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models / compare / kimi-k3-vs-qwen3-5

Kimi K3 vs Qwen3.5-397B-A17B

How Kimi K3 (Moonshot AI, July 2026) and Qwen3.5-397B-A17B (Alibaba Qwen, February 2026) are built, drawn on the same November 2026 blueprint. They make different choices on 8 of 27 components. Select a component in either diagram to find it in the table.

--- frontier-llm@2026-11+++ kimi-k3@2026-07+0~5−3

Kimi K3

Moonshot AI, July 2026, 2.8T total, 104B 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)Block Attention Residuals (AttnRes) — Residual & normBlock Attention Residuals (AttnRes)Residual & normRMSNorm — pre-normRMSNormpre-normQK-Clip and normalized latents (no per-head QK-Norm) — Residual & normQK-Clip and normalized latents (no per…Residual & normPartial 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 layersLatent attention (MLA) — global · GQA in many modelsLatent attention (MLA)global · GQA in many modelsSparse / 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-normSigmoid router with Quantile Balancing — Mixture of expertsSigmoid router with Quantile BalancingMixture of expertsStable LatentMoE routed experts (SiTU-GLU) — Mixture of expertsStable LatentMoE routed experts (SiTU-…Mixture of expertsShared expert — 1–2 · always activeShared expert1–2 · always active⊕ residual⊕residualFinal RMSNormFinal RMSNormLM head — untied · vocab logitsLM headuntied · vocab logitsMTP layer turned EAGLE-3 drafter (not in open weights) — Input & outputMTP layer turned EAGLE-3 drafter (not…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

--- frontier-llm@2026-11+++ qwen3-5@2026-02+0~3−3

Qwen3.5-397B-A17B

Alibaba Qwen, February 2026, 397B total, 17B 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

Component by component

Kimi K3 and Qwen3.5-397B-A17B differ on 8 of 27 components.

Blueprint componentKimi K3Qwen3.5-397B-A17B

Block Attention Residuals (AttnRes)Each sublayer reads a softmax-weighted mix of the embedding and earlier 12-layer block summaries, not one running sum.

Single residual streamOne d = 4,096 pre-norm residual stream with zero-centred RMSNorm gains; no hyper-connections or depth attention.

QK-Clip and normalized latents (no per-head QK-Norm)No per-head RMSNorm on q and k: MLA logits are held by K2's weight clipping, KDA's q and k are L2-normalized.

Same as the blueprintPer-head RMSNorm (zero-centred gain) over the 256-dim heads on q and k in the 15 gated-attention layers, applied before RoPE. The Gated DeltaNet layers instead L2-normalize q and k inside the kernel.

Same as the blueprintGated MLA in 24 layers: every fourth layer (4, 8, …, 92) plus layer 93. 96 heads; queries via a 1,536-dim latent, keys and values from a 512-dim latent plus a 64-dim shared key slot (576 cached values per token per layer); q/k head dim 192, v head dim 128. The attention output is kept in FP32 during training to avoid flash attention's biased rounding.

Gated grouped-query attentionEvery fourth layer: full causal GQA with 32 query heads sharing 2 KV heads of dim 256, QK-Norm and a sigmoid output gate.

Sigmoid router with Quantile BalancingSigmoid scores pick 16 of 896 experts; each expert's selection bias is reset every step from a quantile of routing margins.

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

Stable LatentMoE routed experts (SiTU-GLU)896 SiTU-GLU experts run in a 3,584-dim latent (half of d); 16 fire per token; an RMSNorm precedes the up-projection.

Same as the blueprint512 SwiGLU experts with intermediate size 1,024 at the full model width 4,096 (no latent projection); 10 are active per token, about 2% of the pool. Weights ship in BF16, with an official FP8 variant.

MTP layer turned EAGLE-3 drafter (not in open weights)One pretrained MTP layer is fine-tuned into an EAGLE-3-style draft model that reads features from three depths.

Same as the blueprintOne MTP layer: a full gated-attention + 512-expert MoE block whose input is a linear projection (8,192 → 4,096) of the RMSNorm'd next-token embedding concatenated with the RMSNorm'd trunk state. It reuses the main embedding and LM head and, per the model card, was trained with multiple steps, so the one layer is applied repeatedly to draft several tokens.

Same as the blueprintPer-Head Muon: momentum of the Q, K and V projections is split along the head dimension and each head's block is orthogonalized separately, equalizing update scale across heads; other matrices use standard Muon. Pretraining also keeps Kimi K2's weight-clipping mechanism, a cosine LR schedule with 1% linear warmup and weight decay 0.1.

Not disclosed

Same as the blueprintMulti-teacher on-policy distillation merges the nine domain × effort experts: for a sampled domain and effort level, the matching teacher scores each student token with the clipped log-ratio clip(sg(log π_teacher − log π_θ), −R_max, R_max), used as a dense reward inside the RL framework. Top-k distillation objectives showed no advantage.

Not disclosed

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