DeepSeek-V4.1-Flash vs Qwen3 VL 4B Thinking
DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 12.9. Qwen3 VL 4B Thinking is 1.0x cheaper per token.
DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
DeepSeek-V4.1-Flash leads the overall LLM Stats Score 51.8 to 12.9, ranking #12 overall.
In the 1 individual benchmarks reported for both models, DeepSeek-V4.1-Flash wins 1; this is a narrower head-to-head signal than the composite indexes.
DeepSeek-V4.1-Flash also accepts a larger context window (1,040,000 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-V4.1-Flash
- overall performance matters — it scores 51.8 and ranks #12 on LLM Stats
- your work emphasizes reasoning and agents — it leads those capability indexes
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- you process long inputs — it offers a 1,040,000 token context window
- you want the most recent training data — it shipped Sep 2026
Choose Qwen3 VL 4B Thinking
- you want predictable pricing at $0.10/M input and $1.00/M output
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
20 reported for DeepSeek-V4.1-Flash · 48 for Qwen3 VL 4B Thinking
DeepSeek-V4.1-Flash outperforms in 1 benchmarks (GPQA), while Qwen3 VL 4B Thinking is better at 0 benchmarks.
DeepSeek-V4.1-Flash significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4.1-Flash ($0.22/1M tokens) is 2.2x more expensive than Qwen3 VL 4B Thinking ($0.10/1M tokens).
For output processing, DeepSeek-V4.1-Flash ($0.66/1M tokens) is 1.5x cheaper than Qwen3 VL 4B Thinking ($1.00/1M tokens).
In conclusion, DeepSeek-V4.1-Flash is more expensive than Qwen3 VL 4B Thinking.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4.1-Flash has 759.2B more parameters than Qwen3 VL 4B Thinking, making it 18980.1% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4.1-Flash accepts 1,040,000 input tokens compared to Qwen3 VL 4B Thinking's 262,144 tokens. DeepSeek-V4.1-Flash can generate longer responses up to 393,216 tokens, while Qwen3 VL 4B Thinking is limited to 262,144 tokens.
Input capabilities
Documented input modalities across available providers
Both DeepSeek-V4.1-Flash and Qwen3 VL 4B Thinking support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
DeepSeek-V4.1-Flash
Qwen3 VL 4B Thinking
License
Usage and distribution terms
DeepSeek-V4.1-Flash 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-V4.1-Flash was released on 2026-09-10, while Qwen3 VL 4B Thinking was released on 2025-09-22.
DeepSeek-V4.1-Flash is 12 months newer than Qwen3 VL 4B Thinking.
Sep 10, 2026
0 days ago
11mo 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-V4.1-Flash is available from Fireworks, DeepInfra, DeepSeek, Novita. Qwen3 VL 4B Thinking is available from DeepInfra.
DeepSeek-V4.1-Flash
Qwen3 VL 4B Thinking
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4.1-Flash and Qwen3 VL 4B Thinking side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4.1-Flash vs Qwen3 VL 4B Thinking.