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models / compare / qwen3-8-vs-deepseek-v4-1-flash

Qwen3.8-2.4T-A95B vs DeepSeek-V4.1-Flash

How Qwen3.8-2.4T-A95B (Alibaba Qwen, August 2026) and DeepSeek-V4.1-Flash (DeepSeek, September 2026) are built, drawn on the same November 2026 blueprint. They make different choices on 11 of 27 components. Select a component in either diagram to find it in the table.

--- frontier-llm@2026-11+++ qwen3-8@2026-08+0~3−4

Qwen3.8-2.4T-A95B

Alibaba Qwen, August 2026, 2.4T total, 95B active

Text · code · tool outputText · code · tool outputImages · video · audioImages · video · audioByte-level BPE tokenizer — 128K–262K vocabByte-level BPE tokenizer128K–262K vocabVision / audio encoder — native ViT or encoder-free patchesVision / audio encodernative ViT or encoder-free patchesToken embedding — media embeds spliced in placeToken embeddingmedia embeds spliced in placeEngram n-gram memory — hashed 2–4-gram lookup (V4.1)Engram n-gram memoryhashed 2–4-gram lookup (V4.1)Single residual stream — Residual & normSingle residual streamResidual & normRMSNorm — pre-normRMSNormpre-normQK-Norm — per-head RMSNorm on q, kQK-Normper-head RMSNorm on q, kPartial RoPE / NoPE — rotate 64 dims, or nonePartial RoPE / NoPErotate 64 dims, or noneGated DeltaNet / KDA — linear · ~3 of every 4 layersGated DeltaNet / KDAlinear · ~3 of every 4 layersGated grouped-query attention — AttentionGated grouped-query attentionAttentionSparse / compressed attn — DSA indexer · CSA / HCASparse / compressed attnDSA indexer · CSA / HCASliding-window attention — local · 128–1,024-token windowSliding-window attentionlocal · 128–1,024-token windowOutput gate + sink — σ(x·W_g) ⊙ o · learnable sinkOutput gate + sinkσ(x·W_g) ⊙ o · learnable sink⊕ residual⊕residualRMSNorm — pre-normRMSNormpre-normSoftmax top-k router — Mixture of expertsSoftmax top-k routerMixture of expertsRouted experts — top-6–22 of 256–896 · SwiGLURouted expertstop-6–22 of 256–896 · SwiGLUShared expert — 1–2 · always activeShared expert1–2 · always active⊕ residual⊕residualFinal RMSNormFinal RMSNormLM head — untied · vocab logitsLM headuntied · vocab logitsMTP module(s) — predict t+2 … t+kMTP module(s)predict t+2 … t+kNext-token logitsNext-token logitsDraft tokensDraft tokensMuon optimizer — Newton–Schulz · QK-Clip · per-headMuon optimizerNewton–Schulz · QK-Clip · per-headFP8 → FP4 training — block-scaled FP8 · MXFP4/NVFP4 QATFP8 → FP4 trainingblock-scaled FP8 · MXFP4/NVFP4 QATLong-context extension — staged 4K → 1M · YaRNLong-context extensionstaged 4K → 1M · YaRNRL with verifiable rewards — GRPO family · async agentic RLRL with verifiable rewardsGRPO family · async agentic RLOn-policy distillation — reverse KL to specialist teachersOn-policy distillationreverse KL to specialist teachersSpeculative decoding — MTP self-drafting · DSparkSpeculative decodingMTP self-drafting · DSparkKV-cache compression — FP4 KV · cross-layer reuse · SSDKV-cache compressionFP4 KV · cross-layer reuse · SSD
AttentionMixture of expertsResidual & normInput & outputTraining & inferenceData flowResidual / skipAddedReplacedNot usedNot disclosed

--- frontier-llm@2026-11+++ deepseek-v4-1-flash@2026-09+0~3−1

DeepSeek-V4.1-Flash

DeepSeek, September 2026, 552B + 196B Engram total, 16B (8B in prefill) active

Text · code · tool outputText · code · tool outputImages · video · audioImages · video · audioByte-level BPE tokenizer — 128K–262K vocabByte-level BPE tokenizer128K–262K vocabVision / audio encoder — native ViT or encoder-free patchesVision / audio encodernative ViT or encoder-free patchesToken embedding — media embeds spliced in placeToken embeddingmedia embeds spliced in placeEngram n-gram memory — hashed 2–4-gram lookup (V4.1)Engram n-gram memoryhashed 2–4-gram lookup (V4.1)Residual streams — mHC: n = 4 mixed streamsResidual streamsmHC: n = 4 mixed streamsRMSNorm — pre-normRMSNormpre-normQK-Norm — per-head RMSNorm on q, kQK-Normper-head RMSNorm on q, kPartial RoPE / NoPE — rotate 64 dims, or nonePartial RoPE / NoPErotate 64 dims, or noneGated DeltaNet / KDA — linear · ~3 of every 4 layersGated DeltaNet / KDAlinear · ~3 of every 4 layersCED decoder attention (CSA2 over encoder-projected KV) — AttentionCED decoder attention (CSA2…AttentionCSA2 encoder layers (2× compression, Full + Reuse) — AttentionCSA2 encoder layers (2× comp…AttentionSliding-window attention — local · 128–1,024-token windowSliding-window attentionlocal · 128–1,024-token windowOutput gate + sink — σ(x·W_g) ⊙ o · learnable sinkOutput gate + sinkσ(x·W_g) ⊙ o · learnable sink⊕ residual⊕residualRMSNorm — pre-normRMSNormpre-normMoE router — σ / √softplus · bias balancingMoE routerσ / √softplus · bias balancingRouted experts — top-6–22 of 256–896 · SwiGLURouted expertstop-6–22 of 256–896 · SwiGLUShared expert — 1–2 · always activeShared expert1–2 · always active⊕ residual⊕residualFinal RMSNormFinal RMSNormLM head — untied · vocab logitsLM headuntied · vocab logitsDSpark drafter (trained after pretraining) — Input & outputDSpark drafter (trained after pretrain…Input & outputNext-token logitsNext-token logitsDraft tokensDraft tokensMuon optimizer — Newton–Schulz · QK-Clip · per-headMuon optimizerNewton–Schulz · QK-Clip · per-headFP8 → FP4 training — block-scaled FP8 · MXFP4/NVFP4 QATFP8 → FP4 trainingblock-scaled FP8 · MXFP4/NVFP4 QATLong-context extension — staged 4K → 1M · YaRNLong-context extensionstaged 4K → 1M · YaRNRL with verifiable rewards — GRPO family · async agentic RLRL with verifiable rewardsGRPO family · async agentic RLOn-policy distillation — reverse KL to specialist teachersOn-policy distillationreverse KL to specialist teachersSpeculative decoding — MTP self-drafting · DSparkSpeculative decodingMTP self-drafting · DSparkKV-cache compression — FP4 KV · cross-layer reuse · SSDKV-cache compressionFP4 KV · cross-layer reuse · SSD
AttentionMixture of expertsResidual & normInput & outputTraining & inferenceData flowResidual / skipAddedReplacedNot used

