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Qwen 3.8 Flash

Active Parameters

176B

Context Length

1M

Modality

Multimodal

Auxiliary Parameters

450M

Architecture

Mixture of Experts (MoE)

License

other

Release Date

24 Aug 2026

Knowledge Cutoff

-

API Pricing (per 1M)

Input: $0.15 · Output: $0.47

System Requirements

VRAM requirements for different quantization methods and context sizes

1,024 tokens

345.82 GB VRAM

Consumer

18x RTX 4090

24GB VRAM

Datacenter

5x NVIDIA A100

80GB VRAM

Apple Silicon

4x Apple M3 Max

128GB VRAM

1,000,000 tokens

441.85 GB VRAM

Consumer

23x RTX 4090

24GB VRAM

Datacenter

7x NVIDIA A100

80GB VRAM

Apple Silicon

5x Apple M3 Max

128GB VRAM

Architecture Diagram

Input TokensToken EmbeddingPosition: RoPEHidden: 2.6k · Context: 1M · Vocab: 248.3kx 48 layersRMSNormPre-AttentionDeepSeek Sparse Attention24Q / 2KV headsHead dim: 256+RMSNormPre-FFNSparse MoE FFN (10/512 experts)SwiGLUIntermediate: 640+Final RMSNormOutput Logits

Evaluation Benchmarks

Rank

#38

BenchmarkScoreRank

Agentic Index

Artificial Analysis

0.54

5

Agentic Coding

LiveBench Agentic

0.62

10

Web Development

WebDev Arena

1636

11

Graduate-Level QA

GPQA

0.917

18

0.87

23

0.76

25

LiveBench Average

LiveBench Average

0.76

25

0.73

30

0.74

31

Intelligence Index

Artificial Analysis

0.40

40

0.86

43

0.73

44

Rankings

Overall Rank

#38

Coding Rank

#44

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

Sliding Window Attention

No

Sliding Window Size

-

Sliding Window Ratio

-

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

FFN Intermediate Size (Dense)

-

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

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.


Other Qwen 3.8 Models