Meta Llama Models Guide: Llama 4, Muse, and What to Use

Meta’s usable open-weight Llama models in 2026 are Llama 4 Scout (109B parameters, 10M-token context) and Llama 4 Maverick (about 400B parameters, 1M-token context), and both date from April 2025. Meta’s newer flagship line, Muse Spark, launched in April 2026 as a proprietary model, so Llama 4 is no longer where Meta’s newest work lands.

I read the Llama 4 model cards on Hugging Face, Meta’s current developer page, and launch coverage in October 2026, then scanned Reddit threads from r/LocalLLaMA and Facebook posts about Llama. My pick: use Scout for long-context and single-server work, use Maverick when you can host a 400B model, and watch Meta’s Muse open-weight releases before starting anything new.

If you are comparing Llama with a hosted rival, start with our LLaMA vs ChatGPT comparison.

How I Researched This Guide

Specs and license terms come from the Llama 4 model cards on Hugging Face, and Muse details come from Meta’s own developer page and VentureBeat’s launch report, all checked in October 2026. Facts that appear only in third-party guides are labeled as such in the text.

I did not run the models or benchmark them. Quality claims are limited to what Meta documents and what community threads report, and the Reddit and Facebook items are cited by thread title, not as verified findings.

Which Meta Llama and Muse Models Are Current in 2026?

Meta lists 2 open Llama 4 models on Hugging Face, Scout and Maverick, alongside Llama Guard 4 for safety, while its newest models carry the Muse name. The table separates what you can download from what Meta keeps closed.

Model	Access	Size and context	Best for
Llama 4 Scout	Open weights	109B total, 17B active, 16 experts, 10M tokens	Long documents, single-server hosting
Llama 4 Maverick	Open weights	About 400B total, 17B active, 128 experts, 1M tokens	Higher-quality multimodal work
Llama Guard 4	Open weights	12B	Safety classification for prompts and outputs
Llama 4 Behemoth	Not released	Previewed as a roughly 2T-parameter teacher	Not available
Muse Spark 1.3	Closed (app and preview API)	Not published	Long-horizon coding and agentic work
Muse Glimmer	Open weights	30B per third-party reports	Always-on, local agents

Earlier generations remain available. Meta’s Facebook announcements describe Llama 3.1 with a 405B model and Llama 3.2 with 1B, 3B, 11B, and 90B variants, and Hugging Face still lists Llama 3 8B and 70B Instruct.

What Is the Meta Llama Model Family?

Llama is Meta’s family of open-weight large language models, released since its first launch so developers can download the weights and run them on their own hardware. Open weights differ from open source, because the Llama license restricts some uses.

The family spans small on-device models (Llama 3.2 at 1B and 3B parameters), mid-size general models (Llama 3.1 and Llama 3.3), and mixture-of-experts models (Llama 4). Cloud providers such as Amazon Bedrock host Llama 3, 3.1, 3.2, 3.3, and 4 Instruct models.

Meta also ships safety models in the same family, including Llama Guard 4 at 12B parameters and Prompt Guard at 86M parameters. Teams that self-host often pair a Llama model with one of these classifiers to screen prompts and outputs.

What Is Llama 4 Scout?

Llama 4 Scout is a 109B-parameter mixture-of-experts model with 17B active parameters, 16 experts, and a 10M-token context window. Meta released it on April 5, 2025, with multilingual text and image input and text and code output.

It supports 12 languages: Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese. The 17B active parameters keep inference cost closer to a mid-size dense model than to a 109B one.

The 10M-token window is the headline. Treat it as a retrieval ceiling, since third-party guides note that long-context retrieval and long-context reasoning are different tests. For a hosted comparison, read our Llama 4 vs GPT-4o article.

What Is Llama 4 Maverick?

Llama 4 Maverick is a 400B-parameter mixture-of-experts model with 17B active parameters, 128 experts, and a 1M-token context window. It shares Scout’s 17B active size and its April 5, 2025 release date.