Component by component

Qwen3.8-2.4T-A95B and DeepSeek-V4.1-Flash differ on 11 of 27 components.

Blueprint componentQwen3.8-2.4T-A95BDeepSeek-V4.1-Flash

Not used

Same as the blueprintDeepSeek-ViT, trained from scratch: 32 layers, width 1,024, 16 heads, 14-px patches embedded by a linear projection, 2D-RoPE, RMSNorm and SwiGLU. A 3×3 pixel-unshuffle cuts visual tokens 9× before a 2-layer MLP projector maps them to 5,120 dims. Images only, up to about 1344×1344 px.

Not used

Same as the blueprintTwo Engram modules (196B parameters, FP8) at layers 1 and 14: n-gram orders 2–4, 8 hash heads per order, 2,048 dims per order, about 16M rows per head table. A sigmoid gate on each mHC stream's normalized dot product with its own key adds a shared value. The short causal conv was dropped. Rows are prefetched from host memory over RDMA.

Single residual streamOne d = 8,192 pre-norm residual stream with zero-centred RMSNorm gains; no hyper-connections or depth attention.

Same as the blueprintSingle-Pass mHC: n = 4 streams of width 5,120 and 20 Sinkhorn iterations as in V4, but each sublayer reads its input with the mixing coefficients predicted by the previous sublayer. Removing that dependency lets the Mega-mHC kernel read and write the residual once, halving activation traffic, with negligible quality loss reported.

Same as the blueprintGated DeltaNet in 69 of 92 layers, three before every attention layer. 16 query/key heads and 128 value heads of dim 128, so each q/k head serves 8 value heads (4 in Qwen3.5). A depthwise causal conv (kernel 4) + SiLU runs over q, k and v; each value head has a scalar decay g = −exp(A_log)·softplus(a + dt_bias) and write strength β = σ(b). Each layer keeps a 128 × 128 × 128 state per sequence, which the config holds in FP32.

Not used

Gated grouped-query attentionEvery fourth layer: full causal GQA with 64 query heads sharing 4 KV heads of dim 256, QK-Norm and a sigmoid output gate.

CED decoder attention (CSA2 over encoder-projected KV)Layers 20–39 take uncompressed global KV from the encoder's last hidden state and re-select with a hierarchical indexer.

Not used

CSA2 encoder layers (2× compression, Full + Reuse)Layers 2–19: KV pooled 2→1 and computed in only 3 of 18 layers; the rest reuse that KV and its top-512 selection.

Not used

Same as the blueprintLayers 0–1 are pure sliding-window layers; every other layer adds the 128 most recent uncompressed tokens (SWA KV kept in FP8) to its sparse selection. SWA KV is never persisted: on a prefix-cache hit it is rebuilt by replaying only the last 128 tokens (SWA Bounded Replay), with negligible measured loss.

Softmax top-k routerA linear router scores 512 experts; softmax, keep the top 10, renormalize. No selection bias.

Same as the blueprint√softplus scores over 384 experts, top-6, with auxiliary-loss-free bias balancing kept separately for text and image tokens (each bias set updated from its own modality's load, speed 0.001) plus a 0.0001 sequence-level loss. Selected scores are normalized and scaled by 1.5. V4's hash-routed early layers are gone.

Same as the blueprintOne MTP layer (~26.4B parameters): a full gated-attention + 512-expert MoE block. Its input is a linear projection (16,384 → 8,192) of the RMSNorm'd next-token embedding concatenated with the RMSNorm'd trunk state. It reuses the main embedding and LM head (mtp_use_dedicated_embeddings: false). Per the model card it was trained with multiple steps, and vLLM applies the one layer repeatedly to draft several tokens.

DSpark drafter (trained after pretraining)Three 128-token-window blocks draft 5 tokens per pass from the backbone's last 3 layers; no MTP loss in pretraining.

Not disclosed

Same as the blueprintMuon (momentum 0.95, weight decay 0.1, update RMS 0.18) for backbone linear weights, Engram projections and the vision projector, applied per head to query and key weights. Embeddings, the head and Engram tables use a momentum update with Sinkhorn row/column balancing (Engram learning rate 5×); AdamW covers norms and other non-matrix parameters.

Not disclosed

Same as the blueprintThe final stage is full-vocabulary on-policy distillation over all domains from more than 40 teachers, which may differ architecturally from the student, run with asynchronous generation.

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