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Gemma 4 31B vs gpt-oss-120b

How Gemma 4 31B (Google DeepMind, April 2026) and gpt-oss-120b (OpenAI, August 2025) are built, drawn on the same November 2026 blueprint. They make different choices on 17 of 29 components. Select a component in either diagram to find it in the table.

--- frontier-llm@2026-11+++ gemma-4@2026-04+2~7−6

Gemma 4 31B

Google DeepMind, April 2026, 30.7B

Text · code · tool outputText · code · tool outputImages · video · audioImages · video · audioSentencePiece BPE with byte fallback — Input & outputSentencePiece BPE with byte fallbackInput & outputVision / 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 with per-layer scalars — Residual & normSingle residual stream with per-layer…Residual & normRMSNorm — pre-normRMSNormpre-normQK-Norm — per-head RMSNorm on q, kQK-Normper-head RMSNorm on q, kRoPE by layer type: full RoPE local, p-RoPE global — AttentionRoPE by layer type: full RoPE local, p…AttentionGated DeltaNet / KDA — linear · ~3 of every 4 layersGated DeltaNet / KDAlinear · ~3 of every 4 layersGlobal GQA with keys reused as values — AttentionGlobal GQA with keys reused…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 sinkPost-attention RMSNorm (sandwich norm) — Residual & normPost-attention RMSNorm (sandwich norm)Residual & norm⊕ residual⊕residualRMSNorm — pre-normRMSNormpre-normDense GeGLU feed-forward — Mixture of expertsDense GeGLU feed-forwardMixture of expertsPost-feed-forward RMSNorm (sandwich norm) — Residual & normPost-feed-forward RMSNorm (sandwich no…Residual & normRouted 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 RMSNormTied LM head with logit soft-capping — Input & outputTied LM head with logit soft-cappingInput & outputAutoregressive MTP drafter (4 layers, reads trunk KV) — Input & outputAutoregressive MTP drafter (4 layers,…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 usedNot disclosed

--- frontier-llm@2026-11+++ gpt-oss-120b@2025-08+0~4−9

gpt-oss-120b

OpenAI, August 2025, 117B total, 5.1B 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, kFull RoPE with YaRN scaling — AttentionFull RoPE with YaRN scalingAttentionGated DeltaNet / KDA — linear · ~3 of every 4 layersGated DeltaNet / KDAlinear · ~3 of every 4 layersDense grouped-query attention — AttentionDense 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

Gemma 4 31B and gpt-oss-120b differ on 17 of 29 components.

Blueprint componentGemma 4 31Bgpt-oss-120b

SentencePiece BPE with byte fallbackGemini's 262,144-entry SentencePiece vocabulary: character-level BPE, split digits, bytes only as a fallback.

Same as the blueprinto200k_harmony: a byte-level BPE with 201,088 entries, extending o200k with the tokens of OpenAI's harmony chat and tool-call format.

Same as the blueprintA ~550M-parameter ViT (27 layers, width 1,152, MLP 4,304, 16 heads, 16-px patches) with 2D RoPE and 2D absolute position embeddings, kept frozen during pretraining. Its outputs are pooled 3 × 3, so each soft token covers 48 × 48 px, under a per-image budget of 70, 140, 280 (default), 560 or 1,120 tokens. Video enters as frames; the 31B has no audio encoder.

Not used

Single residual stream with per-layer scalarsOne d = 5,376 stream, RMSNorm before and after each sublayer, and a stored scalar that rescales the stream after every block.

Single residual streamOne d = 2,880 residual stream with pre-norm sublayers; no hyper-connections.

Same as the blueprintPer-head RMSNorm with a learned gain on queries and keys (256-dim heads in local layers, 512-dim in global ones), so the logits use scale 1 instead of 1/√d. Values get a gain-free RMSNorm. There is no attention-logit soft-cap.

Not used

RoPE by layer type: full RoPE local, p-RoPE globalLocal layers rotate all 256 dims (θ = 10⁴); global layers keep only the 64 fastest of 256 frequency pairs (θ = 10⁶).

Full RoPE with YaRN scalingRotary embeddings on all 64 head dims, θ = 150,000, stretched 32× with YaRN.

Global GQA with keys reused as valuesEvery sixth layer: full causal attention, 32 query heads and 4 KV heads of dim 512, V taken from the key projection.

Dense grouped-query attentionFull causal attention with 64 query heads sharing 8 KV heads, in every other block.

Not used

Same as the blueprintNo output gate. Each head instead has a learned sink logit added to the softmax denominator, so it can assign attention mass to nothing.

Post-attention RMSNorm (sandwich norm)The attention output is RMS-normalized with a learned gain before the residual add; there is no output gate or sink.

Not present

Dense GeGLU feed-forwardOne gated MLP per layer, 5,376 → 21,504 → 5,376 with tanh-approximated GELU; all 30.7B parameters are active.

Softmax top-k routerA linear router picks the top 4 of 128 experts; softmax over those 4 logits gives the weights.

Post-feed-forward RMSNorm (sandwich norm)The MLP output is RMS-normalized with a learned gain before it is added to the residual stream.

Not present

Not used

Same as the blueprint128 experts, top-4 active, each a SwiGLU with intermediate size 2,880. The gate is clamped at 7.0 and the linear branch to ±7.0, combined as (x_lin + 1) · x_glu · σ(1.702 · x_glu). Expert weights ship in MXFP4.

Tied LM head with logit soft-cappingLogits come from the input embedding matrix (no separate head) and are squashed by 30·tanh(z/30).

Same as the blueprintUntied output projection to 201,088 logits, kept in BF16.

Autoregressive MTP drafter (4 layers, reads trunk KV)A separate ~500M-parameter, 4-layer Transformer drafts tokens one at a time while attending to the 31B's own KV cache.

Not used

Not disclosed

Same as the blueprintPretrained at 4,096 tokens and extended to 131,072 with YaRN (factor 32).

Not disclosed

Same as the blueprintPost-trained with chain-of-thought reinforcement learning described as similar to o3, with selectable low, medium and high reasoning effort and native tool use through the harmony format.

Same as the blueprintThe 31B pairs with its released drafter (gemma-4-31B-it-assistant): the drafter proposes several tokens and the 31B verifies them in one forward pass. The drafter's model card claims up to 3× faster decoding at the same output quality. Google's example starts at 4 draft tokens and adapts the count to the acceptance rate.

Not used

Same as the blueprintEvery 31B layer owns its KV, but most of it is bounded: the 50 local layers keep 1,024 tokens, and the 10 global layers cache 4 heads × 512 dims with keys reused as values. The report lists +1.10 GB for an int8 KV cache at 32K tokens on the 31B. Cross-layer KV sharing is used only in E2B and E4B.

Not used

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