
1 weeks ago
10mo newerSep 22, 2025
11 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-V4-Pro-0813 is available from DeepSeek, DeepInfra, Novita, Together. Qwen3 VL 4B Thinking is available from DeepInfra.
DeepSeek-V4-Pro-0813
Qwen3 VL 4B Thinking
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Pro-0813 and Qwen3 VL 4B Thinking side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Pro-0813 vs Qwen3 VL 4B Thinking.