Gemma 4 vs Gemma 3: What Actually Changed?

Quick Answer

Gemma 4 (released April 2, 2026) replaces Gemma 3’s restrictive custom license with Apache 2.0, adds a native Thinking Mode, native audio input on the small models, and a new encoder-free 12B “Unified” model that Google barely marketed. The benchmark gap is not subtle: AIME 2026 math scores jump from 20.8% (Gemma 3 27B) to 89.2% (Gemma 4 31B), and LiveCodeBench goes from 29.1% to 80.0%. Unless you’re locked into a legacy Gemma 3 pipeline, Gemma 4 is the better pick for anything you’re starting today.

See the Official Gemma 4 Model Card →

I’ve been running local LLMs on a mid-tier workstation and an old Android tablet for about two years now, and Gemma 3 was the model I kept coming back to whenever a client project needed something I could self-host without a legal team reading the license twice. It was solid. It was also, honestly, a little safe — good multilingual coverage, decent function calling, but the custom Gemma license always meant a pause before shipping anything commercial.

Google DeepMind released Gemma 4 on April 2, 2026, and this isn’t a version bump. It’s a different philosophy — five model sizes instead of four, a real reasoning mode, native audio on the edge models, and a license that finally lets you build a product on it without a call to legal.

Quick Comparison: Gemma 4 vs Gemma 3

Here’s the feature matrix that actually matters if you’re choosing between them right now.

How I Tested Both Models

My process is the same one I use for every model comparison on this site: pull the quantized GGUF or MLX weights, load them through LM Studio and Ollama, and run a mix of my own logic puzzles, a 200-page PDF retrieval test, and a handful of real client-style coding prompts. For Gemma 4 specifically, I spun up the E4B on an old Android tablet and the 31B Dense on my workstation, and cross-referenced everything against the numbers published in Google’s own model card rather than trusting a single benchmark run.

One rough edge I hit early: the default sampling parameters matter a lot more with Gemma 4 than they did with Gemma 3. Google’s documentation specifies temperature=1.0, top_p=0.95, and top_k=64 as the standard configuration, and running the 31B at a lower temperature (something I do out of habit) noticeably flattened the Thinking Mode output — it stopped self-correcting on logic puzzles and just committed to its first answer.

Gemma 3 Overview

Gemma 3 launched in four sizes — 1B, 4B, 12B, and 27B — with a 128K context window on everything except the 1B model, which topped out at 32K. Vision support started at 4B; the 1B was text-only. It handled 140+ languages in pretraining and was genuinely strong for its size, which is why it became the default recommendation for anyone running a single RTX 3090 or 4090.

The friction was always the license. The Gemma Terms of Use weren’t hostile, but they weren’t Apache 2.0 either — Google reserved usage restrictions that made some legal teams nervous about production deployment, and there was no native reasoning mode or audio support of any kind.

Gemma 4 Overview: Five Models, Not Four

This is the part most coverage of Gemma 4 gets wrong — including my own earlier draft of this article. Gemma 4 ships in five sizes, not four. The 12B “Unified” model gets buried in Google’s own launch materials, but it’s the most architecturally interesting release in the family.

The “E” in E2B/E4B stands for “effective” parameters — these models use Per-Layer Embeddings (PLE), where each decoder layer gets its own lightweight lookup table, keeping the active compute footprint far smaller than the total parameter count suggests. The “A” in 26B A4B stands for “active” — it’s a Mixture-of-Experts model with 8 active experts out of 128 total (plus 1 shared expert), so you get 26B-model knowledge at roughly 4B-model inference speed.

The 12B Unified model is the outlier. Every other Gemma 4 size uses dedicated encoders to pre-process images and audio before handing them to the language model. The 12B skips that step entirely — it projects raw image patches and audio waveforms directly into the transformer’s embedding space through lightweight linear layers. Practically, that means lower multimodal latency and the ability to fine-tune the entire model, vision and audio included, in a single training pass, instead of juggling separate encoder checkpoints.

If you were running Gemma 3 12B against Phi-4, this new Unified architecture changes that comparison meaningfully — it’s a different animal from last year’s 12B.

Architecture: What Changed Under the Hood

Gemma 4’s biggest structural change is its hybrid attention mechanism: local sliding-window attention (1024 tokens on the larger models, 512 on E2B/E4B) interleaved with full global attention, with the final layer always kept global. Global layers use unified Keys and Values plus Proportional RoPE (p-RoPE) to keep memory usage down on long sequences. This is the direct technical reason long-context retrieval improved so dramatically — Gemma 3 never had this interleaving.

Gemma 4 also introduces native support for the system role in the chat template, which Gemma 3 lacked — previous generations had to fake system instructions by prepending them to the user turn.

Benchmark Results: The Generational Leap

These are Google’s own published numbers for instruction-tuned models, comparing Gemma 3 27B (non-thinking) against Gemma 4 31B.

The Codeforces ELO jump is the single most dramatic number in the whole card: Gemma 3 27B scored 110, Gemma 4 31B scored 2150. That’s not a tuning improvement — that’s a different class of coding model.

For context on where these numbers land against closed frontier models, our Grok 4 vs GPT-5 benchmark comparison is a useful companion read.

Thinking Mode: How It Actually Works

Gemma 3 was a “fast” model — you asked, it answered, no visible reasoning step. Gemma 4 introduces a genuine Thinking Mode, and the implementation detail is worth knowing if you’re building on the API rather than just chatting with it.

