Gemma 4 vs Gemma 3: What's New and Should You Upgrade?

Gemma 4 is a major upgrade over Gemma 3, but is it worth switching? The answer depends on what you're doing. This article breaks down every meaningful difference so you can make an informed decision.

The Big Changes at a Glance

License: From Restricted to Open

This is arguably the biggest change. Gemma 3 used Google's custom license that restricted commercial use in certain scenarios and had usage caps. Gemma 4 switches to Apache 2.0 — the same license used by projects like Kubernetes and TensorFlow.

What this means for you:

• No usage restrictions. Use it in any product, commercial or otherwise.
• No output ownership concerns. Google doesn't claim rights to model outputs.
• Fork and modify freely. Build derivative models without legal uncertainty.
• Enterprise-friendly. Legal teams love Apache 2.0 because it's well-understood.

If licensing was the reason you avoided Gemma 3 in production, that blocker is gone.

MoE Architecture: The 26B Model

Gemma 4 introduces a Mixture of Experts (MoE) model alongside the traditional dense models. The 26B MoE model has 26 billion total parameters, but only activates about 3.8 billion per token.

Why this matters:

• Speed: MoE runs much faster than a dense model of equivalent quality because fewer parameters are active
• Memory: The full 26B needs to be loaded, but inference computation is closer to a 4B model
• Quality: Benchmarks show the 26B MoE performs comparably to the 27B dense on most tasks

# Run the MoE model with Ollama
ollama run gemma4:26b

# Compare speed — you'll notice the MoE is significantly faster
ollama run gemma4:27b

Audio Input: E2B and E4B

Gemma 4 adds audio understanding through the E2B (2 billion) and E4B (4 billion) edge models. These can process spoken audio alongside text and images.

Use cases:

• Voice command processing on-device
• Audio transcription with context understanding
• Multimodal applications combining speech, text, and images

Note: Audio support is only in the E2B and E4B models. The larger 12B, 27B, 26B, and 31B models handle text and vision but not audio.

256K Context Window

Gemma 3 maxed out at 128K tokens. Gemma 4 doubles that to 256K. In practice:

Keep in mind that longer context uses more memory and slows inference. Just because you can use 256K doesn't mean you should — set context to what you actually need.

Benchmark Improvements

Gemma 4 shows meaningful improvements across standard benchmarks:

The biggest gains are in code generation (HumanEval) and mathematical reasoning (MATH). General knowledge (MMLU) improved too, but more modestly.

Migration Guide

From Gemma 3 with Ollama

# Remove old model
ollama rm gemma3:12b

# Pull new model
ollama pull gemma4:12b

# Your existing scripts using the Ollama API work unchanged
# Just update the model name

From Gemma 3 with transformers

# Before (Gemma 3)
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("google/gemma-3-12b-it")
tokenizer = AutoTokenizer.from_pretrained("google/gemma-3-12b-it")

# After (Gemma 4) — same API, different model name
model = AutoModelForCausalLM.from_pretrained("google/gemma-4-12b-it")
tokenizer = AutoTokenizer.from_pretrained("google/gemma-4-12b-it")

Breaking Changes

• Chat template format: Gemma 4 uses an updated chat template. If you're constructing prompts manually, check the new format.
• Tokenizer updates: Some special tokens changed. If you're doing token-level manipulation, verify your code.
• MoE models need different configs: The 26B MoE model requires frameworks that support MoE architectures. Not all tools handle this yet.

When to Stay on Gemma 3

There are valid reasons to stick with Gemma 3:

• Your tooling doesn't support Gemma 4 yet. Some frameworks lag behind new releases.
• You've fine-tuned Gemma 3. Your fine-tuned weights won't transfer to Gemma 4. Re-fine-tuning takes time and compute.
• Stability matters more than features. Gemma 3 has months of community bug-fixing behind it.
• You're on very constrained hardware. Gemma 4 models may have slightly higher memory requirements for the same size.

Next Steps

• Ready to pick a model? Check Which Gemma 4 Model Should You Pick? for detailed size recommendations
• Want to understand MoE vs Dense better? Read Gemma 4 26B vs 31B: MoE vs Dense for a deep comparison
• Curious how Gemma 4 stacks up against competitors? See Gemma 4 vs Llama 4 for a cross-family comparison

The bottom line: Gemma 4 is a better model in every measurable way, and the Apache 2.0 license removes the biggest commercial barrier. Unless you have a specific reason to stay on Gemma 3, upgrading is worth it.