On-device applications
Evaluate E2B or E4B when latency, privacy, offline use, or mobile deployment matters more than maximum capability.
Independent tool overview
Gemma 4 at a glance
Gemma 4 is Google's five-model family of Apache 2.0-licensed open-weight models for reasoning, coding, agents, and multimodal understanding across devices from high-end phones to servers.
Visit the official Gemma 4 site ↗Gemma 4
- Developer
- Google DeepMind
- License
- Apache 2.0
- Model sizes
- E2B, E4B, 12B, 26B A4B, and 31B
- Context
- 128K on E2B/E4B; 256K on larger models
- Inputs
- Text and image on all; audio on E2B, E4B, and 12B
- Output
- Text
- Languages
- Pre-trained on more than 140 languages
Overview
What Gemma 4 is
Gemma 4 includes E2B, E4B, 12B Unified, 26B A4B, and 31B models in pre-trained and instruction-tuned variants. The smaller E2B and E4B models target mobile and laptop use; 12B targets laptops, desktops, and small servers; 26B A4B uses a mixture-of-experts design that activates about 4B parameters per token; and 31B targets larger servers or clusters.
All five models accept text and image input and produce text. E2B, E4B, and 12B also accept audio, while video is handled as image frames. The family adds configurable thinking, native function calling and system prompts, long context, multilingual support, and dedicated draft models for speculative decoding. Those capabilities make Gemma 4 flexible, but deployment still requires model selection, hardware planning, prompt-template compliance, evaluation, safety controls, and application code around the weights.
Use cases
Who Gemma 4 is best for
The strongest fit depends on the job you need the product to complete, not the size of its feature list.
Laptop and desktop assistants
Use E4B or 12B for local multimodal assistants, document tools, and developer workflows on capable consumer hardware.
Efficient server inference
Start with 26B A4B when the mixture-of-experts architecture offers the right quality, speed, and memory tradeoff.
Agentic applications
Use native function-call generation for tools and workflows, with application-side schema validation, permissions, and execution.
Multimodal extraction
Process documents, charts, screenshots, OCR, handwriting, images, short audio, and video frames into text.
Customization
Fine-tune or adapt downloadable weights when a domain-specific deployment justifies the data and evaluation work.
Capabilities
Core Gemma 4 features
Five deployment sizes
Covers efficient edge models, a unified 12B model, a sparse 26B A4B model, and a dense 31B model.
Configurable thinking
Supports reasoning modes that trade extra latency and tokens for more deliberate responses.
Long context
Provides 128K-token windows on E2B and E4B and 256K on 12B, 26B A4B, and 31B.
Image understanding
Handles OCR, documents and PDFs, charts, handwriting, user interfaces, object detection, and pointing tasks.
Audio understanding
E2B, E4B, and 12B can transcribe or translate short speech inputs.
Video understanding
Processes videos as sequences of frames, with quality and compute shaped by sampling and visual-token settings.
Native function calling
Generates structured tool calls for an application to validate and execute.
Coding
Supports code generation, completion, correction, and agent-oriented development tasks.
System prompts
Adds native support for the system role to control assistant behavior more cleanly.
Multilingual support
Google reports pre-training across more than 140 languages and out-of-the-box support for 35 or more.
Variable image budgets
Lets developers choose visual-token budgets from 70 to 1,120 to balance detail and compute.
Speculative decoding
Dedicated multi-token-prediction draft models can accelerate inference without changing the target model's output quality.
Open ecosystem
Official weights are available through Hugging Face and Kaggle with documentation for common inference stacks.
Process
How the Gemma 4 workflow works
Step 1
Define the workload
List modalities, languages, latency, privacy, context, tool use, quality, and cost requirements using representative examples.
Step 2
Pick a model size
Start with the smallest model that may pass the task, or Google's suggested 26B A4B general-purpose starting point for server-class use.
Step 3
Choose precision and runtime
Benchmark full or quantized weights on the actual device, inference library, batch size, and target context length.
Step 4
Implement the template correctly
Use Gemma 4's current chat, thinking, modality-order, and function-calling formatting rather than an older Gemma template.
Step 5
Add application controls
Validate tool calls, restrict permissions, filter untrusted inputs, manage context, and keep generated reasoning out of normal conversation history as documented.
Step 6
Evaluate before release
Measure factuality, task success, latency, memory, safety, language quality, multimodal accuracy, and failure recovery on your own data.
Step 7
Monitor and update
Track model revisions, dependency changes, drift, user-reported failures, hardware cost, and regressions after tuning or quantization.
Cost
Gemma 4 pricing and free plan
Google publishes Gemma 4 weights under the Apache 2.0 license without a per-token model-license fee. Total cost depends on hardware or cloud compute, storage, bandwidth, inference software, engineering, observability, security, fine-tuning, and support. Hosted providers may charge their own rates.
