Google Gemma 4: A Comprehensive Guide to the Most Capable Open Model Family

On April 2, 2026, Google DeepMind released Gemma 4 — a family of four open-weight models that push the boundaries of what small-to-medium sized language models can achieve. Built on the same research lineage as Gemini 3, Gemma 4 is designed for advanced reasoning, agentic workflows, and multimodal understanding, all under the commercially permissive Apache 2.0 license.

This guide covers everything you need to understand before using Gemma 4: the model variants, architecture innovations, benchmark performance, multimodal capabilities, and where it stands among the current generation of open LLMs.

The Gemma 4 Model Family at a Glance

Gemma 4 ships in four distinct variants, each targeting a different deployment scenario:

Model	Total Params	Active Params	Architecture	Context Window	Target Use Case
Gemma 4 E2B	~2.3B	~2.3B	Dense + PLE	128K	On-device, mobile, edge
Gemma 4 E4B	~4B	~4B	Dense + PLE	128K	On-device, smart assistants
Gemma 4 26B-A4B	26B	~3.8B	Mixture of Experts	256K	Cloud & edge balance
Gemma 4 31B	31B	31B	Dense	256K	Maximum quality, cloud

The naming convention reflects an intentional design philosophy. The “E” prefix (Effective) in E2B and E4B indicates these models use Per-Layer Embeddings (PLE), a novel technique that makes a smaller model behave as if it has more parameters. The 26B-A4B notation tells you the total parameter count (26B) and the active parameters per token (approximately 4B).

Architecture Deep Dive

Gemma 4 introduces several architectural innovations that deserve detailed examination. These aren’t incremental tweaks — they represent meaningful advances in how transformer models handle efficiency, context length, and multimodal inputs.

1. Per-Layer Embeddings (PLE)

PLE is arguably the most interesting innovation in the E2B and E4B models. Traditional transformers use a single embedding table at the input layer. PLE adds a second embedding table that produces a dedicated conditioning vector for every decoder layer.

Here’s how it works:

For each input token, PLE generates a small vector per layer by combining a token-identity component (from a learned embedding lookup) with a context-aware component (from a learned projection).
Each decoder layer receives its corresponding PLE vector and uses it to modulate hidden states via a lightweight residual block after both the attention and feed-forward sublayers.

The result is that each layer gets a richer, token-specific signal than what standard positional embeddings provide. This allows smaller models to punch well above their weight class — the E4B model with PLE can approach the quality of models with significantly more parameters.

2. Hybrid Attention: Sliding Window + Global

Gemma 4 alternates between two types of attention across its layers:

Local sliding-window attention (512–1024 token windows): Fast and memory-efficient, handles nearby token interactions.
Global full-context attention: Captures long-range dependencies across the entire context.

By interleaving these two attention types (and ensuring the final layer is always global), Gemma 4 achieves the speed and low memory footprint of a lightweight model without sacrificing the deep contextual awareness required for complex, long-context tasks.

3. Dual RoPE (Rotary Position Embeddings)

Standard RoPE works well for moderate context lengths but degrades at very long distances. Gemma 4 solves this with a dual configuration:

Standard RoPE for sliding-window (local) layers
Proportional RoPE for global attention layers

This dual approach is what enables reliable 256K token context windows on the 26B and 31B models without the quality degradation that typically plagues long-context inference.

The last N layers of the model reuse key-value tensors from earlier layers rather than computing their own KV projections. Specifically, the last num_kv_shared_layers layers don’t compute their own key and value matrices — they reference the K and V tensors from the last non-shared layer of the same attention type (sliding or full).

This reduces both memory consumption and compute during inference, which is particularly valuable for deployment on resource-constrained devices and for serving at scale.

5. Mixture of Experts (26B-A4B)

The 26B variant uses a fine-grained MoE architecture with:

128 small experts in the feed-forward layers
8 experts activated + 1 shared expert per token
Only ~3.8B parameters firing per forward pass

This design achieves roughly 97% of the dense 31B model’s quality while requiring a fraction of the compute per token. The shared expert ensures that common knowledge patterns are always available, while the routed experts handle specialized knowledge domains.

