DeepSeek-V4-Flash-0423 vs Qwen3.8-Flash-Next
Qwen3.8-Flash-Next leads the LLM Stats Score 50.5 to 36.6.
DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
Qwen3.8-Flash-Next leads the overall LLM Stats Score 50.5 to 36.6, ranking #14 overall.
In the 5 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-V4-Flash-0423
- you want predictable pricing at $0.10/M input and $0.20/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 4 of 5 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
19 reported for DeepSeek-V4-Flash-0423 · 22 for Qwen3.8-Flash-Next
DeepSeek-V4-Flash-0423 outperforms in 1 benchmarks (Humanity's Last Exam), while Qwen3.8-Flash-Next is better at 4 benchmarks (GPQA, SWE-bench Multilingual, SWE-Bench Pro, 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-V4-Flash-0423 has 159.0B more parameters than Qwen3.8-Flash-Next, making it 127.2% larger.
Context Window
Maximum input and output token capacity
Only DeepSeek-V4-Flash-0423 specifies input context (1,048,576 tokens). Only DeepSeek-V4-Flash-0423 specifies output context (65,536 tokens).
Input capabilities
Documented input modalities across available providers
Qwen3.8-Flash-Next supports multimodal inputs, whereas DeepSeek-V4-Flash-0423 does not.
Qwen3.8-Flash-Next can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4-Flash-0423
Qwen3.8-Flash-Next
License
Usage and distribution terms
DeepSeek-V4-Flash-0423 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-V4-Flash-0423 was released on 2026-04-23, while Qwen3.8-Flash-Next was released on 2026-08-26.
Qwen3.8-Flash-Next is 4 months newer than DeepSeek-V4-Flash-0423.
Apr 23, 2026
4 months ago
Aug 26, 2026
2 days ago
4mo 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-V4-Flash-0423 and Qwen3.8-Flash-Next side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Flash-0423 vs Qwen3.8-Flash-Next.