Skip to content
archdiff

models / compare / kimi-k3-1-pro-vs-nemotron-3-ultra

Kimi K3.1 Pro vs Nemotron 3 Ultra

How Kimi K3.1 Pro (Moonshot AI, October 2026) and Nemotron 3 Ultra (NVIDIA, June 2026) are built, drawn on the same November 2026 blueprint. They make different choices on 12 of 27 components. Select a component in either diagram to find it in the table.

--- frontier-llm@2026-11+++ kimi-k3-1-pro@2026-10+0~5−2

Kimi K3.1 Pro

Moonshot AI, October 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; LayerNorm on the indexer key — Residual & normQK-Clip and normalized latents; LayerN…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 logitsNo MTP layer in the open checkpoint — Input & outputNo MTP layer in the open checkpointInput & 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 usedNot disclosed

--- frontier-llm@2026-11+++ nemotron-3-ultra@2026-06+0~4−7

Nemotron 3 Ultra

NVIDIA, June 2026, 550B total, 55B 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, one sublayer per layer — Residual & normSingle residual stream, one sublayer p…Residual & 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 noneMamba-2 selective state-space layers — AttentionMamba-2 selective state-spac…AttentionGlobal GQA attention without positional encoding — AttentionGlobal GQA attention without…AttentionSparse / 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-normMoE router — σ / √softplus · bias balancingMoE routerσ / √softplus · bias balancingLatentMoE routed experts (ReLU², 2,048-dim latent) — Mixture of expertsLatentMoE routed experts (ReLU², 2,048…Mixture of expertsShared 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 used

Component by component

Kimi K3.1 Pro and Nemotron 3 Ultra differ on 12 of 27 components.

Blueprint componentKimi K3.1 ProNemotron 3 Ultra

Same as the blueprintMoonViT-V2, as in K3: a 27-layer, 401M-parameter ViT (14-px patches, width 1,024, 12 heads) with RMSNorm and no bias terms, trained from scratch jointly with the language model. Images and video share weights; a 2×2 pixel shuffle cuts tokens 4× before an MLP projector to 7,168 dims. The model card lists text and image input; Moonshot's API docs also list video.

Not used

Block Attention Residuals (AttnRes)Each sublayer reads a softmax-weighted mix of the embedding and earlier 12-layer block summaries, as in K3.

Single residual stream, one sublayer per layer108 pre-norm layers on one d = 8,192 residual stream; each layer is one mixer or one MoE, not an attention+FFN pair.

QK-Clip and normalized latents; LayerNorm on the indexer keyNo per-head QK-Norm: K2-style weight clipping for MLA, L2-normalized q/k in KDA, a LayerNorm on the DSA indexer key.

Not used

Same as the blueprintKDA in 69 of 93 layers (three of every four, starting at layer 1), unchanged from K3: 96 heads with 128-dim q, k, v and a 128 × 128 state per head; short conv (kernel 4) + Swish on q, k, v; L2-normalized q, k. The log-decay is bounded as g = −5·σ(eᴬ·z), which keeps 16-token-tile rescaling inside BF16 range so every intra-chunk tile runs as a tensor-core matmul.

Mamba-2 selective state-space layers48 of the 60 mixer layers: SSD with 256 heads × 64 dims, state size 128, 8 B/C groups, causal conv of kernel 4.

Same as the blueprintGated MLA in the same 24 layers as K3 (4, 8, …, 92 plus 93), every projection the same shape: 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 difference is the key set: each query now attends only to the 2,048 tokens its layer's DSA indexer selects, not the full prefix.

Global GQA attention without positional encoding12 layers of dense causal attention: 64 query heads share 2 KV heads (d_h = 128); no RoPE, QK-norm, gate or sink.

Same as the blueprintNew in K3.1: a DSA lightning indexer in each of the 24 MLA layers, each with its own weights (vLLM: no index sharing). 32 heads × 128 dims; queries from the 1,536-dim query latent (1,536 → 4,096), one shared 128-dim key from the hidden state with a LayerNorm, per-head weights from a 7,168 → 32 projection. Scores I = Σₕ wₕ·ReLU(qₕ·k) pick the top 2,048 tokens per query. 7.44M BF16 parameters per layer. DeepSeek-V3.2's indexer has 64 heads. How it was trained is unpublished.

Not used

Same as the blueprintBoth mixers keep K3's input-dependent, full-rank, channel-wise sigmoid gate σ(W_g·x), W_g: 7,168 → 12,288 (96 × 128). In gated DSA it multiplies the sparse MLA output; in KDA it multiplies the head-wise RMSNorm of the recurrent output. No sink logits.

Not used

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

Same as the blueprintLinear router at the full 8,192 width: sigmoid scores over 512 experts, a per-expert bias steers top-22 selection, and the selected scores are renormalized and scaled by 5.0. Balancing follows the Nemotron 3 Super recipe: aux-loss-free bias updates (rate 1e-3) plus a standard load-balancing loss (coefficient 1e-4).

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.

LatentMoE routed experts (ReLU², 2,048-dim latent)512 non-gated squared-ReLU experts of width 5,120 run in a 2,048-dim latent space; 22 active per token.

No MTP layer in the open checkpointThe checkpoint ships without MTP weights; Moonshot has not described a K3.1 drafter, so serving engines bring their own.

Same as the blueprintOne MTP block (an attention layer followed by a LatentMoE layer) whose weights are shared across steps and applied recursively. It ran 2 steps in pretraining (total loss weight 0.1, 0.05 per step). MTP Boosting then trains only this head with a forward-KL loss to the frozen backbone's logits over 7 steps, to match the inputs it sees when drafting.

Same as the blueprintInherited from K3's pretraining: Per-Head Muon, which orthogonalizes each head's block of the Q, K and V momentum separately, plus Kimi K2's weight clipping, a cosine LR schedule with 1% linear warmup and weight decay 0.1. Moonshot has not said which optimizer trained the new indexer or K3.1's own training stages.

Not used

Not disclosed

Same as the blueprintMulti-teacher on-policy distillation (MOPD) in two iterations. More than ten domain-specialized teachers (STEM, coding, SWE, search, terminal use, office work, chat, safety and others) give dense token-level reverse-KL signals on the student's own rollouts. The RLVR student acts as a self-teacher for uncovered domains.

More comparisons