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Gemma 4
AI Modelsโ€บGemma

Gemma 4

Family: Gemma
Open (Apache 2.0) family of multimodal AI models from Google DeepMind (E2B/E4B/26B A4B/31B). Supports text, image, audio, and video. Native function calling.
โœ“ Activeโœ“ Public accessโš– Open sourceโ˜… FeaturedLLMMultimodalTool-using model๐Ÿ“ Gemma
Context window
256K
tokens
Parameters
25.2B
parameters
๐ŸขGoogle DeepMindProducer
Access:APIDownloadHostedDeployment:๐Ÿ’ป Localโ˜ Cloud๐Ÿ“ฑ On-device

Overview

Gemma 4 is a family of open multimodal AI models from Google DeepMind, released under the Apache 2.0 license. The models support text, image, audio (E2B/E4B), and video input, and generate text, code, and structured data.

**Model variants**

Gemma 4 is available in four sizes: E2B and E4B (mobile and edge devices, 128K token context window), 26B A4B (Mixture of Experts, consumer GPU, 256K context window), and 31B (dense, workstation-class GPU, 256K context window).

**Key capabilities**

Built-in reasoning mode (Thinking) โ€” models feature native chain-of-thought

Native function calling (native function-calling support)

Native system prompt support (native system role)

Hybrid attention mechanism: local sliding window + global attention (final layer is always global)

The 26B A4B and E4B models operate at speeds comparable to 4B models due to the MoE architecture

Classification
LLMMultimodalTool-using model
Family: Gemma
Access & deployment
APIDownloadHosted
LocalCloudOn-device
Weights: Open source
Key parameters
๐Ÿ“ Context: 256K
๐Ÿงฉ Parameters: 25.2B
โœ“ Toolsย ยทย โœ“ Fine-tuning
๐Ÿ“ฅ Input: text, image, audio, video
Platforms
Vertex AIHugging Face Hub

Technical specification

Context window
256K
tokens
Parameters
25.2B
parameters
License
Apache 2.0
Hardware requirements
E2B/E4B: mobile and edge devices (phones, tablets, IoT); 26B A4B (MoE): consumer GPU or workstation; 31B: workstation-class GPU. The E2B/E4B variants are designed to run on devices without cloud connectivity.
Features:โœ“ Tool useโœ“ Fine-tuning
Modalities
โฌ‡ Input
textimageaudiovideo
โฌ† Output
textcodestructured_data

Capabilities and applications

Native model capabilities
Coding
Generating, analysing and modifying source code.
Category: coding
Multilingual
Understanding and generating text in many languages.
Category: language
Multi-step reasoning
Carrying out multi-step chains of reasoning across long, complex tasks.
Category: reasoning
Long context
Maintaining coherence and focus across very long input context.
Category: language
Structured output
Producing data in structured formats such as JSON.
Category: structured_generation
Image understanding
Analysing and interpreting the content of images.
Category: vision
Audio understanding
Category: audio
Multimodal understanding
Category: multimodal
Reasoning
The model's ability to reason logically and solve complex problems.
Category: reasoning
Video Understanding
Category: video
Function Calling
Category: planning
Interleaved Multimodal Input
Category: reasoning

Benchmark results

5 benchmarks
MMLU Pro
accuracy ยท instruction-tuned (Gemma 4 31B IT)
85.2%
๐Ÿ“… 31 Mar 2026๐Ÿ“„ Gemma 4 model card | Google AI for Developers
Source: ai.google.dev/gemma/docs/core/model_card_4. Score for Gemma 4 31B (IT). MMLU Pro is harder than standard MMLU.
GPQA
accuracy ยท Instruction-tuned variant (Gemma 4 31B IT).
84.3%
๐Ÿ“… 31 Mar 2026๐Ÿ“„ Gemma 4 model card | Google AI for Developers
Source: ai.google.dev/gemma/docs/core/model_card_4. Result for Gemma 4 31B (IT). Diamond subset of GPQA.
LiveCodeBench v6
accuracy ยท instruction-tuned (Gemma 4 31B IT)
80.0%
๐Ÿ“… 31 Mar 2026๐Ÿ“„ Gemma 4 model card | Google AI for Developers
Source: ai.google.dev/gemma/docs/core/model_card_4. Result for Gemma 4 31B (IT). LiveCodeBench v6 coding benchmark.
AIME 2026 (no tools)
accuracy ยท Instruction-tuned variant (Gemma 4 31B IT), no tools enabled.
89.2%
๐Ÿ“… 31 Mar 2026๐Ÿ“„ Gemma 4 model card | Google AI for Developers
Source: ai.google.dev/gemma/docs/core/model_card_4. Score for Gemma 4 31B (IT). American Invitational Mathematics Examination 2026.
MMMU Pro (Vision)
accuracy ยท Instruction-tuned variant (Gemma 4 31B IT), vision-capable model.
76.9%
๐Ÿ“… 31 Mar 2026๐Ÿ“„ Gemma 4 model card | Google AI for Developers
Source: ai.google.dev/gemma/docs/core/model_card_4. Score for Gemma 4 31B (IT). MMMU Pro is an extended, more challenging version of MMMU.

Pricing

Technical architecture

Core Architecture
MOMoE
Model Form
MLMultimodal LLM
Training Techniques
ITInstruction TuningRLRLHFP/PEFT / LoRA

Deployment and security

๐Ÿ”’ Security / Enterprise
โœ“ Verified enterprise information

Gemma 4 model card includes safety evaluation results. As an open-source model, deployment responsibility lies with the user. Documentation on responsible AI use is available.

Gemma 4 is an open-source model (Apache 2.0). Production deployments require independent risk assessment. Google publishes a model card with safety evaluation results.
Updated: 5 Apr 2026โ†— Security documentation

Sources and related pages