Gemma 4 in 2026: Full Review – Sizes, Apache 2.0 License & Comparison with Gemma 3

🤖 What is Gemma 4 and how does it differ from Gemini

Gemma and Gemini are two different Google products. Confusing them is the most common mistake when first encountering them.

Gemini is Google's closed model, available only through a paid API. You cannot download its weights, run it locally, or integrate it into your product without paying for each request.

Gemma 4 is an open model (Google DeepMind) built on the same research foundation as Gemini 3, but with open weights. You download the model to your hardware and run it locally — without the internet, without API keys, without paying per token.

On April 2, 2026, Google released Gemma 4, the fourth generation of this line. Since the launch of the first Gemma, developers have downloaded the models over 400 million times and created more than 100,000 variants based on them.

Gemma 4 is the first model in the line that simultaneously supports:

• images and audio as native input (not through a separate pipeline)
• built-in reasoning mode (step-by-step thinking before answering)
• native function calling for agentic scenarios
• a commercially free Apache 2.0 license

📄 Apache 2.0 License: Why It Matters for Business

Previous versions of Gemma could be used, but with limitations. Gemma 4 is the first one without any restrictions at all.

Gemma 3 was released under Google's own license ("Gemma Open"), which allowed commercial use but had restrictions on certain scenarios and required adherence to Google-specific terms. This created legal uncertainty for businesses.

Gemma 4 is released under Apache 2.0, one of the most permissive licenses in open-source software. The same license is used in Kubernetes, TensorFlow, and Android.

For Ukrainian businesses and developers, this means: you can integrate Gemma 4 into commercial products, SaaS services, and enterprise systems — without legal risks and without paying Google.

📐 Four Sizes of Gemma 4: E2B, E4B, 26B MoE, 31B Dense

Gemma 4 is not a single model, but a family for different hardware. From smartphones to server GPUs.

Google has divided Gemma 4 into two classes: edge models (E-series) for devices with limited memory, and large models for desktops and servers. Choosing the right size is not about "bigger is better," but about matching your hardware and task.

🔵 Gemma 4 E2B — for edge and low-end hardware

What does "E" in E2B and E4B mean? "E" stands for "effective" parameters. The actual model size is larger (5.1B with embeddings), but only 2.3B is activated during operation. This allows the model to run on devices with minimal resources.

E2B is the smallest model in the family. Designed for smartphones, Raspberry Pi, and laptops with 4-6 GB of available memory. It supports images and audio — a unique feature for a model of this size. Context is 128K tokens.

Who it's for: mobile app developers, IoT projects, laptops with weak hardware, scenarios where offline operation on the device is critical.

Where it's not suitable: complex code generation, long structured texts, tasks requiring high response quality. For such cases, E4B is better.

🟢 Gemma 4 E4B — the optimal choice for most

E4B is the default option for Gemma 4. When you simply type ollama run gemma4, it's E4B that gets downloaded. 4.5B effective parameters, ~6 GB in 4-bit quantization, 128K context, support for images and audio.

This model is the main surprise of the family. On the LiveCodeBench v6 benchmark, E4B scores 80% — the same as Gemma 3 27B scored on AIME. This means a small edge model outperforms the previous generation's large model in code. This is a result of the reasoning mode and fundamentally better training.

Who it's for: most developers on Mac M1/M2 with 8-16 GB, Windows laptops with 8+ GB RAM, daily work with code and text, RAG products on low-end hardware.

The only drawback: the reasoning mode is enabled by default and adds 30-75 seconds to each response. For routine quick tasks, this might be inconvenient — in such cases, Qwen3:8b is faster with similar code quality.

🟡 Gemma 4 26B MoE — speed of a large model with lower consumption

What does MoE mean? Mixture of Experts is an architecture where the model consists of 128 specialized "experts," but only a small portion of them is activated for each token. In 26B MoE, ~3.8B parameters are activated during inference — hence the high generation speed with quality significantly higher than a 4B model.

This sounds ideal — but there's an important nuance: all 26B must be loaded into memory, meaning ~18 GB. Less is activated, but everything is stored. This is a fundamental difference from E4B, where both storage and activation are minimal.

