# Qwen3.8-Flash-Next (125B-A6B)

> Experimental vision-language MoE whose text backbone combines Gated DeltaNet, micro-block QSA, four-branch gated residuals, and n-gram embeddings; the figure omits the vision encoder and MTP head.

| Property | Value |
| --- | --- |
| Lab | Alibaba Cloud (China) |
| Released | 2026-08-26 |
| Decoder type | Sparse hybrid |
| Family | Hybrid MoE |
| Scale | 125B core, 6B active (4.8% active); plus 51B n-gram embeddings and 4B MTP |
| Context window | 262,144 tokens |
| Attention | 3:1 Gated DeltaNet and Qwen Sparse Attention (QSA) |
| Layer mix | 12 QSA + 36 DeltaNet |
| KV cache / token (bf16) | 24 KiB (Very low) |
| License | Qwen Community License 1.0 |

**Key detail:** Replaces full attention with micro-block QSA while using four gated residual branches and a 20M-entry n-gram embedding at layer 2.

## Architecture concepts

- [Hybrid Attention (Hybrid attention)](https://llm-atlas-7or.pages.dev/concepts/hybrid-attention.md): Mixes cheap linear-attention or state-space layers with a few full-attention layers.
- [Qwen Sparse Attention (QSA)](https://llm-atlas-7or.pages.dev/concepts/qwen-sparse-attention.md): Like DSA, but the indexer picks whole four-token blocks rather than single tokens.
- [Gated Residuals (GR)](https://llm-atlas-7or.pages.dev/concepts/gated-residuals.md): Gated read/write modules over four residual streams, without mHC's mixing matrix.
- [Grouped-Query Attention (GQA)](https://llm-atlas-7or.pages.dev/concepts/gqa.md): Several query heads share one key/value head, shrinking the KV cache with little quality loss.
- [Gated Attention (Gated attention)](https://llm-atlas-7or.pages.dev/concepts/gated-attention.md): A learned sigmoid gate scales the attention output before it re-enters the residual stream.
- [Per-Layer Embeddings (PLE)](https://llm-atlas-7or.pages.dev/concepts/per-layer-embeddings.md): Each layer gets its own token-specific embedding signal from a large lookup table.
- [Mixture of Experts (MoE)](https://llm-atlas-7or.pages.dev/concepts/moe.md): Many feed-forward experts, but a router sends each token to only a few of them.
- [QK-Norm (QK-Norm)](https://llm-atlas-7or.pages.dev/concepts/qk-norm.md): RMSNorm applied to queries and keys before attention to keep logits well-behaved.
- [Multi-Token Prediction (MTP)](https://llm-atlas-7or.pages.dev/concepts/mtp.md): Extra heads learn to predict several future tokens, and can draft tokens at inference.

## Benchmark (Artificial Analysis Intelligence Index)

- AA Intelligence Index v4.3.2: total 39.8 (General 35.8, Scientific 24.6, Coding 37.9, Agents 55.1)

## Links

- Architecture diagram: https://sebastianraschka.com/llm-architecture-gallery/images/architectures/qwen3-8-flash-next.webp
- config.json: https://huggingface.co/Qwen/Qwen3.8-Flash-Next/blob/main/config.json
- License: https://huggingface.co/Qwen/Qwen3.8-Flash-Next/blob/main/LICENSE
- Tech report: https://github.com/QwenLM/Qwen3.8-Flash-Next/blob/main/tech_report.pdf
- Source card (source of truth): https://sebastianraschka.com/llm-architecture-gallery/#card-qwen3-8-flash-next-125b-a6b
- Web page: https://llm-atlas-7or.pages.dev/models/qwen3-8-flash-next-125b-a6b/
- JSON: https://llm-atlas-7or.pages.dev/api/models/qwen3-8-flash-next-125b-a6b.json

_Data from Sebastian Raschka's LLM Architecture Gallery (https://sebastianraschka.com/llm-architecture-gallery/). Synced 2026-11-04._