DeepSeek-V4.1-Flash-1017 vs Qwen3 VL 4B Instruct
DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.2 to 10.2. Qwen3 VL 4B Instruct is 2.3x cheaper per token.
DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
DeepSeek-V4.1-Flash-1017 leads the overall LLM Stats Score 51.2 to 10.2, ranking #15 overall.
On price, Qwen3 VL 4B Instruct is roughly 2.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4.1-Flash-1017 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 LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-V4.1-Flash-1017
- overall performance matters — it scores 51.2 and ranks #15 on LLM Stats
- your work emphasizes reasoning and agents — it leads those capability indexes
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Oct 2026
Choose Qwen3 VL 4B Instruct
- cost matters — it's about 2.3x cheaper per token
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
11 reported for DeepSeek-V4.1-Flash-1017 · 45 for Qwen3 VL 4B Instruct
DeepSeek-V4.1-Flash-1017 and Qwen3 VL 4B Instructdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
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-1017 ($0.30/1M tokens) is 3.0x more expensive than Qwen3 VL 4B Instruct ($0.10/1M tokens).
For output processing, DeepSeek-V4.1-Flash-1017 ($1.20/1M tokens) is 2.0x more expensive than Qwen3 VL 4B Instruct ($0.60/1M tokens).
In conclusion, DeepSeek-V4.1-Flash-1017 is more expensive than Qwen3 VL 4B Instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4.1-Flash-1017 has 759.2B more parameters than Qwen3 VL 4B Instruct, making it 18980.1% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4.1-Flash-1017 accepts 1,048,576 input tokens compared to Qwen3 VL 4B Instruct's 262,144 tokens. DeepSeek-V4.1-Flash-1017 can generate longer responses up to 393,216 tokens, while Qwen3 VL 4B Instruct is limited to 262,144 tokens.
Input capabilities
Documented input modalities across available providers
Both DeepSeek-V4.1-Flash-1017 and Qwen3 VL 4B Instruct support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
DeepSeek-V4.1-Flash-1017
Qwen3 VL 4B Instruct
License
Usage and distribution terms
DeepSeek-V4.1-Flash-1017 is licensed under MIT, while Qwen3 VL 4B Instruct 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-1017 was released on 2026-10-17, while Qwen3 VL 4B Instruct was released on 2025-09-22.
DeepSeek-V4.1-Flash-1017 is 13 months newer than Qwen3 VL 4B Instruct.
Oct 17, 2026
5 days ago
1.1yr newerSep 22, 2025
1.1 years 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-1017 is available from DeepSeek. Qwen3 VL 4B Instruct is available from DeepInfra.
DeepSeek-V4.1-Flash-1017
Qwen3 VL 4B Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4.1-Flash-1017 and Qwen3 VL 4B Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4.1-Flash-1017 vs Qwen3 VL 4B Instruct.