In practice, this means: 26B MoE works comfortably on RTX 3090/4090 with 24 GB VRAM or Macs with 24-32 GB unified memory. On Mac M1/M2 with 16 GB, it is not recommended — it will cause swapping and freezing. More details on this can be found in a separate article about the pitfalls of Gemma 4 26B MoE.

Who it's for: developers with RTX 3090/4090, Mac M2/M3 Pro with 24+ GB, scenarios requiring 256K context and high quality with fast inference.

🔴 Gemma 4 31B Dense — maximum quality

31B Dense is the flagship model of the family. "Dense" means that all 30.7B parameters are activated for each token — unlike MoE where only a portion is activated. This provides maximum quality but requires more resources.

On Arena AI (an independent rating based on human comparisons), Gemma 4 31B ranks 3rd among all open models worldwide as of April 2026. AIME 2026 — 89.2%, LiveCodeBench — 80%, GPQA Diamond — 84.3%.

For local execution, ~20 GB of RAM is needed in 4-bit quantization. This means a Mac M2/M3 Max with 32+ GB or an RTX 4090. On smaller devices — only with aggressive swapping, which makes operation uncomfortable.

Who it's for: developers with top-tier hardware, fine-tuning and research tasks, production RAG where quality is critical and a powerful server is available.

How to choose between 26B MoE and 31B Dense?

This is the most frequent question among those who have enough RAM for both. The short answer:

• 26B MoE — if inference speed is important, you have 24 GB VRAM but not 32 GB, or you need 256K context with minimal latency.
• 31B Dense — if maximum quality is important and you have 32+ GB, especially for fine-tuning and complex reasoning tasks.

On benchmarks, the difference between them is small: AIME 88.3% vs 89.2%, MMLU Pro 82.3% vs 85.2%. But in practice, 31B Dense often feels higher quality for complex multi-step tasks — precisely because all parameters are active.

📊 Gemma 4 vs Gemma 3: What Really Changed

This is not an evolution — it's a category change. The numbers speak for themselves.

Below is a comparison on the same benchmark versions. Gemma 3 was tested upon its release in March 2025, Gemma 4 — upon its release in April 2026 (official Gemma 4 model card). We are comparing the closest sized variants: Gemma 3 27B vs Gemma 4 31B.

What's Behind These Numbers

AIME 2026 — the most dramatic leap. AIME (American Invitational Mathematics Examination) is a university-level competitive mathematics exam where most people solve no more than 2-3 problems out of 15. Gemma 3 27B scored 20.8% — that's the level of "sometimes guesses." Gemma 4 31B — 89.2%. The reason: the built-in reasoning mode allows the model to construct a step-by-step plan of over 4000 tokens before answering. Without this, such a result would be impossible.

LiveCodeBench v6 — real code, not school problems. Unlike HumanEval where problems are known and the model could have "memorized" them during training, LiveCodeBench uses fresh problems from real competitions. Gemma 3 27B — 29.1%, Gemma 4 31B — 80%. This means the previous generation solved every third problem, the new one — four out of five.

τ2-bench — most important for product developers. This benchmark tests agentic scenarios: tool invocation, sequential step execution, error handling. Gemma 3 27B — 6.6%, Gemma 4 31B — 86.4%. This means Gemma 3 could hardly perform agentic tasks reliably. Gemma 4 — can. For those building RAG products or automation, this is a fundamental difference.

RULER 128K — the most underestimated result. Gemma 3 nominally supported 128K context tokens. But a score of 13.5% on RULER means the model barely used information from the middle and end of the context — it "forgot" what was at the beginning of a long document. If you fed a large PDF and got incomplete or inaccurate answers — that was the reason. Gemma 4 — 66.4%. Context finally works in reality, not just on paper. For RAG scenarios and working with corporate documents, this is a key change.

Codeforces ELO — an order of magnitude change. An ELO of 110 for Gemma 3 meant a level below the weakest registered participants on the platform — the model couldn't solve even the simplest competitive problems. An ELO of 2150 for Gemma 4 is the level of "Candidate Master," top few hundred players globally. The reason is the same: reasoning mode + native function calling.

What Has Changed in Capabilities

Is It Worth Switching from Gemma 3 to Gemma 4?

The short answer is yes, unless there's a specific reason to stay. Gemma 4 is better in every measured aspect.