Multimodal Capabilities

Unlike many open models that are text-only, Gemma 4 is natively multimodal across the entire family:

Vision

All four models support image input with variable aspect ratio and resolution. You can feed in photographs, screenshots, diagrams, or charts, and the model processes them natively through its vision encoder — no separate CLIP-like module bolted on after the fact.

Video

The 26B and 31B models support video understanding, processing sequences of frames to answer questions about temporal events, actions, and scene changes.

Audio

The E2B and E4B models feature native audio processing. This is particularly significant for on-device applications — imagine a smartphone assistant that can listen, see, and reason without sending data to the cloud.

Function Calling

All models include native structured tool-use support, enabling agentic workflows where the model can plan multi-step actions, call external tools, and process the results — all without specialized fine-tuning.

Benchmark Performance: The Numbers

Gemma 4 delivers dramatic improvements over its predecessor. Here’s a detailed look at benchmark results for the 31B dense model:

Gemma 4 31B vs Gemma 3 (Same-Class Comparison)

Benchmark	Gemma 3	Gemma 4 31B	Improvement
AIME 2026 (Math)	20.8%	89.2%	+68.4 pts
LiveCodeBench v6 (Coding)	29.1%	80.0%	+50.9 pts
GPQA Diamond (Science)	42.4%	84.3%	+41.9 pts
MMLU Pro (General)	—	85.2%	—
Codeforces ELO	—	2,150	—

These aren’t marginal gains — the jump from 20.8% to 89.2% on AIME represents a fundamentally different tier of mathematical reasoning capability.

Arena Rankings

Gemma 4 31B: LMArena Elo 1452 — ranked #3 on the text leaderboard
Gemma 4 26B-A4B: LMArena Elo 1441 — ranked #6 with only 4B active parameters
Both models beat out competitors with 20x their parameter count

The fact that the 26B MoE model (with only 3.8B active parameters) achieves Elo 1441 — just 11 points behind the full 31B dense model — validates the efficiency of the MoE architecture.

Where Gemma 4 Stands Among Open LLMs (April 2026)

The open-weight LLM landscape in early 2026 is highly competitive. Here’s how Gemma 4 compares to the other major players:

vs. Qwen 3.5 (Alibaba)

Qwen 3.5 is currently the top-ranked open model family. Key differences:

Math: Qwen 3.5 still leads on harder benchmarks (48.7 vs 42.1 on AIME for the largest variants), though Gemma 4 31B is competitive at its size class.
Coding: Qwen 3.5 edges ahead on SWE-bench and LiveCodeBench for the largest models.
Multilingual: Qwen 3.5 has a 250K vocabulary trained on 201 languages — the broadest multilingual coverage.
License: Both use Apache 2.0 — full commercial freedom with no restrictions.
Size: Qwen 3.5’s flagship is the 397B-A17B MoE model, significantly larger than Gemma 4’s 31B.

Verdict: Qwen 3.5 leads at the top end, but Gemma 4 offers better quality at the sub-31B parameter range, especially for multimodal tasks.

vs. Llama 4 (Meta)

Llama 4 (Scout and Maverick variants) is Meta’s latest offering:

Context Length: Llama 4 Scout has an unmatched 10M token context window — 40x longer than Gemma 4’s 256K.
MMLU: Llama 4 Maverick has the highest MMLU score (85.5%) among open models.
Architecture: Llama 4 Scout uses a 109B MoE with 17B active parameters per token.
License: Llama licenses include a 700M MAU cap and EU restrictions — significantly more restrictive than Gemma 4’s Apache 2.0.

Verdict: Llama 4 wins on raw context length and general knowledge, but Gemma 4’s Apache 2.0 license and stronger on-device variants make it more practical for many production deployments.

vs. DeepSeek V3.2

Reasoning: DeepSeek V3.2 remains very competitive on reasoning and coding tasks.
Architecture: DeepSeek uses its own MoE variant with similar efficiency principles.
License: More restrictive than Apache 2.0.

Verdict: DeepSeek V3.2 is a strong reasoning model, but Gemma 4 offers a more complete package with native multimodal support and a better license.

vs. GLM-5 (Zhipu AI) and Kimi K2.5 (Moonshot AI)

Chinese competitors GLM-5 and Kimi K2.5 both slightly edge out Gemma 4 on certain benchmarks, but Gemma 4 competes closely and has advantages in license terms, multimodal breadth, and ecosystem support.