Thinking is triggered by placing the <|think|> token at the start of the system prompt. When enabled, the model outputs its reasoning inside a <|channel>thought block before the final answer. Remove the token and thinking is disabled — though on every model except E2B and E4B, the model still emits the channel tags with an empty thought block rather than skipping them entirely. In multi-turn conversations, Google’s own docs specify that historical thinking content should be stripped from prior turns before the next user message, with one exception: tool-call turns, where the thinking content needs to be preserved for the model to stay coherent about why it called a tool.

In practice, on a logic puzzle where Gemma 3 gave me a confident wrong answer on the first try, Gemma 4 with thinking enabled worked through the problem, caught its own error mid-reasoning, corrected course, and landed on the right answer. It took noticeably longer — thinking adds real latency — but for anything math- or logic-adjacent, it’s worth the wait.

Licensing: The Actual Biggest Change

Gemma 3 lived under the Gemma Terms of Use — open-weight, but with Google-defined usage restrictions that made procurement and legal teams nervous. Gemma 4 ships under Apache 2.0, the same license used by most of the open-weight ecosystem. That means fine-tuning, redistribution, and commercial integration without an acceptable-use policy hanging over your deployment. It’s the change that doesn’t show up in a benchmark table, but for a startup deciding what to build on, it’s arguably more consequential than the AIME score.

Modality: Audio, Video, and the Catch

Gemma 3 could see images (4B and up). Gemma 4 adds video-frame understanding across every size, plus native audio — but only on E2B, E4B, and the 12B Unified model. The 26B MoE and 31B Dense stay image-and-text only; Google didn’t build an audio encoder for the largest sizes.

Audio input is capped at 30 seconds per clip, and video is capped at 60 seconds when processed at one frame per second. For image inputs, Gemma 4 supports five configurable visual token budgets — 70, 140, 280, 560, and 1120 tokens per image — so you can trade detail for speed: lower budgets for classification or video frame sampling, higher budgets for OCR and document parsing where small text matters.

If audio and agentic coding are both on your shortlist, it’s worth reading how Claude AI handles coding tasks for comparison before committing to a fully local stack.

Use-Case Comparison

Mobile developer: Gemma 4 E2B, no contest. It’s designed for on-device deployment and integrates with LiteRT-LM for Android. Gemma 3 never had an equivalent purpose-built edge variant.

Coding assistant: Gemma 4 31B. The 29.1% → 80.0% LiveCodeBench jump and the Codeforces ELO leap from 110 to 2150 aren’t close calls.

If you’re weighing a fully local coding setup against a browser-based agent, our guide to running Claude Code in the browser covers the trade-offs.

Cost-sensitive hosting: The 26B A4B MoE is the sleeper pick — 26B worth of knowledge with only 3.8B active parameters per token, so inference speed lands closer to a 4B model while retrieval and reasoning quality stay much higher than Gemma 3 27B ever managed.

For a broader look at how open-weight and closed models trade off on cost versus capability, see our Claude 2.1 vs GPT-4 breakdown.

Pricing and Hardware Requirements

Both families are free as open weights. What differs is the cost to actually run them. Gemma 3 27B needed roughly 20GB of VRAM at 4-bit quantization. Gemma 4 31B Dense needs a comparable ~18GB in 4-bit, but the 26B MoE runs comfortably in around 12GB thanks to its sparse activation — these are community-reported estimates, not official Google figures, so treat them as a starting point rather than a spec sheet. If you’d rather skip local hosting entirely, Google also serves all five sizes through Vertex AI and AI Studio on a pay-as-you-go basis.

FAQ

Is Gemma 4 really better than Gemma 3?

Yes, and not marginally. On AIME 2026, Gemma 4 31B scores 89.2% versus Gemma 3 27B’s 20.8% — a generational leap rather than a routine update.

Can I use Gemma 4 commercially?

Yes. Gemma 4 is released under Apache 2.0, which explicitly permits commercial use, modification, and redistribution — a clear departure from Gemma 3’s custom license.

Which model is best for a phone?

Gemma 4 E2B. It runs in under 1.5GB of RAM, works fully offline, and integrates with LiteRT-LM for Android — Gemma 3 never had an equivalent edge-optimized variant.

Does Gemma 4 support voice input?

Yes, but only on E2B, E4B, and the 12B Unified model. The 26B MoE and 31B Dense models handle text and image only, not audio.

What’s the 12B “Unified” model, and why does it matter?

It’s an encoder-free variant that projects raw image and audio data directly into the language model instead of using separate encoder modules. That means lower multimodal latency and the ability to fine-tune the whole model — including vision and audio — in one pass.

Is Thinking Mode worth using?

For math, logic, and coding tasks, yes — the self-correction behavior is real and measurable in the benchmarks. It does add latency, so for casual chat you can leave it disabled by omitting the <|think|> token.

Closing Line

If you’re maintaining a frozen pipeline built on Gemma 3 and can’t justify a migration right now, it’s still a stable, capable model — there’s no need to panic-rip it out. But for anyone starting fresh, evaluating a commercial deployment, or building anything agentic, Gemma 4 is the clear pick. Google didn’t just improve the numbers; it fixed the license, added a genuine reasoning mode, and quietly shipped a fifth model — the 12B Unified — that deserves more attention than it’s gotten. I’ve moved my own local RAG pipeline over to the 26B MoE, and the retrieval quality alone made the switch worth it.