Open weights
No per-token license fee
Download and run the official models under Apache 2.0, while paying the operational cost of the chosen deployment.
- Five model sizes
- Pre-trained and instruction-tuned variants
- Self-hosted compute not included
- Third-party hosted pricing varies
Pricing checked . Check current pricing at the source ↗
Assessment
Gemma 4 strengths and limitations
Where it stands out
- Five sizes cover mobile, laptop, workstation, and server deployment targets.
- Apache 2.0 licensing provides broad use, modification, and redistribution rights subject to the license.
- Reasoning, function calling, coding, and system prompts support modern agent workflows.
- All models handle images, and three sizes also accept audio.
- Long 128K and 256K context windows suit larger documents and multi-step sessions.
- The 26B A4B model offers a sparse efficiency option between small and dense server models.
- Variable visual-token budgets let teams trade image detail for speed and memory.
- Official Hugging Face, Kaggle, Colab, and framework guidance lowers the integration barrier.
- Downloadable weights support private, offline, and controlled-environment deployments.
What to consider
- Gemma 4 is a model family, not a finished assistant, hosted application, or managed agent system.
- Larger models and long contexts can require substantial accelerator memory, storage, power, and engineering effort.
- Quantization and aggressive optimization can reduce quality and must be evaluated per task.
- The models can produce incorrect, outdated, biased, unsafe, or fabricated information.
- Function calling only generates a proposed call; the application must validate and execute it safely.
- Reasoning modes increase latency and generated tokens and do not guarantee a correct result.
- Audio input is limited to E2B, E4B, and 12B, with Google documenting a maximum audio length of 30 seconds.
- Video is processed as frames rather than native video output, with a documented maximum of 60 seconds at one frame per second.
- All variants produce text output; they do not generate images, audio, or video.
- Multilingual coverage does not imply equal fluency, cultural accuracy, or safety in every supported language.
- Prompt formatting and modality order matter, and older Gemma templates can produce degraded or invalid behavior.
- Generated thinking should not normally be copied into later chat history, except where the function-calling flow requires it.
- Local deployment shifts patching, abuse prevention, access control, logging, data governance, and incident response to the operator.
- Fine-tuning can overfit, regress safety, or reduce general capability without a carefully designed evaluation suite.
Compare
Gemma 4 alternatives
The right alternative depends on the specific output, workflow, controls and budget your project requires.
Consumer
Qwen3.5-Omni
Qwen3.5-Omni is a close alternative when broad text, image, audio, and video understanding across many languages is the priority.
Explore Qwen3.5-Omni →Consumer
Mistral 4 Small
Mistral 4 Small is a larger sparse open model to compare for reasoning, coding, vision, and server deployment.
Explore Mistral 4 Small →Coding
Meta Llama Models
Meta's Llama family offers another widely supported downloadable-model ecosystem with different licensing and deployment tradeoffs.
Explore Meta Llama Models →Questions
Gemma 4 FAQs
What is Gemma 4?
Gemma 4 is Google's family of downloadable models for reasoning, coding, agentic tool use, and multimodal understanding across mobile, laptop, desktop, and server targets.
How many Gemma 4 models are there?
There are five core sizes: E2B, E4B, 12B Unified, 26B A4B, and 31B. The family includes pre-trained and instruction-tuned variants.
Is Gemma 4 open source?
Google describes Gemma as an open-model family and publishes Gemma 4 weights under the Apache 2.0 license. Review that license and the distribution package for your use case.
Can Gemma 4 run locally?
Yes. E2B and E4B target mobile and laptop use, while larger variants need progressively more capable hardware. Actual feasibility depends on precision, runtime, context, batch size, and available memory.
Which Gemma 4 model should I start with?
Use the smallest model that might meet your quality target. Google's getting-started guide suggests 26B A4B as a flexible general starting point when desktop or server resources are available.
Does Gemma 4 support images, audio, and video?
All models accept text and images. E2B, E4B, and 12B also accept audio. Video is analyzed as image frames. All models output text.
What context window does Gemma 4 have?
E2B and E4B support 128K tokens. The 12B, 26B A4B, and 31B models support 256K tokens.
Can Gemma 4 call tools?
It can generate structured function calls, but it cannot execute code by itself. The surrounding application must validate arguments, enforce authorization, run the tool, and return results.
How much does Gemma 4 cost?
There is no per-token license fee for the Apache 2.0 weights. You still pay for hardware or cloud inference, storage, network use, engineering, safety, monitoring, and any third-party service.
Bottom line
Our Gemma 4 verdict
Gemma 4 is a strong open-model family for teams that need control over deployment without giving up modern reasoning, multimodal, long-context, coding, and function-calling capabilities. Its biggest advantage is choice: small edge models, a unified multimodal 12B, an efficient 26B A4B, and a larger dense 31B. The right decision comes from benchmarking the smallest viable variant on the actual hardware and application—not from choosing the largest parameter count.
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