Gemma 4 vs Gemma 3 — What Changed?

Key Improvements in Gemma 4

Gemma 4 represents a meaningful architectural leap over Gemma 3 across five distinct areas. Each improvement addresses a real limitation that Gemma 3 users encountered in production use cases.

1. Thinking Mode

Gemma 4 E4B and Gemma 4 31B add extended reasoning through Thinking Mode — a chain-of-thought mechanism where the model reasons through a problem before committing to a final answer. Gemma 3 had no equivalent of this capability across any model size. Thinking Mode in Gemma 4 is particularly effective for multi-step math, code generation, and logical deduction tasks where intermediate reasoning steps improve final output quality.

2. Larger Context Windows

Gemma 4 extends context windows significantly across the lineup. The Gemma 4 31B model supports 256K tokens — double the 128K maximum in Gemma 3's 27B variant. Gemma 4 26B A4B also reaches 128K context, matching the Gemma 3 top tier but at a lower hardware requirement. Practically, the 256K context in Gemma 4 31B means an entire technical book, codebase, or legal document can be processed in a single prompt without chunking.

3. MoE Architecture (New in Gemma 4)

Gemma 4 introduces the 26B A4B Mixture-of-Experts variant — a model architecture type that was absent from Gemma 3 entirely. The Gemma 4 26B A4B activates only ~4B parameters per token despite having 26.1B total parameters, enabling higher throughput at a given memory budget compared to an equivalent dense model. For teams building high-throughput inference services, this MoE design is a meaningful option that simply did not exist in the Gemma 3 family.

4. Multimodal Improvements

Gemma 4 adds image understanding to the E4B — the smallest multimodal model in the Gemma 4 family at 4.4B parameters. In Gemma 3, multimodal capability started at the 4B tier but was more limited in scope. With Gemma 4 E4B, developers get reliable image-plus-text reasoning in a package that fits in 4GB of VRAM (Q4 quantization), significantly lowering the hardware bar for multimodal applications compared to what was practical with Gemma 3.

5. Tool Calling

Gemma 4 ships with an improved function calling format based on structured JSON output. Users of Gemma 3 frequently reported inconsistent tool use behavior — particularly around nested JSON schemas, optional parameters, and multi-tool calls in a single turn. Gemma 4 addresses these pain points with a more reliable tool use protocol that integrates cleanly with LangChain, LlamaIndex, and custom agent frameworks.

What Stayed the Same

• •Gemma Terms of Use license — Gemma 4 carries the same usage restrictions as Gemma 3. Both require acceptance of the Gemma Terms before commercial use.
• •GGUF / Ollama compatibility — Gemma 4 works with the same local runtimes as Gemma 3. If you ran Gemma 3 via llama.cpp or Ollama, the same toolchain works for Gemma 4 with no breaking changes.
• •Python API patterns with Hugging Face Transformers — The model loading and inference patterns from Gemma 3 translate directly to Gemma 4. Updating model_id to a Gemma 4 checkpoint is typically the only required code change.

Model Size Comparison

The Gemma 4 family restructures the size ladder compared to Gemma 3, introducing a new MoE variant and removing the 12B slot in favor of a significantly more capable 26B A4B option.

Should You Upgrade from Gemma 3 to Gemma 4?

For most use cases, yes. Gemma 4 is a strict improvement over Gemma 3 in the dimensions that matter most for local AI development: reasoning quality (Thinking Mode), context handling (256K max), and multimodal accessibility (E4B at 4GB Q4). The migration path is low-friction — the same runtimes, the same Python APIs, and similar Ollama tag conventions make the switch straightforward.

The only scenario where staying on Gemma 3 makes sense is if you have fine-tuned a Gemma 3 model for a specific task and lack the compute to re-run fine-tuning on Gemma 4 weights. For inference-only deployments with no custom training, Gemma 4 is the clear upgrade path.