DeepSeek-R1-0528 vs Qwen3.8-Flash-Next
Qwen3.8-Flash-Next leads the LLM Stats Score 50.5 to 24.6.
DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Qwen3.8-Flash-Next leads the overall LLM Stats Score 50.5 to 24.6, ranking #14 overall.
In the 3 individual benchmarks reported for both models, Qwen3.8-Flash-Next wins 3; 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-R1-0528
- you want predictable pricing at $0.50/M input and $2.15/M output
Choose Qwen3.8-Flash-Next
- overall performance matters — it scores 50.5 and ranks #14 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 3 of 3 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
16 reported for DeepSeek-R1-0528 · 22 for Qwen3.8-Flash-Next
DeepSeek-R1-0528 outperforms in 0 benchmarks, while Qwen3.8-Flash-Next is better at 3 benchmarks (GPQA, Humanity's Last Exam, SWE-bench Multilingual).
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-R1-0528 has 546.0B more parameters than Qwen3.8-Flash-Next, making it 436.8% larger.
Context Window
Maximum input and output token capacity
Only DeepSeek-R1-0528 specifies input context (131,072 tokens). Only DeepSeek-R1-0528 specifies output context (131,072 tokens).
Input capabilities
Documented input modalities across available providers
Qwen3.8-Flash-Next supports multimodal inputs, whereas DeepSeek-R1-0528 does not.
Qwen3.8-Flash-Next can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-R1-0528
Qwen3.8-Flash-Next
License
Usage and distribution terms
DeepSeek-R1-0528 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-R1-0528 was released on 2025-05-28, while Qwen3.8-Flash-Next was released on 2026-08-26.
Qwen3.8-Flash-Next is 15 months newer than DeepSeek-R1-0528.
May 28, 2025
1.3 years ago
Aug 26, 2026
2 days ago
1.2yr 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-R1-0528 and Qwen3.8-Flash-Next side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-R1-0528 vs Qwen3.8-Flash-Next.