DeepSeek-V4.1-Flash-1017 vs QwQ-32B
DeepSeek-V4.1-Flash-1017 leads the LLM Stats Score 51.2 to 14.3.
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 14.3, ranking #15 overall.
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 coding — it leads those capability indexes
- you want the most recent training data — it shipped Oct 2026
Choose QwQ-32B
- 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
11 reported for DeepSeek-V4.1-Flash-1017 · 7 for QwQ-32B
DeepSeek-V4.1-Flash-1017 and QwQ-32Bdon'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
Model Size
Parameter count comparison
DeepSeek-V4.1-Flash-1017 has 730.7B more parameters than QwQ-32B, making it 2248.3% larger.
Context Window
Maximum input and output token capacity
Only DeepSeek-V4.1-Flash-1017 specifies input context (1,048,576 tokens). Only DeepSeek-V4.1-Flash-1017 specifies output context (393,216 tokens).
Input capabilities
Documented input modalities across available providers
DeepSeek-V4.1-Flash-1017 supports multimodal inputs, whereas QwQ-32B does not.
DeepSeek-V4.1-Flash-1017 can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4.1-Flash-1017
QwQ-32B
License
Usage and distribution terms
DeepSeek-V4.1-Flash-1017 is licensed under MIT, while QwQ-32B 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 QwQ-32B was released on 2025-03-05.
DeepSeek-V4.1-Flash-1017 is 20 months newer than QwQ-32B.
Oct 17, 2026
2 days ago
1.6yr newerMar 5, 2025
1.6 years ago
Knowledge Cutoff
When training data ends
QwQ-32B has a documented knowledge cutoff of 2024-11-28, while DeepSeek-V4.1-Flash-1017's cutoff date is not specified.
We can confirm QwQ-32B's training data extends to 2024-11-28, but cannot make a direct comparison without DeepSeek-V4.1-Flash-1017's cutoff date.
—
Nov 2024
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4.1-Flash-1017 and QwQ-32B side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4.1-Flash-1017 vs QwQ-32B.