Three reasons to stay on Gemma 3:

• You have already fine-tuned Gemma 3 — weights are not transferable, retraining is required
• Your framework or tool does not yet support Gemma 4 — some niche integrations lag behind new releases
• You need stability, not features — Gemma 3 has several months of community bug-fixing behind it, Gemma 4 is still fresh

In all other cases — switch. Especially if you use Gemma for code, agentic tasks, or working with long documents.

⚔️ Gemma 4 vs Llama 4 vs Qwen3: where it wins, where it loses

There are currently three main players in the open model market. Each has its own strengths.

In short: Gemma 4 wins on license, math, and text quality. Qwen3 wins on speed on low-end hardware. Llama 4 has the longest context in the Scout variant. For most local scenarios, Gemma 4 E4B or Qwen3 8B is the best choice depending on your priority.

⚙️ How to download Gemma 4 via Ollama — first start

Ollama is the engine. Gemma 4 is the model. You install Ollama once, then connect any model with a single command.

If Ollama is not yet installed, download it from the official website or install it via Homebrew on Mac. Detailed guide: What is Ollama and why developers are massively switching to local AI.

Important: Gemma 4 requires Ollama 0.20+. Check your version and update if necessary:

ollama --version
brew upgrade ollama          # update on Mac
brew services restart ollama # restart after update

Download and run:

# Recommended option for most (6-9 GB RAM)
ollama run gemma4

# Lightweight option for low-end hardware
ollama run gemma4:e2b

# MoE option — requires ~18 GB
ollama run gemma4:26b

# Maximum quality — requires ~20 GB
ollama run gemma4:31b

After the first run, the model is loaded into memory (⠇ symbol), after a few seconds >>> appears, and you can start typing prompts. The model is also available through any Ollama UI — Open WebUI, Continue.dev, and others.

💾 Which model to choose for your hardware: 8 GB, 16 GB, 32 GB

The most common mistake is downloading a model that doesn't fit into memory. The result: swapping, freezing, disappointment.

Detailed review of models for specific hardware: Ollama on 8 GB RAM: which models work in 2026. Real tests of Gemma 4 on MacBook Pro M1 16 GB: Gemma 4 on M1 16 GB — real tests: code, text, speed.

✅ Conclusion: who should try Gemma 4 right now

Gemma 4 is the best open model for most local scenarios in 2026. But not for everyone — and I know this from my own experience.

I tested Gemma 4 on a MacBook Pro M1 16 GB — alongside Qwen3:8b and Mistral Nemo which I already have locally. Detailed results are in a separate article with real tests: Gemma 4 on M1 16 GB — code, text, speed. Here is my final conclusion.

Gemma 4 truly surprised me with the quality of its text. When I gave the same prompt to three models, Gemma 4 was the only one that added structure and a table that I didn't ask for, but which genuinely improved the answer. For content generation, documentation, and business explanations, it's a cut above the competition.

The situation with code is more complex. The quality of Spring Boot code from Gemma 4 and Qwen3:8b is practically the same — but Qwen3 produced the result in 67 seconds, while Gemma 4 took almost 4 minutes to think. For daily coding, this is a significant difference.

Choose Gemma 4 if:

• You are building a commercial product — Apache 2.0 covers all legal issues
• You work with documents and need context that is actually readable, not just nominal
• You are building a local RAG — native function calling and 128K/256K context
• You generate complex text — articles, documentation, explanations
• You have 8+ GB RAM and response time is not critical

Stick with Qwen3:8b if:

• You write code daily and need speed — Qwen3 is 3-4 times faster with similar code quality
• You use the model as an autocompletion in an IDE — a 4-minute delay is unacceptable there
• You have already fine-tuned Gemma 3 — weights will not transfer, you need to retrain

On my M1 16 GB, I now have both models installed simultaneously — they take up ~15 GB together and do not conflict. I switch: Gemma 4 for text and complex tasks, Qwen3 for fast code. This is my practical conclusion.

Read more on the topic:

• Gemma 4 on M1 16 GB — real tests: code, text, speed
• Gemma 4 26B MoE — pitfalls and when it really wins
• Reasoning mode in Gemma 4 — how to enable, when needed, and its cost in 2026
• What is Ollama and how to get started

Vadym Kharovuyuk — developer, founder of WebsCraft and AskYourDocs.