Gemma 4 Review 2026: I Ran Google's New Open Model Locally — Results
Google DeepMind dropped Gemma 4 on April 2, 2026, and within an hour I had it running locally on my machine. I had seen the benchmark numbers — 89.2% on AIME 2026, Arena AI Elo of 1452, ranked third among all open models in the world — but numbers on a leaderboard and a model actually running on your hardware are two different things. So I tested it. Here is what I found.
What is Gemma 4, in plain English?
Gemma 4 is a family of four free, open-weight AI models released by Google DeepMind. "Open-weight" means Google releases the actual model files — you download them, you own them, you run them on your own hardware. No subscription. No API calls. No data leaving your machine. And unlike some open models that come with restrictive licenses, Gemma 4 ships under Apache 2.0 — the most permissive open-source license available. You can use it commercially, modify it, redistribute it, build products on it. No legal headaches.
The four models are:
Effective 2B (E2B) — Built for phones, IoT devices, and Raspberry Pi. Runs on 4GB of RAM. Handles text, images, and audio. 128,000-token context window. This is the one that fits in your pocket.
Effective 4B (E4B) — Slightly larger, runs on 8GB laptops. Same multimodal capabilities as the E2B including audio. Also 128K context. This is the daily driver for most edge use cases.
26B Mixture of Experts (MoE) — The clever one. It has 25.2 billion total parameters but only activates 3.8 billion during inference, so it thinks like a 26B model but runs at 4B speed and memory cost. 256K context. No audio input, but handles text, images, and video. Runs on a 24GB GPU with quantization.
31B Dense — The powerhouse. All 31 billion parameters active all the time. Currently ranked #3 among all open models in the world on the Arena AI text leaderboard. 256K context. This is the one that competes with frontier proprietary models. Needs a single 80GB H100 unquantized, or a consumer GPU with Q4 quantization via Ollama.
All four are built on the same research that powers Google's commercial Gemini 3 model. You are essentially getting a distilled version of one of the most advanced proprietary AI systems in the world, for free, to run locally.
How I got it running in under 5 minutes
The fastest path to running Gemma 4 locally is Ollama — a tool that handles model downloading, quantization, and serving in a single command. If you do not have Ollama installed, get it at ollama.com. Then open your terminal and run:
ollama run gemma4:27b
That pulls the 26B MoE model in quantized form and drops you straight into a chat interface. For the 31B Dense:
ollama run gemma4:31b
For the edge models on lower-spec hardware:
ollama run gemma4:4b
If you prefer Hugging Face and want the full unquantized weights for fine-tuning or integration into your own pipeline, the models are live at huggingface.co/google under the gemma-4 organization. Kaggle also hosts all four variants. For cloud experimentation without local setup, Google AI Studio gives you immediate access to the 31B and 26B MoE — no download required.
Test 1: Writing and tone
I gave the 31B model a prompt I use to benchmark writing quality: "Write the opening paragraph of a long-form article about why most people never change, even when they know they should. Make it feel like a punch in the gut, not a self-help book."
What came back was sharp, direct, and carried the right weight. No filler phrases, no "In today's fast-paced world," no hedging. The model understood that I wanted urgency and delivered it. I ran the same prompt on several other local models I keep around for comparison — the Gemma 4 31B output was noticeably better in voice and rhythm. It did not feel like AI padding a response. It felt like a writer who understood the assignment.
The 26B MoE was close — genuinely close — with slightly less stylistic range on follow-up variations. Both were miles ahead of what Gemma 3 produced on the same task.
Test 2: Coding
I asked it to write a Python function that takes a list of dictionaries, deduplicates them by a specified key while preserving the first occurrence, and returns both the deduplicated list and a count of how many duplicates were removed. Then I asked it to add type hints, a docstring, and unit tests.
It nailed all of it on the first pass. The function was clean, the type hints were correct, the docstring followed Google style, and the unit tests covered edge cases I had not asked for — empty list, all duplicates, no duplicates, and a list with a key that does not exist in some entries. That last edge case was not in my prompt. The model anticipated it.
The LiveCodeBench v6 score of 80.0% for the 31B is not just a number. It translates directly into this kind of behavior: the model thinks about code the way a competent engineer would, not just syntactically but semantically.
Test 3: Math reasoning
I threw a competition-style math problem at it — the kind that appears in AMC and AIME papers, requiring multi-step reasoning rather than formula recall. The 31B worked through it step by step, showed its reasoning at each stage, caught an arithmetic error it had made in step three and self-corrected before reaching the final answer. The answer was correct.
The 89.2% score on AIME 2026 is not a fluke. This model reasons through math the way a strong student does — methodically, with self-checking. For anyone using AI for tutoring, homework help, or quantitative analysis, this is a meaningful capability upgrade over anything previously available at this size and price point (free).
Test 4: Image understanding
I dropped a screenshot of a complex data table — the kind you might export from a spreadsheet, with merged cells, color coding, and multiple header rows — and asked the model to summarize the key trends it could identify.
It correctly parsed the structure, identified the merged headers, read the numerical values accurately, and produced a coherent summary of the trends. It noted one area where the color coding appeared to mark outliers and flagged that it was inferring the meaning from context rather than a legend. That kind of epistemic honesty — acknowledging uncertainty rather than confabulating — is exactly what you want from a model doing visual analysis on real documents.
What I would use each model for
After a full day of testing across all four variants, here is how I would actually deploy them. The E2B and E4B belong on mobile applications and edge devices where privacy is non-negotiable — medical apps, personal assistants, anything where the data should never touch a server. The 26B MoE is the daily workhorse for developers: fast enough for interactive use, smart enough for serious reasoning tasks, and cheap enough on memory to run alongside other tools. The 31B Dense is the fine-tuning base and the high-stakes reasoning engine — use it when quality matters more than speed, or when you are building a specialized derivative for a specific domain.
The one thing that surprised me most
I expected the benchmark improvements. What I did not expect was how much the Apache 2.0 license changes the feel of using the model. With previous Gemma releases, there was always a nagging question about what you could and could not do commercially. That friction is gone. Gemma 4 feels like a tool you actually own — not a service you are borrowing under someone else's terms.
That might sound abstract, but for developers building products, it is the difference between building on a foundation and building on a lease. Gemma 4 is a foundation. The benchmark scores are impressive. The license is what makes it genuinely exciting.
Bottom line
Gemma 4 is the best free, locally-runnable AI model available as of April 2026 in its size class. The 31B Dense is a legitimate frontier-class model that competes with paid proprietary systems. The 26B MoE is an engineering masterpiece of parameter efficiency. The edge models are the most capable on-device AI I have run on consumer hardware.
If you have not tried it yet: open your terminal, run ollama run gemma4:27b, and spend 20 minutes with it. I think you will be as surprised as I was.