Llama vs Gemma: Which Should You Run?

In the Llama vs Gemma choice for local use in late 2026, pick Gemma 4 for almost every new setup: it is the newer family, it has a model for phones, laptops and single 24 GB GPUs, every size reads images, the small sizes also take audio, and the weights use plain Apache 2.0 with no gated download. Pick Llama when your stack already depends on Llama 3.x or a Llama fine tune, or when you need Llama 4 Scout's 10 million token context window on a large server.

This guide compares Meta's Llama and Google DeepMind's Gemma on lineup, memory, context, inputs, licence, vendor benchmarks and tooling. Every figure comes from the official model cards and the Ollama library, checked on November 12, 2026. We did not run either family for this guide and we quote no benchmark of our own. For Meta against Alibaba's family, see Llama vs Qwen.

Llama vs Gemma at a glance

The short reading: Gemma 4 is the current, permissive family that covers consumer hardware well. Llama is the established family with the biggest context windows and the longest list of community fine tunes.

Where each family stands in 2026

Meta's Llama 4 Scout model card lists two models released on April 5, 2025. Scout uses 16 experts with 17B active and 109B total parameters and a 10M token context window. Maverick uses 128 experts with 17B active and 400B total and a 1M window. Both take text and images, and the card says they were tested for image understanding with up to 5 input images. Meta still offers the Llama 3 line: Llama 3.1 (8B, 70B, 405B), Llama 3.2 (1B and 3B text, 11B and 90B vision) and Llama 3.3 70B.

Meta has not released a Llama 5. Its newer open models use the Muse name, including Muse Spark Open, so a Llama vs Gemma decision today compares a family that paused in 2025 with one Google is still building on.

The Gemma 4 31B model card describes five instruction tuned sizes. E2B and E4B are small dense models that use Per Layer Embeddings, so they behave like 2.3B and 4.5B parameter models (5.1B and 8B counting embeddings). The 12B Unified model has no separate vision or audio encoder; it projects image patches and audio straight into the language model. The 26B A4B model is a mixture of experts with 25.2B total and 3.8B active parameters. The 31B model is dense, at 30.7B parameters. Google says the family was pretrained on over 140 languages with support for more than 35 out of the box.

Memory and hardware fit

Download sizes below come from the Ollama library tags for gemma4, llama4 and the Llama 3.x pages, checked today. Leave extra memory on top for context.

Two things stand out. At the very small end, Llama 3.2 is lighter: the 1B and 3B downloads are smaller than Gemma 4 E2B, so a machine with very little free memory may only fit Llama. At 24 GB, Gemma 4 has two strong models and Llama has nothing current; the jump from Llama 3.1 8B to Llama 3.3 70B skips the whole consumer GPU range.

The Gemma 26B A4B model also generates fast for its size, because only 3.8B parameters are active per token. Llama 4 Scout has the same trick at a larger scale (17B active), but you still need memory for all 109B weights. Our VRAM guide shows how to size any model, and can a local LLM use RAM instead of VRAM covers spilling a big model into system memory.

Context, inputs and languages

Llama 4 wins on context length by a wide margin. Scout's 10M and Maverick's 1M tokens dwarf Gemma 4's 256K. On a home machine this rarely matters, because filling even 256K tokens of KV cache takes a lot of memory, but it can matter on a server that reads whole code bases or long document sets. KV cache quantization helps either family stretch further.

Gemma 4 wins on inputs. Every size reads images with a configurable visual token budget (70 to 1120 tokens per image), every size can read short videos as frames (up to 60 seconds at one frame per second), and E2B, E4B and 12B accept audio clips up to 30 seconds for speech recognition and translation. Llama 3.2 1B, 3B and Llama 3.1 8B are text only; you need Llama 3.2 Vision or Llama 4 for images, and no Llama model takes audio. Note that Ollama's gemma4 tags currently list text and image input, so audio may need Transformers.

Languages tilt toward Gemma. Llama 4 lists 12 supported languages and Llama 3.2 lists 8 (English, German, French, Italian, Portuguese, Hindi, Spanish and Thai). Gemma 4 claims 35+ supported and 140+ in pretraining.

Gemma 4 also has a thinking mode. Put the <|think|> token at the start of the system prompt, or pass enable_thinking=True in Transformers, and the model reasons before answering. Llama models answer directly. Gemma 4 adds a proper system role and native function calling, which helps if you build agents.

Benchmarks: what the cards say

Neither publisher tested the other's models. Meta compares Llama 4 with Llama 3.x, and Google compares Gemma 4 with Gemma 3. Two tests appear on both instruction tuned tables, so here they are, with the caveat that settings and harnesses can differ.

