Gemma vs Llama: Google and Meta's open model families in 2026

Nobody signs up for Gemma or Llama. You download a checkpoint, point a runtime at it, and live with whatever license came attached — and the license is the part that turns up later in a lawyer's email. That's the axis most comparisons skip, so it's the one I've spent the most words on.

Disclosure. I build Outlier, a local AI app for Mac, and it ships Qwen-family and Gemma-family weights: six tiers on Apache 2.0 Qwen checkpoints, one on Gemma-4-26B-a4b, none on Llama. I'm an interested party on this exact axis, so every claim below points at a primary document you can check.

These are weights, not products

Neither Google nor Meta ships a Mac app, an installer, or a chat window for these files. No conversation history, no document handling, no updates, no support desk. You get a folder of tensors and a config; you or some third-party application supply the runtime, the memory budget, the quantization and the prompt template. Google's own docs point at llama.cpp, Ollama, LM Studio, MLX and LiteRT-LM. It's a component, not a product — and that gap is what my company exists to fill, so read the rest of this skeptically.

Sizes: what you can actually get

Gemma 4 ships five sizes: E2B and E4B for edge and mobile, 12B, 26B-A4B (mixture-of-experts), and 31B dense. One family covers a phone, a MacBook Air and a Mac Studio. The 12B instruction-tuned build is 11.95B parameters with a context window up to 256K tokens; it takes text, image, audio and video in and produces text only. Training data cuts off January 2025, so anything newer has to arrive through retrieval.

Llama 4's ladder starts far higher. Its smallest member, Scout Instruct, is a 108.6B-total-parameter mixture-of-experts model with 17B active across 16 experts. Maverick is bigger. There's no small Llama 4 and nothing newer either: Scout and Maverick landed on Hugging Face 1–3 April 2025, and no Llama 5 exists. Meta's April 2026 flagship, Muse Spark, ships as a private API preview with no weight download, and llama.com now redirects to Meta's developer site.

Licensing: the difference that decides things

Gemma 4 is Apache 2.0. Google moved the family off its custom terms on 2 April 2026. Every official Gemma 4 repository in Google's Hugging Face org is tagged license:apache-2.0 and isn't gated — no access request, no queue. No user ceiling, no mandatory branding, no naming rule on derivatives, no geographic carve-out, and you may redistribute the weights inside a commercial app. Google also claims no rights in the outputs you generate.

The catch is that the family is split. Gemma 3 and earlier, plus variants like PaliGemma and FunctionGemma, stay under the custom Gemma Terms of Use (dated 1 April 2026), with a Prohibited Use Policy, redistribution notices, and an obligation to pass restrictions downstream. Ship two generations in one product and you track two regimes. That applies to me: Outlier's Gemma tier is disclosed under the Gemma Terms of Use, which I need to revisit now that Gemma 4 has moved.

Llama 4 is source-available, not open source. The Open Source Initiative has stated publicly that Meta's license fails the Open Source definition, so the phrase "open source Llama" is inaccurate however often you see it. The Community License, effective 5 April 2025, requires a separate license from Meta above 700 million monthly active users, requires you to prominently display "Built with Llama," and requires any derived model's name to begin with "Llama."

The sharpest condition is territorial. Meta's Acceptable Use Policy withholds the Section 1(a) grant from anyone domiciled in, or any company with a principal place of business in, the European Union with respect to multimodal models — and every Llama 4 model is natively multimodal. End users of a product built on one are exempt; the developer isn't. The weights are also gated on Hugging Face behind terms acceptance and a request queue.

Side by side

Strengths by task, and language coverage

Gemma 4's edge is capability per gigabyte. A roughly 12B model with a 256K context that ingests images, audio and video is a lot of surface area for something that fits on a laptop, and Google describes the family as pre-trained across more than 140 languages — well past what most locally-runnable models handle. It reads video; it can't make one.

Llama's edge is ecosystem maturity, and there it isn't close. Years of fine-tunes, LoRAs, quantization recipes, kernels and evaluation harnesses target Llama first, and it's the de facto research baseline, so its failure modes and training recipes are documented better than any other family's. Reproducing published work, Llama 3.x is the well-lit path and Gemma isn't.

Neither ships an application-level safety layer. Filtering and abuse tooling come as separate models — ShieldGemma, Llama Guard — that you wire up yourself. Both disclaim warranties entirely; the Gemma terms provide the model "AS IS" and "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND."

What runs on a Mac, at what RAM

The arithmetic is simple: 4-bit weights are half a byte per parameter, so a 12B model's weights land near 6 GB before context and overhead, and a 31B near 15–16 GB. Gemma 4 was Apple-silicon-ready immediately — MLX 4-bit conversions of E2B, E4B, 26B-A4B and 31B hit mlx-community on 2 April 2026, release day. Google also publishes quantization-aware-trained int4 GGUF builds (the 12B and 31B qat-q4_0-gguf repos, May–June 2026), and QAT holds quality better than naive post-hoc quantization.

One number from my own build: Outlier's Quick tier is Gemma-4-26B-a4b-it, a 15.61 GB download with a 16 GB RAM floor. Llama 4 is a different story — 108.6B parameters is north of 50 GB at 4 bits before overhead. As of mid-2026 the popular mlx-community Llama conversions are all Llama 3.x (3.1 8B, 3.2 1B and 3B, 3.3 70B), with Llama 4 absent. Running Llama on a Mac means running last generation's Llama.

One trap worth naming: run either family locally and no prompt touches a network, but that follows from local execution, not from a vendor promise. On Google's Gemini API, Gemma 4 is free-tier-only with the paid tier marked "Not available," and the free tier marked "Used to improve our products: Yes." Google's terms say human reviewers may read and annotate API input and output, and warn against submitting sensitive or personal information. Same weights, opposite privacy posture.

Where each family genuinely wins

Gemma 4 wins on licensing, cleanly. Apache 2.0 from a frontier lab, ungated, with no user ceiling, no branding obligation, no naming rule and no geography clause, isn't a marginal improvement on Llama's terms — it's a different category of freedom. If you're building something you intend to sell, that's worth more than a few benchmark points.

Llama wins on ecosystem depth, and it isn't close. For tooling, published fine-tuning recipes and comparability to prior research, Llama 3.x has more support than anything else you can download, and its 1B, 3B and 8B models are among the best-understood on Apple silicon.

And raw weights beat any packaged app, mine included. A downloaded checkpoint can't be deprecated, rate-limited, price-hiked or silently swapped, so results stay reproducible permanently. Both families grant full fine-tuning rights, so you can specialize on your own data without a vendor seeing it. Self-hosting costs nothing ongoing but hardware and power, against Outlier Pro at $20/month or $149/year — and the weights run on Windows and Linux, while Outlier is Apple Silicon Mac only, macOS 12+, with no web search, image generation or mobile app.

Who should pick which

Pick Gemma 4 if you're shipping something commercial and want the license question closed, if you're EU-domiciled, if the model has to fit ordinary hardware, or if you need multilingual coverage and multimodal input at laptop scale.

Pick Llama 3.x if you want the deepest fine-tuning ecosystem, you're reproducing published research, or you already run its 8B and 70B checkpoints and it works. Go in knowing you adopt the license conditions along with the model, and that the open line has been quiet since April 2025.

Pick a finished app if you'd rather use a local model this afternoon than assemble one. That's Ollama and LM Studio, both free, and it's Outlier — seven tiers, no account, no caps, nothing uploaded. Choose it for the packaging, not because you think the weights are weak. They aren't. They're just unfinished.