The Competitive Landscape Summary

Model	Best At	License	Multimodal	On-Device
Gemma 4 31B	Balanced perf + license	Apache 2.0	Text, Image, Video, Audio	Limited
Gemma 4 E4B	Edge/mobile deployment	Apache 2.0	Text, Image, Audio	Excellent
Qwen 3.5 397B	Raw benchmark scores	Apache 2.0	Text, Image	No
Llama 4 Scout	Ultra-long context (10M)	Llama License (restrictive)	Text, Image	No
DeepSeek V3.2	Reasoning, coding	Custom (restrictive)	Text	No

The Apache 2.0 License: Why It Matters

Previous Gemma models used the Gemma Use License, which included acceptable-use restrictions and redistribution limitations. Gemma 4’s switch to Apache 2.0 is arguably as significant as its technical improvements:

No usage restrictions: No MAU limits, no acceptable use policies
No geographic restrictions: Unlike Llama’s EU limitations
Full commercial freedom: Use it in any product, modify it, redistribute it
Patent grant: Apache 2.0 includes an explicit patent license

For enterprises evaluating open models for production deployment, this license change eliminates a major category of legal risk. You can fine-tune, distill, quantize, and deploy Gemma 4 without worrying about license compliance beyond standard Apache 2.0 terms.

Practical Deployment Considerations

Memory Requirements

Model	FP16 VRAM	INT8 VRAM	INT4 VRAM
Gemma 4 E2B	~5 GB	~3 GB	~2 GB
Gemma 4 E4B	~8 GB	~5 GB	~3 GB
Gemma 4 26B-A4B	~12 GB*	~8 GB*	~5 GB*
Gemma 4 31B	~62 GB	~32 GB	~18 GB

*MoE models only load active experts into memory during inference.

Recommended Hardware

E2B/E4B: Modern smartphones, Raspberry Pi 5, edge accelerators
26B-A4B: Single consumer GPU (RTX 4090/5090), Apple M-series Macs
31B: Multi-GPU setup or cloud (A100/H100)

Framework Support

Gemma 4 is supported across the major inference frameworks from day one:

Hugging Face Transformers: Full support including multimodal
vLLM: Optimized serving with PagedAttention
LMStudio: Local deployment with GUI
Ollama: Easy local setup
TensorRT-LLM: NVIDIA-optimized inference
MediaPipe / LiteRT: On-device deployment for Android

When to Choose Which Gemma 4 Variant

Choose E2B when:

You need AI on a smartphone or IoT device
Privacy requirements mandate on-device processing
Audio understanding is needed on-device
Latency must be sub-100ms

Choose E4B when:

You need higher quality on-device inference
Smart assistant or voice-first applications
Mobile apps with vision + audio capabilities
Moderate edge hardware is available

Choose 26B-A4B (MoE) when:

You want near-31B quality at a fraction of the compute
Serving cost matters (cloud deployment at scale)
Single consumer GPU deployment is needed
You need the best quality-per-FLOP ratio

Choose 31B (Dense) when:

Maximum quality is the priority
Complex agentic workflows with long context
Fine-tuning stability matters (dense models are easier to fine-tune than MoE)
Research and experimentation

Conclusion

Gemma 4 represents a significant moment in the open LLM landscape. It’s not the single best model on every benchmark — Qwen 3.5 leads at the top end, Llama 4 Scout has unmatched context length. But Gemma 4 offers the best combination of:

Strong performance across reasoning, coding, and general knowledge
Native multimodal support (text, image, video, audio) across the full model family
On-device viability with E2B and E4B variants
Unrestricted Apache 2.0 license for commercial deployment
Architectural efficiency with PLE, KV cache sharing, and MoE

For developers evaluating open models in April 2026, Gemma 4 deserves serious consideration — not because it’s the biggest or the highest-scoring, but because it’s the most versatile and the most deployable. It’s the kind of model family where you can start prototyping with the 31B on a cloud GPU, optimize with the 26B MoE for production serving, and deploy the E4B to edge devices — all under the same architecture, the same API, and the same license.

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