Gemma 4 Review: Google's Best Open-Source Model — All Sizes Compared

Google released Gemma 4 on April 2, 2026, and it's the most significant jump the Gemma family has ever made. The 31B flagship model scores 92.4% on MMLU — above GPT-4o's 88.7%. The smallest model, the E2B, runs on a smartphone with a sub-1.5 GB memory footprint and supports text, images, video, and audio. The entire family ships under Apache 2.0, meaning fully commercial use with no restrictions.

This guide covers every variant, what changed from Gemma 3, hardware requirements, benchmark scores, and how to start running Gemma 4 locally today.

The Four Models in the Gemma 4 Family

Gemma 4 ships in four variants, split across two architectural approaches:

The "E" in E2B and E4B stands for Effective — these models use Per-Layer Embeddings (PLE) to keep active computation low even though total parameter counts are higher. The 26B MoE activates only about 4B parameters per token during inference, meaning it runs at roughly the same speed as a 4B dense model while carrying 26B worth of knowledge.

What Changed From Gemma 3

Gemma 4 isn't a small update. The performance improvements on certain benchmarks are dramatic:

The jump in tool use performance (+1,200%) reflects an architectural change: Gemma 4 was designed from the ground up to handle function calling and agentic tasks, not adapted from a text-only base. The reasoning improvement is powered by a built-in extended thinking mode that generates up to 4,000 tokens of internal reasoning before returning an answer — similar to DeepSeek-R1's approach.

Architecture Deep-Dive

Vision and Multimodal

All four Gemma 4 models are natively multimodal — not a text model with a bolted-on vision adapter, but a single architecture trained on text, images, video, and audio simultaneously.

The E2B and E4B models include a 150M parameter vision encoder. The 26B and 31B models use a 550M parameter encoder. All models support:

• Variable resolution and aspect ratio images
• Object detection and spatial reasoning (pointing to locations in images)
• OCR and document/PDF parsing
• Chart and graph comprehension
• Handwriting recognition

The E2B and E4B models add native audio input — speech recognition and understanding built directly into the model, not as a separate pipeline.

Attention Mechanism

Gemma 4 uses alternating local and global attention:

• Local (sliding window): 512 tokens for E2B/E4B, 1,024 for 26B/31B — handles most tokens efficiently
• Global (full context): Interspersed layers that can attend to the full 128K or 256K context when needed
• Shared KV cache: The last N layers share key/value states with the previous layer, reducing memory for long contexts with minimal quality impact

Context Windows

The 256K context on the 26B and 31B models is practically usable — not a theoretical number. Gemma 4 achieves 66.4% on long-context benchmarks (versus Gemma 3's 13.5%), meaning the model actually reads and uses long documents rather than just "supporting" them on paper.

Benchmark Results

Flagship (31B Dense)

The 31B outperforms GPT-4o across the board on knowledge, math, and coding benchmarks. It's the first open-weight model to clearly and consistently beat the GPT-4 tier on a broad benchmark suite.

Edge Models (E2B and E4B)

The small models are equally impressive given their size:

For on-device use cases, the E4B is currently the strongest 4B-class model available anywhere.

Hardware Requirements and Local Setup

Running Gemma 4 with Ollama

Ollama supports all four Gemma 4 variants:

# Tiny model — runs on any modern laptop or phone
ollama run gemma4:e2b

# Best-in-class edge model — 8 GB RAM minimum
ollama run gemma4:e4b

# MoE model — runs like a 4B, thinks like a 26B
ollama run gemma4:26b

# Flagship — needs a serious GPU
ollama run gemma4:31b

Hardware Requirements by Variant

Gemma 4 E2B:

• Minimum: 4 GB RAM (any device)
• Ideal: Any Apple Silicon Mac, modern Android/iOS phone
• Speed: 15–22 tokens/sec on iPhone 16 Pro, 25–40 on M3 MacBook Air

Gemma 4 E4B:

• Minimum: 6 GB RAM / 4 GB VRAM
• Ideal: RTX 4060, M2 MacBook Air, Raspberry Pi 5 with 8 GB
• Speed: 45–80 tokens/sec on RTX 4060

Gemma 4 26B MoE:

• Minimum: 16 GB VRAM (RTX 3080, RX 7900 XTX)
• Ideal: RTX 4090, RTX 5080
• Speed: 40–65 tokens/sec on RTX 4090 (similar to a 4B model due to MoE)

Gemma 4 31B Dense:

• Minimum: 20 GB VRAM (RTX 3090, RTX 4090)
• Ideal: RTX 4090 or dual-GPU setup
• Speed: 45–55 tokens/sec on RTX 4090 at Q4_K_M

Check if your hardware can run Gemma 4 → Hardware Analyzer

Who Should Use Each Variant

Gemma 4 E2B is for mobile-first and always-offline scenarios: voice assistants on phones, on-device document scanning, embedded edge devices with strict memory constraints. Gemma 4 E4B is the best sub-10B model currently available. Use it for laptop-only workflows, Raspberry Pi, NVIDIA Jetson, or any scenario where you need the most capability from the least hardware. Gemma 4 26B MoE hits a remarkable sweet spot: near-frontier reasoning on mid-range consumer hardware (16–20 GB VRAM). The MoE architecture means it runs faster than its parameter count suggests. Ideal for developers who want excellent performance without an RTX 4090. Gemma 4 31B Dense is for users who want the best open-weight model available, period. If you have a 24 GB GPU or Apple Silicon with 32+ GB unified memory, this is the model to run. It competes directly with GPT-4o and wins on most benchmarks.

The Apache 2.0 Advantage

Gemma 4's Apache 2.0 license is a significant practical upgrade over Gemma 3's more restricted terms. Apache 2.0 means:

• Commercial use: Deploy in production products and sell services built on it — no royalties, no fees
• No attribution required in outputs: Your product doesn't need to disclose "Powered by Gemma"
• Modification: Fine-tune, distill, adapt — the resulting model is yours
• No data harvesting: Google has no access to your inputs, outputs, or fine-tuning data when running locally
• No deprecation risk: The model files you download today work forever, regardless of Google's future product decisions

This matters especially for startups and enterprises building AI products. With a proprietary API, you're renting access. With Gemma 4 under Apache 2.0, you own the capability.

Gemma 4 vs. the Competition

For edge/mobile workloads, the Gemma 4 E2B and E4B have no real competition — no other model family offers native audio, vision, and 128K context at sub-2.5 GB memory. For consumer GPU workloads, the 26B MoE's ability to deliver near-31B reasoning at 4B speeds is genuinely novel.

Verdict

Gemma 4 is the best open-source model family Google has ever released, and it's arguably the best open-weight family available as of April 2026. The performance gains from Gemma 3 are not incremental — they're generational. The tool use jump alone (6.6% → 86.4%) transforms Gemma from a chat model into a viable agentic framework component.

The E2B and E4B models redefine what "small model" means. Native multimodal + audio + 128K context at phone-sized memory footprints will accelerate on-device AI significantly. The Apache 2.0 license removes the last business-case objection to building on it.

If you have a 24 GB GPU, download gemma4:31b. If you have an M3/M4 MacBook or mid-range GPU, gemma4:26b. If you're on a laptop or phone, gemma4:e4b. There is no scenario where Gemma 4 is a wrong choice.

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Check hardware requirements for Gemma 4 variants → Hardware Analyzer See Gemma 4 benchmark comparisons → Model Library