Qwen 3.8 Flash
Active Parameters
176B
Context Length
1M
Modality
Multimodal
Auxiliary Parameters
450M
Architecture
Mixture of Experts
KV Cache Rate
96 KB/tok
License
other
Release Date
24 Aug 2026
Knowledge Cutoff
-
API Pricing (per 1M)
Input: $0.15 · Output: $0.47
Hardware Sizing
Precision
Context
Workload Memory: 342.25 GB
FP16 · 8K context
Base Weights: 339.00 GB
KV Cache: 0.75 GB
Runtime & Graphs: 2.50 GB
Minimum Required Hardware
8x NVIDIA H100 80GB
342.3 / 576.0 GB usable · TP 8
Determine requirements for your specific workload:
Open in VRAM Calculator
Architecture Diagram
Evaluation Benchmarks
Rank
#42
| Benchmark | Score | Rank |
|---|---|---|
Coding | 0.87 | 4 |
Agentic Index | 0.54 | 5 |
Graduate-Level QA | 0.917 | 18 |
Reasoning | 0.87 | 27 |
Coding Index | 0.73 | 30 |
Intelligence Index | 0.40 | 35 |
Data Analysis | 0.74 | 36 |
Mathematics | 0.86 | 49 |
General | 0.69 | 55 |
LiveBench Average | 0.69 | 55 |
Rankings
Overall Rank
#42
Coding Rank
#15
About Qwen 3.8 Flash
Qwen 3.8 Flash is an efficient multimodal reasoning model from Alibaba designed for rapid inference across coding, agentic workflows, and document analysis. It delivers strong vision-language understanding while maintaining low latency and a long context window.
Technical Specifications
Attention
Attention Structure
DeepSeek Sparse Attention
Attention Heads
24
Key-Value Heads
2
Attention Head Dimension
256
Position Embedding
ROPE
RoPE Theta
10,000,000
Linear Attention
Yes
Linear Attention Ratio
75.0%
Normalization
RMS Normalization
Activation Function
SwigLU
Dimensions
Auxiliary Parameters
450M
Hidden Dimension Size
2,560
Number of Layers
48
Multi-Token Prediction Heads
1
Tokenizer
Vocabulary Size
248,320
Mixture of Experts
Total Expert Parameters
6.0B
Number of Experts
512
Active Experts
10
Shared Experts
1
FFN Intermediate Size (per Expert)
640
Dense Layers Before MoE
0
Resources
About Qwen 3.8
Alibaba's Qwen 3.8 generation represents the frontier hybrid Mixture-of-Experts architecture designed for coding, professional work, research, and long-horizon agentic tasks. It features a 2.4-trillion parameter architecture (95B active per token) combining Gated DeltaNet linear attention with standard Gated Attention, available both as open weights and as a hosted flagship service.