DeepSeek-V4-Flash-0731 vs Qwen3.8-27B
DeepSeek-V4-Flash-0731 and Qwen3.8-27B are closely matched at 45.1 and 45.2 on the LLM Stats Score.
DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
DeepSeek-V4-Flash-0731 and Qwen3.8-27B are closely matched on the overall LLM Stats Score at 45.1 and 45.2.
In the 3 individual benchmarks reported for both models, DeepSeek-V4-Flash-0731 wins 2; this is a narrower head-to-head signal than the composite indexes.
DeepSeek-V4-Flash-0731 also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-V4-Flash-0731
- you value its reported benchmark strengths — it wins 2 of 3 exact shared results
- you process long inputs — it offers a 1,048,576 token context window
Choose Qwen3.8-27B
- 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
9 reported for DeepSeek-V4-Flash-0731 · 26 for Qwen3.8-27B
DeepSeek-V4-Flash-0731 outperforms in 2 benchmarks (NL2Repo, Terminal-Bench 2.1), while Qwen3.8-27B is better at 1 benchmark (Agents' Last Exam).
DeepSeek-V4-Flash-0731 shows notably better performance in the majority of benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Model Size
Parameter count comparison
DeepSeek-V4-Flash-0731 has 276.2B more parameters than Qwen3.8-27B, making it 994.3% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4-Flash-0731 accepts 1,048,576 input tokens compared to Qwen3.8-27B's 262,144 tokens. DeepSeek-V4-Flash-0731 can generate longer responses up to 384,000 tokens, while Qwen3.8-27B is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Qwen3.8-27B supports multimodal inputs, whereas DeepSeek-V4-Flash-0731 does not.
Qwen3.8-27B can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4-Flash-0731
Qwen3.8-27B
License
Usage and distribution terms
DeepSeek-V4-Flash-0731 is licensed under MIT, while Qwen3.8-27B 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-Flash-0731 was released on 2026-07-31, while Qwen3.8-27B was released on 2026-08-14.
Qwen3.8-27B is 0 month newer than DeepSeek-V4-Flash-0731.
Jul 31, 2026
1 months ago
Aug 14, 2026
3 weeks ago
2w newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-V4-Flash-0731 is available from DeepInfra, Novita, Fireworks. Qwen3.8-27B is available from FriendliAI.
DeepSeek-V4-Flash-0731
Qwen3.8-27B
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Flash-0731 and Qwen3.8-27B side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Flash-0731 vs Qwen3.8-27B.