Model Comparison
DeepSeek-V4-Flash-0731 vs Qwen3 VL 4B ThinkingWhich is better in 2026?
Comparing DeepSeek-V4-Flash-0731 and Qwen3 VL 4B Thinking across benchmarks, pricing, and capabilities.
Verdict: DeepSeek-V4-Flash-0731 vs Qwen3 VL 4B Thinking — which is better?
DeepSeek-V4-Flash-0731 (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.
On price, DeepSeek-V4-Flash-0731 is roughly 2.9x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4-Flash-0731 also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.
Choose DeepSeek-V4-Flash-0731 if…
- cost matters — it's about 2.9x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Jul 2026
Choose Qwen3 VL 4B Thinking if…
- you want predictable pricing at $0.10/M input and $1.00/M output
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V4-Flash-0731 and Qwen3 VL 4B Thinkingdon'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-V4-Flash-0731 ($0.09/1M tokens) is 1.1x cheaper than Qwen3 VL 4B Thinking ($0.10/1M tokens).
For output processing, DeepSeek-V4-Flash-0731 ($0.18/1M tokens) is 5.6x cheaper than Qwen3 VL 4B Thinking ($1.00/1M tokens).
In conclusion, Qwen3 VL 4B Thinking is more expensive than DeepSeek-V4-Flash-0731.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4-Flash-0731 has 300.0B more parameters than Qwen3 VL 4B Thinking, making it 7500.0% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4-Flash-0731 accepts 1,048,576 input tokens compared to Qwen3 VL 4B Thinking's 262,144 tokens. Qwen3 VL 4B Thinking can generate longer responses up to 262,144 tokens, while DeepSeek-V4-Flash-0731 is limited to 65,536 tokens.
Input Capabilities
Supported data types and modalities
Qwen3 VL 4B Thinking supports multimodal inputs, whereas DeepSeek-V4-Flash-0731 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-Flash-0731
Qwen3 VL 4B Thinking
License
Usage and distribution terms
DeepSeek-V4-Flash-0731 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-Flash-0731 was released on 2026-07-31, while Qwen3 VL 4B Thinking was released on 2025-09-22.
DeepSeek-V4-Flash-0731 is 10 months newer than Qwen3 VL 4B Thinking.
Jul 31, 2026
3 days ago
10mo newerSep 22, 2025
10 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-Flash-0731 is available from DeepInfra, Fireworks, Novita. Qwen3 VL 4B Thinking is available from DeepInfra.
DeepSeek-V4-Flash-0731
Qwen3 VL 4B Thinking
Outputs Comparison
Key Takeaways
Qwen3 VL 4B Thinking
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against DeepSeek-V4-Flash-0731 and Qwen3 VL 4B Thinking side-by-side, then vote on the output you prefer.
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FAQ
Common questions about DeepSeek-V4-Flash-0731 vs Qwen3 VL 4B Thinking.