DeepSeek-V3.2 (Thinking) vs Qwen3.8-Flash-Next
Qwen3.8-Flash-Next leads the LLM Stats Score 49.7 to 33.0.
DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Qwen3.8-Flash-Next leads the overall LLM Stats Score 49.7 to 33.0, ranking #15 overall.
In the 4 individual benchmarks reported for both models, Qwen3.8-Flash-Next wins 4; 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-V3.2 (Thinking)
- you want predictable pricing at $0.28/M input and $0.42/M output
Choose Qwen3.8-Flash-Next
- overall performance matters — it scores 49.7 and ranks #15 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 4 of 4 exact shared results
- you want the most recent training data — it shipped Aug 2026
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
14 reported for DeepSeek-V3.2 (Thinking) · 22 for Qwen3.8-Flash-Next
DeepSeek-V3.2 (Thinking) outperforms in 0 benchmarks, while Qwen3.8-Flash-Next is better at 4 benchmarks (GPQA, Humanity's Last Exam, SWE-bench Multilingual, Toolathlon).
Qwen3.8-Flash-Next significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Model Size
Parameter count comparison
DeepSeek-V3.2 (Thinking) has 560.0B more parameters than Qwen3.8-Flash-Next, making it 448.0% larger.
Context Window
Maximum input and output token capacity
Only DeepSeek-V3.2 (Thinking) specifies input context (131,072 tokens). Only DeepSeek-V3.2 (Thinking) specifies output context (65,536 tokens).
Input capabilities
Documented input modalities across available providers
Qwen3.8-Flash-Next supports multimodal inputs, whereas DeepSeek-V3.2 (Thinking) does not.
Qwen3.8-Flash-Next can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V3.2 (Thinking)
Qwen3.8-Flash-Next
License
Usage and distribution terms
DeepSeek-V3.2 (Thinking) is licensed under MIT, while Qwen3.8-Flash-Next uses Qwen Community License 1.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Qwen Community License 1.0
Open weights
Release Timeline
When each model was launched
DeepSeek-V3.2 (Thinking) was released on 2025-12-01, while Qwen3.8-Flash-Next was released on 2026-08-26.
Qwen3.8-Flash-Next is 9 months newer than DeepSeek-V3.2 (Thinking).
Dec 1, 2025
9 months ago
Aug 26, 2026
5 days ago
8mo newerKnowledge 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-V3.2 (Thinking) and Qwen3.8-Flash-Next side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V3.2 (Thinking) vs Qwen3.8-Flash-Next.