Google Gemma 4: A Technical Overview

A 31-billion parameter model beating models 20 times its size. That is a verified benchmark result from the Arena AI leaderboard, and it is what makes Gemma 4 one of the most significant open-source AI releases of 2026.

Google DeepMind released Gemma 4 on April 2, 2026. It is not just an update but a full architectural rethink available in four sizes, E2B, E4B, 26B, and 31B, giving developers the flexibility to run powerful AI on a phone, a laptop, or a single GPU.

What Are Gemma Models?

Gemma is Google DeepMind's family of open-weight language models. Unlike Gemini, which runs on Google's infrastructure, Gemma models are downloadable. You own the weights. You run them on your hardware.

The first Gemma launched in early 2024. Since then, developers have downloaded Gemma models over 400 million times. The community has built more than 100,000 fine-tuned variants. Google calls this the "Gemmaverse."

Each generation of Gemma has shared research foundations with Gemini. Gemma 4 is built directly from Gemini 3 research. That matters because Gemini 3 represents Google's best proprietary work. Gemma 4 brings those ideas into the open.

Introducing Gemma 4

Gemma 4 comes in four model sizes, split across two deployment tiers.

The edge tier is for phones, Raspberry Pi, and embedded hardware. It has two models: E2B, with 2.3 billion effective parameters, and E4B, with 4.5 billion. Both support text, image, and audio input. Both work offline. Both run in under 128K tokens of context.

The workstation tier is for developers with GPUs and cloud infrastructure. It has the 26B A4B Mixture-of-Experts (MoE) model and the 31B Dense model. Both support text, image, and video input, with a 256K-token context window. The unquantized 31B model fits on a single 80GB NVIDIA H100 GPU.

A key architectural innovation in the edge models is Per-Layer Embeddings (PLE). Standard transformers give each token one embedding at the input. PLE adds a dedicated conditioning vector at every decoder layer. This gives a 2.3B active parameter model the representational depth of its full 5.1B parameter count. It is why the E2B can run in under 1.5 GB of memory with quantization and still produce serious outputs.

The 26B MoE model uses 128 small experts. Only 8 fire for any given token. This means you get the intelligence of a 26B model at the inference cost of a 4B model. For latency-sensitive applications, this is a massive advantage.

All four models support native function calling, structured JSON output, and a configurable thinking mode that lets you trade speed for deeper reasoning on demand.

Why Gemma 4 Is a Bigger Deal Than the Benchmarks Suggest

Before talking numbers, one thing stands out: the license.

Previous Gemma models used a custom Google license. It had restrictions. Legal teams flagged it. Many enterprises skipped it entirely and chose Mistral or Qwen instead.

Gemma 4 ships under Apache 2.0. The same license used by Qwen and most of the open-weight ecosystem. No monthly active user caps. No acceptable-use policy enforcement. Full commercial freedom. For the first time, enterprise teams can evaluate Gemma without a call to legal first.

That licensing shift, combined with the performance jump, is what makes this release different.

Benchmark Breakdown: The Numbers Tell a Clear Story

  gemma model compariso

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The benchmark table from Google's official model card shows the scale of improvement across all four Gemma 4 models versus Gemma 3 27B.

Arena AI (text): The 31B scores 1452 and the 26B MoE scores 1441. Gemma 3 27B scored 1365. That is not incremental. That is a step change.

AIME 2026 (mathematics): This is the clearest signal. Gemma 3 27B scored 20.8%. Gemma 4 31B scores 89.2%. The 26B MoE reaches 88.3% with only 3.8B active parameters. Even the tiny E4B hits 42.5%, more than double what the previous full-size model could do.

LiveCodeBench v6 (competitive coding): Gemma 3 27B managed 29.1%. Gemma 4 31B scores 80.0%. The 26B MoE reaches 77.1%.

GPQA Diamond (graduate-level science): The 31B scores 84.3%, the MoE 82.3%. Gemma 3 27B sat at 42.4%.

τ2-bench (agentic tool use): Gemma 3 27B scored 6.6%. Gemma 4 31B scores 86.4%. This jump is the most telling for real-world deployment. Agentic workflows, where the model calls tools, executes steps, and handles errors, depend on this capability.

MMLU Pro (multilingual Q&A): The 31B scores 85.2%, which exceeds Qwen 3.5 27B on the same benchmark.

  Model performance vs size

The scatter chart from Google's Arena data shows the full picture. Gemma 4's two larger models sit at the top-left of the performance-vs-size plot. High ELO, low parameter count. The 31B dense model reaches an ELO of 1452. The 26B MoE reaches 1441. Both sit well above Qwen 3.5 27B at 1403, and DeepSeek-V3.2 at around 1425, with a fraction of the total parameter count.

Models like GLM-5 and kimi-k2.5-thinking match or slightly exceed Gemma 4 in raw ELO, but they require hundreds of billions of parameters. Gemma 4 delivers a comparable result at 31B. That is the efficiency story in one chart.

What Gemma 4 Can Process

This is the first Gemma family that handles text, image, audio, and video natively across all sizes.

The edge models (E2B and E4B) accept up to 30 seconds of audio input through a USM-style conformer encoder. This enables speech recognition and audio question answering directly on device, with no cloud call.

The workstation models (26B and 31B) handle video up to 60 seconds at 1 frame per second. They also support images with variable aspect ratios and resolutions, which matters for real documents, charts, and screenshots. The training dataset includes web documents, code, mathematics, and images, with a knowledge cutoff of January 2025 and content spanning over 140 languages.

Testing Gemma 4 on a Real Business Dashboard

The Image Used :

The Prompt Used :

The Output Gemma 4 Gave

Analysis of the Output

This was not a basic image description. Gemma 4 read the visual data, applied business context to it, and produced structured output that a junior analyst could put directly into a report.

What stood out was the accuracy. It did not guess values or blur them. It did not confuse chart types. It kept the scorecard clean and the recommendations specific rather than generic.

The one area where you would want human review is the exact numerical readings from the slider charts, those are visually ambiguous even for a human. But on every structured element, trends, categories, gauges, recommendations, the output was reliable and ready to use.

For a model accessible for free in Google AI Studio, this level of multimodal reasoning is a meaningful result.

Who Should Use Gemma 4 and How to Get Started

The E2B and E4B models are for on-device developers. Android developers can prototype agentic flows today using the AICore Developer Preview. The ML Kit GenAI Prompt API supports production deployment on Android. These models run completely offline.

The 26B MoE is the best balance of intelligence and inference cost for GPU-constrained environments. If you have limited VRAM, this is where to start. It runs quantized on consumer GPUs.

The 31B Dense model is the flagship. It is the right choice for fine-tuning and for workloads that need maximum reasoning quality. It fits on a single H100.

All four models are available right now on Hugging Face, Kaggle, and Ollama. Google AI Studio lets you test the 31B and 26B MoE in a browser with no setup. Framework support at launch covers Hugging Face Transformers, vLLM, llama.cpp, MLX, NVIDIA NIM, SGLang, and more.

Conclusion

Open-weight AI in 2026 is a crowded space. Llama 4, Qwen 3.5, DeepSeek V3, Kimi K2.5, all strong. Gemma 4 earns its place in that list, and in some ways goes further.

The licensing is clean. The benchmark gains over Gemma 3 are not marginal. The edge models bring genuine multimodal intelligence to hardware that most people already own. And the 31B dense model competes with models many times its size on the tasks that matter most: math, code, reasoning, and agentic tool use.

For developers who want a capable, fully open, commercially deployable model they can run on their own infrastructure, Gemma 4 is now the clearest answer Google has ever given.

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