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models / kimi-k3

Moonshot AI

Kimi K3

93 layers of 3:1 KDA / gated MLA with no positional encoding, Block Attention Residuals and a 16-of-896 latent MoE in MXFP4.

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Parameters
2.8T total, 104B active
Context
1M tokens (1,048,576)
Depth
93 layers
Weights
Open, Kimi K3 License
Released
July 2026

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

The November 2026 LLM blueprint, marked with Kimi K3's differences: 19 components the same, 5 done differently, 3 not used. Select any component.

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

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