The release of Google’s latest open-weights family has sent shockwaves through the developer community, particularly for those interested in local LLM performance. In our comprehensive gemma 4 coding test, we evaluate the two heavyweights of the lineup: the 31B Dense model and the 26B Mixture of Experts (MoE) model. These models are marketed as the most capable open models "byte-for-byte," and our benchmarks aim to see if they can truly handle complex software engineering tasks. Whether you are building a React-based browser OS or a 3D flight simulator, understanding the nuances of this gemma 4 coding test is essential for optimizing your 2026 workflow. From multimodal portfolio generation to raw logic handling in JavaScript, we push these models to their absolute limits to see if they can replace closed-source giants for daily coding assistance.
The Gemma 4 Family: Technical Specifications
Before diving into the results of the gemma 4 coding test, it is important to understand the architecture behind these models. Google has released four distinct sizes, but the 26B and 31B models are the primary focus for heavy-duty development. The 26B model utilizes a Mixture of Experts (MoE) architecture with only 4B active parameters, making it incredibly efficient for local hardware. Meanwhile, the 31B Dense model is designed for maximum reasoning depth.
| Model Size | Architecture | Active Parameters | Context Window | License |
|---|---|---|---|---|
| Gemma 4 2B | Dense | 2.3B | 128K | Apache 2.0 |
| Gemma 4 4B | Dense | 4.5B | 128K | Apache 2.0 |
| Gemma 4 26B | MoE | 4B | 256K | Apache 2.0 |
| Gemma 4 31B | Dense | 31B | 256K | Apache 2.0 |
💡 Tip: For developers with limited VRAM, the 26B MoE model offers a "sweet spot" of performance, running significantly faster than the 31B Dense model while maintaining high reasoning capabilities.
Web Development: Building a Browser OS
One of the most revealing segments of our gemma 4 coding test involved asking the models to generate a functional "Browser OS" using HTML, CSS, and JavaScript. This task tests the model's ability to handle state management, UI aesthetics, and multi-component logic.
The 26B MoE model initially produced a minimalistic result. However, when provided with "negative reinforcement" (critique on its aesthetic choices), it pivoted brilliantly. The second iteration included:
- Translucent window effects.
- A "Rocket Ship" start menu.
- Functional apps including a Snake game and a Memory game.
- A dynamic theme engine (Forest, Midnight, and Sunset themes).
The 31B Dense model, tested via cloud APIs, produced a similar but slightly more polished initial UI called "Nova OS." It included a functional clock and a "Clicker Quest" game with auto-clicker upgrade logic. Interestingly, the 26B model's ability to follow complex aesthetic instructions through iterative prompting made it the preferred choice for front-end prototyping.
3D Game Development and Physics
In 2026, AI models are expected to do more than just write "Hello World." We tasked the models with creating a 3D subway scene that could be navigated using WASD keys.
| Feature | 26B MoE Result | 31B Dense Result |
|---|---|---|
| Movement | Fluid WASD logic | Standard WASD logic |
| Lighting | Basic brightness slider | Advanced realistic projection |
| Materials | Procedural textures | High-shine reflective surfaces |
| Combat | "Subway Protocol" FPS | "Subway Survival" FPS |
The gemma 4 coding test took an unexpected turn when we asked the models to convert these static scenes into First-Person Shooters (FPS). Both models successfully implemented:
- Enemy Spawning: Infinite waves of enemies.
- Weapon Mechanics: 3D weapon models with muzzle flashes.
- Advanced Physics: The 31B model implemented impressive weapon recoil that felt surprisingly tactile for AI-generated code.
⚠️ Warning: While the models excel at generating boilerplate for 3D games, they often struggle with "Health Logic." In our tests, enemies could be shot, but the player character was effectively invincible as the models neglected to write damage-taking functions.
Multimodal Capabilities: Wireframe to Website
Gemma 4 is multimodal, meaning it can "see" images and translate them into code. We provided a hand-drawn wireframe of a professional portfolio and asked for a high-end implementation.
The 26B model outperformed expectations, creating a site for a fictional engineer named "Levi Lapis." It didn't just copy the layout; it added a Live Inference Simulation feature. This included a visual representation of a neural network firing hidden units when a "Forward Pass" button was clicked. This level of creative interpretation from a hand-drawn sketch demonstrates that the gemma 4 coding test results for frontend developers are exceptionally positive.
Complex Application Testing: DAWs and Video Editors
To truly stress-test the logic of these models, we moved away from simple UI and into complex data processing. We asked Gemma 4 to build a Web Digital Audio Workstation (DAW) and a Video Editor.
The Web DAW Test
The model successfully generated a UI with a piano, drum engine, and EDM rompler. However, the logic was hit-or-miss:
- Drums: Fully functional (Kick, Snare, Hi-Hat).
- Piano: UI appeared, but no sound was produced.
- BPM: Functional slider that correctly adjusted the playback speed.
- Recording: The button existed but lacked the backend logic to actually capture audio.
The Video Editor Test
The generated video editor allowed for media imports and featured a timeline. While the "C" key worked for cutting clips and scaling worked for resizing, the anchor points were incorrectly set to the top-left rather than the center. This shows that while Gemma 4 understands the concept of complex tools, it still requires human oversight to fix coordinate geometry and deep signal processing.
Local Performance and Quantization
A major part of any gemma 4 coding test is how it runs on local consumer hardware. We utilized the DGX Spark for our local testing.
- 26B MoE: Ran flawlessly at Q8 quantization. It maintained high speeds (approx. 22-28 tokens per second) and followed instructions accurately.
- 31B Dense: Faced significant hurdles with local quantization. At Q4 and Q8, the model often produced "hallucinated" characters or responded in incorrect languages. For 2026, it is recommended to run the 31B model via high-quality FP16 cloud APIs or specialized NIM services until quantization kernels are further optimized.
For the most up-to-date documentation on deploying these models locally, you can visit the official Google AI Blog or check the latest model cards on Hugging Face.
FAQ
Q: Is Gemma 4 better than GPT-4 for coding?
A: In our gemma 4 coding test, we found that while it rivals top-tier models in UI generation and basic game logic, it still falls slightly short in complex backend architecture like real-time audio processing. However, its "byte-for-bite" performance is industry-leading for open weights.
Q: What hardware is needed to run the Gemma 4 26B model locally?
A: Because it is a Mixture of Experts (MoE) model with only 4B active parameters, you can run it on mid-range GPUs with at least 16GB-24GB of VRAM (depending on quantization) at very high speeds.
Q: Does Gemma 4 support multimodal coding?
A: Yes. As shown in our tests, you can upload images of UI wireframes or circuit diagrams, and the model can identify components and generate the corresponding code (HTML/CSS or Arduino C++).
Q: Is Gemma 4 free for commercial use?
A: Yes, the Gemma 4 family is released under the Apache 2.0 license, which allows for commercial use, modification, and distribution without the per-token fees associated with closed-source APIs.