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Inkling vs MiniMax-M2.7

How Inkling (Thinking Machines Lab, July 2026) and MiniMax-M2.7 (MiniMax, March 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+++ inkling@2026-07+0~5−3

Inkling

Thinking Machines Lab, July 2026, 975B total, 41B 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 pre-norm residual stream — Residual & normSingle pre-norm residual streamResidual & normRMSNorm — pre-normRMSNormpre-normQK-Norm — per-head RMSNorm on q, kQK-Normper-head RMSNorm on q, kLearned relative-position bias (no RoPE) — AttentionLearned relative-position bias (no RoP…AttentionGated DeltaNet / KDA — linear · ~3 of every 4 layersGated DeltaNet / KDAlinear · ~3 of every 4 layersGlobal GQA with log-length scaling — AttentionGlobal GQA with log-length s…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 windowShort causal convolutions (SConv) — AttentionShort causal convolutions (SConv)Attention⊕ residual⊕residualRMSNorm — pre-normRMSNormpre-normSigmoid router with shared-expert sink — Mixture of expertsSigmoid router with shared-expert sinkMixture 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+++ minimax-m2@2026-03+0~4−7

MiniMax-M2.7

MiniMax, March 2026, 229.9B total, 9.8B 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-normLayer-wide QK RMSNorm — Residual & normLayer-wide QK RMSNormResidual & 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 layersFull grouped-query attention in all 62 layers — AttentionFull grouped-query attention…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 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 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 block-scaled checkpoint — Training & inferenceFP8 block-scaled checkpointTraining & inferenceLong-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

Inkling and MiniMax-M2.7 differ on 11 of 27 components.

Blueprint componentInklingMiniMax-M2.7

Same as the blueprintEncoder-free. Images and video are cut into 40 × 40-pixel patches, 2 frames deep. A 4-layer hierarchical MLP (three linear + RMSNorm + GELU stages that fold space and time into channels, then a linear projection) maps each patch to one 6,144-dim token. 16 kHz audio becomes dMel frames: 80 mel bins, each quantized to 16 levels, embedded by lookup and summed per frame.

Not used

Single pre-norm residual streamOne d = 6,144 residual stream through 66 pre-norm blocks; no hyper-connections, branch outputs pass a short conv.

Single residual streamOne d = 3,072 residual stream with pre-norm sublayers; no hyper-connections.

Same as the blueprintPer-head RMSNorm on q and k (head dim 128) in all 66 layers, applied to keys after their short convolution. Because q and k are normalized, the softmax scale is 1/d_h = 1/128 instead of 1/√d_h.

Layer-wide QK RMSNormOne RMSNorm over all query heads (6,144 dims) and one over all key heads (1,024 dims), applied before RoPE.

Learned relative-position bias (no RoPE)Each head adds a bias by backward distance: a query-dependent mix of 16 learned profiles, over 512 (local) or 1,024 (global) tokens.

Same as the blueprintPartial RoPE in all 62 layers: the first 64 of each head's 128 dims are rotated (rotate-half layout), the other 64 carry no position, with base θ = 5,000,000. No YaRN or other RoPE scaling is configured.

Global GQA with log-length scaling11 full-attention layers (every 6th, incl. the last): 64 Q / 8 KV heads, d_h = 128; logits scaled up logarithmically past 128K.

Full grouped-query attention in all 62 layersCausal softmax over the whole context in every layer: 48 query heads share 8 KV heads of dim 128; no window, sink or gate.

Same as the blueprint55 of 66 layers (all except 5, 11, …, 65) attend to the last 512 tokens with 64 query heads and 16 KV heads of dimension 128. Keys and values pass through a kernel-4 causal convolution before caching, and the relative-position bias spans the whole window.

Not used

Short causal convolutions (SConv)Depthwise causal convs (kernel 4, with skip) on K, V and on both sublayer outputs before they rejoin the residual.

Not used

Sigmoid router with shared-expert sinkScores 256 routed + 2 shared experts; bias-steered top-6; weights normalized jointly over the 8 active experts, then ×8.

Same as the blueprintSigmoid scores computed in FP32 over 256 experts; a per-expert bias (e_score_correction_bias) is added only to choose the top 8, and the 8 unbiased scores are renormalized to sum to 1. The report calls the biases learnable and says they let the auxiliary balance loss be greatly reduced, not removed (the M2 config lists router_aux_loss_coef = 0.001).

Same as the blueprintTwo shared SwiGLU experts of intermediate size 3,072 run on every token. Unlike the blueprint's ungated shared expert, each is weighted by its own router score, normalized jointly with the 6 routed scores. They stay in BF16 in the NVFP4 checkpoint.

Not used

Same as the blueprintMuon for large matrix weights and Adam for all other parameters, with weight decay coupled to the square of the learning rate. Hyperparameter schedules draw on Thinking Machines' modular-manifolds research. Newton–Schulz settings are not published.

Not disclosed

Same as the blueprintTraining precision is not published. Weights ship in BF16 and as an NVFP4 checkpoint (W4A4, 16-element blocks with E4M3 scales) that quantizes only the routed experts of MoE layers 3–65. Attention, shared experts, routers, dense MLPs, the first MoE layer's experts, embeddings, head, norms and convolutions stay BF16, and the KV cache is not quantized. Whether NVFP4 was trained in (QAT) is not stated.

FP8 block-scaled checkpointOpen weights ship as FP8 E4M3 with 128 × 128 block scales; router, its bias, embeddings and LM head stay in higher precision.

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