On both, the Gemma 4 workstation models report higher scores than Llama 4 Maverick, a model with about 13 times the total parameters of the 31B. Part of the gap is age: Llama 4 shipped in April 2025 with an August 2024 data cutoff, and the two cards may use different settings. The tables do not settle every task, so run your own prompts before you commit. Our open weight model leaderboard guide explains which independent boards to trust.

Licence: Gemma vs Llama terms

For a product, this is often the deciding factor.

The Llama 4 Community License is free and allows commercial use, with conditions. Companies whose products had more than 700 million monthly active users on the release date must request a separate licence. If you distribute the model or a product built on it, you must show "Built with Llama". If you use Llama or its outputs to train a model you distribute, its name must start with "Llama". Use must follow Meta's acceptable use policy. The Hugging Face repos are gated behind a form asking for your legal name, date of birth and organization.

The Gemma 4 cards list Apache 2.0, and the repos are not gated. That is a change from earlier Gemma releases: Gemma 3 used the separate Gemma Terms of Use and a gated download. Read the card for the exact model you pick, since community fine tunes can carry other terms. Neither family publishes its training data, so both are open weight rather than fully open source; our explainer on open weight vs open source covers the difference.

Running and fine tuning both

Both families run in Ollama, llama.cpp, LM Studio and vLLM. To try them side by side on one prompt:

# Llama 3.1 8B, a 4.9 GB download
ollama run llama3.1:8b

# Gemma 4 12B, a 7.7 to 8.0 GB download
ollama run gemma4:12b

Google recommends temperature 1.0, top_p 0.95 and top_k 64 for Gemma 4 in every use case, so set those before you judge it. Our step by step guide to running Gemma 4 locally covers size tags, thinking mode and vision tips.

For training your own adapter, Unsloth supports both families, and Gemma 4 12B Unified can be fine tuned in one pass across text, image and audio. Llama's edge is its back catalog: Llama 3.x has been a common base for fine tunes and tutorials for a long time. If you distribute a Llama fine tune, remember the naming rule. See how to fine tune an LLM locally for the full workflow.

Which should you use?

• Choose Gemma 4 for a new local assistant, coding helper or agent on an 8 GB to 32 GB machine; for screenshots, documents, charts or audio; for non English text; or when you want Apache 2.0 terms with no gated download. On a 24 GB GPU, start with gemma4:26b.
• Choose Llama if you already run Llama 3.x in production and it works; if you depend on a specific Llama fine tune; if you need 1M to 10M tokens of context on server hardware; or if your device has under 4 GB of free memory, where Llama 3.2 1B (1.3 GB) fits and Gemma 4 E2B does not.
• Choose neither yet if you need the smallest fully documented model; compare SmolLM vs Gemma and SmolLM vs Llama.

FAQ

Is Gemma better than Llama?

For most new local projects in 2026, yes. Gemma 4 is newer, reports higher GPQA Diamond and MMLU Pro scores than Llama 4 Maverick on the vendors' own cards, reads images on every size, and uses Apache 2.0. Llama 4 still leads on context length, and Llama 3.x has a larger catalog of fine tunes.

Gemma 4 vs Llama 4: which needs less memory?

Gemma 4 by far. Its largest model, 31B, is a 19 to 20 GB download at 4 bit in Ollama. The smallest Llama 4 model, Scout, is 67 GB at 4 bit, because all 109B parameters must be loaded even though only 17B are active.

Llama 3.2 vs Gemma 4 for a small laptop?

Gemma 4 E2B or E4B if you have 8 GB or more: they add image input, thinking mode and 128K context. Llama 3.2 1B or 3B if memory is very tight, since their downloads are 1.3 GB and 2.0 GB against 4.6 GB for E2B.

Can I use Llama and Gemma commercially?

Yes. Gemma 4 uses Apache 2.0 with no extra conditions. Llama's community licences allow commercial use but add attribution, naming rules, an acceptable use policy and a separate licence for companies above 700 million monthly active users.

Does Llama have a thinking mode like Gemma 4?

No. Llama 3.x and Llama 4 answer directly. Gemma 4 has a thinking mode you switch on with the <|think|> token or enable_thinking=True, and you can leave it off for quick chat.

Do Llama and Gemma run in the same tools?

Yes. Ollama, llama.cpp, LM Studio and vLLM support both. Use GGUF files for llama.cpp, Ollama and LM Studio, and the original weights for vLLM on a GPU server.