Similar to Gemma-3n, Gemma 4 supports image, text, and audio inputs, and generates text responses. The text decoder is based on the Gemma model with support for long context windows. The image encoder is similar to the one from Gemma 3 but with two crucial improvements: variable aspect ratios, and configurable number of image token inputs to find your sweet spot between speed, memory, and quality. All models support images (or video) and text inputs, while the small variants (E2B and E4B) support audio as well.

Gemma 4 leverages several architecture components used in previous Gemma versions and other open models, and leaves out complex or inconclusive features such as Altup. The combination is a mix designed to be highly compatible across libraries and devices, that can efficiently support long context and agentic use cases, whilst being ideal for quantization.

As shown in the benchmarks above, this feature mix (combined with the training data and recipe) enables the 31B dense model to achieve an estimated LMArena score (text only) of 1452, while the 26B MoE reaches 1441 with just 4B active parameters 🤯. As we'll see, multimodal operation is comparatively as good as text generation, at least in informal and subjective tests.

One of the most distinctive features in smaller Gemma 4 models is Per-Layer Embeddings (PLE), which was introduced previously in Gemma-3n. In a standard transformer, each token gets a single embedding vector at input, and the same initial representation is what the residual stream builds on across all layers, forcing the embedding to frontload everything the model might need. PLE adds a parallel, lower-dimensional conditioning pathway alongside the main residual stream. For each token, it produces a small dedicated vector for every layer by combining two signals: a token-identity component (from an embedding lookup) and a context-aware component (from a learned projection of the main embeddings). Each decoder layer then uses its corresponding vector to modulate the hidden states via a lightweight residual block after attention and feed-forward. This gives each layer its own channel to receive token-specific information only when it becomes relevant, rather than requiring everything to be packed into a single upfront embedding. Because the PLE dimension is much smaller than the main hidden size, this adds meaningful per-layer specialization at modest parameter cost. For multimodal inputs (images, audio, video), PLE is computed before soft tokens are merged into the embedding sequence — since PLE relies on token IDs that are lost once multimodal features replace the placeholders. Multimodal positions use the pad token ID, effectively receiving neutral per-layer signals.