DeepSeek-V4.1-Flash vs Qwen3.5-2B
DeepSeek-V4.1-Flash leads the LLM Stats Score 51.8 to 3.8.
DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
DeepSeek-V4.1-Flash leads the overall LLM Stats Score 51.8 to 3.8, ranking #13 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.
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 #13 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 want the most recent training data — it shipped Sep 2026
Choose Qwen3.5-2B
- you are already invested in the Alibaba Cloud / Qwen Team ecosystem
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 · 20 for Qwen3.5-2B
DeepSeek-V4.1-Flash outperforms in 1 benchmarks (GPQA), while Qwen3.5-2B 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
Model Size
Parameter count comparison
DeepSeek-V4.1-Flash has 761.2B more parameters than Qwen3.5-2B, making it 38060.3% larger.
Context Window
Maximum input and output token capacity
Only DeepSeek-V4.1-Flash specifies input context (1,040,000 tokens). Only DeepSeek-V4.1-Flash specifies output context (393,216 tokens).
Input capabilities
Documented input modalities across available providers
Both DeepSeek-V4.1-Flash and Qwen3.5-2B support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
DeepSeek-V4.1-Flash
Qwen3.5-2B
License
Usage and distribution terms
DeepSeek-V4.1-Flash is licensed under MIT, while Qwen3.5-2B 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 Qwen3.5-2B was released on 2026-03-02.
DeepSeek-V4.1-Flash is 6 months newer than Qwen3.5-2B.
Sep 10, 2026
1 weeks ago
6mo newerMar 2, 2026
6 months ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4.1-Flash and Qwen3.5-2B side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4.1-Flash vs Qwen3.5-2B.