Maverick targets higher output quality on multimodal and general tasks. The trade-off is memory, because all 400B parameters must be loaded even though only 17B run per token.

Hugging Face lists an FP8 version of Maverick with more downloads (42.6K) than the base instruct model (9.92K), which shows most people run the compressed build.

What Happened to Llama 4 Behemoth?

Llama 4 Behemoth has not shipped publicly, and Meta has not formally announced its cancellation. Meta previewed it in April 2025 as a roughly 2T-parameter teacher model with 288B active parameters and 16 experts.

Those Behemoth specs come from third-party trackers, not from a Meta model card, because none exists. No weights, API, or release date are published as of October 2026.

Plan around Scout and Maverick. A model with no weights cannot anchor a project, and Meta has given no timeline that changes that.

What Are Meta’s Muse Models and Is Llama Still Updated?

Muse is Meta’s newer model family, and its flagship Muse Spark launched on April 8, 2026 as a proprietary model available through the Meta AI app and a private API preview. VentureBeat reported that Meta said “Our current Llama models will continue to be available as open source.”

That statement does not say Meta will build new Llama models. Hugging Face shows the Llama 4 repositories last updated in May 2025.

Meta’s developer page now lists Muse Wonder, Muse Spark 1.1 to 1.3, Muse Glimmer, and Muse Spark Open. It calls Muse Spark Open “the most capable US open-weight model to date,” but the page gives no license or parameter count for it.

Third-party guides report Muse Glimmer as a 30B open-weight model under Apache 2.0, released August 10, 2026. Verify that on the Hugging Face model card before you build on it.

What Does the Llama 4 Community License Allow?

The Llama 4 Community License lets you use, modify, and distribute the models, with 4 conditions: a 700-million monthly-user cap, “Built with Llama” attribution, a “Llama” naming prefix for derived models, and the Acceptable Use Policy. Companies above 700 million monthly active users on the release date must request a separate license from Meta.

Derived models must start with “Llama” in the name. Distributions must include a copy of the agreement and a notice file.

The license sits below Apache 2.0 and MIT in permissiveness. DeepSeek’s V4.1 Flash weights ship under MIT, and Mistral lists Apache 2.0 for several models, so license terms are a real reason to look elsewhere. See our LLaMA vs Mistral comparison for that contrast.

Which Llama Model Should You Use?

Use Llama 4 Scout for long documents and smaller servers, Llama 4 Maverick for the strongest Llama quality, and a Llama 3.x model when you need a small footprint. The branches below tie each pick to a documented spec.

Choose Scout if:
– You process documents beyond 1M tokens, since only Scout offers 10M.
– You want 17B active parameters with a 109B weight footprint.

Choose Maverick if:
– You host 400B parameters or use an FP8 build on a multi-GPU server.
– You prioritize multimodal quality and accept a 1M-token window.

Choose a Llama 3.x model if:
– You run on-device or on a laptop, where Llama 3.2 offers 1B and 3B parameters.
– You need broad cloud availability, where Llama 3.3 is widely hosted.

Choose another family if:
– You want an MIT or Apache 2.0 license.
– You need weights released this year, since Llama 4 dates from 2025.

What Do Reddit and Facebook Users Say About Llama?

The community conversation has moved from Llama to Meta’s Muse releases and to rival open-weight models. On r/LocalLLaMA, “Introducing Muse Spark Open: the most capable US open-weight model to date” drew 1,357 upvotes and 359 comments.

Other recent thread titles show what local-model users ask about:
– “llama.cpp on the stage” reached 906 upvotes.
– “Every Model That Can Be Run On 10-16GB VRAM Ranked” reached 751 upvotes.
– “72 hours, four big open weights drops. The only one that fits in 24GB came from NVIDIA, of all companies” reached 852 upvotes.

On Facebook, Meta’s own Llama 3.1 announcement drew 1,209 reactions and 582 comments, and a 2025 post about Llama 4 Maverick argued that “the model layer is becoming a commodity.” Quora searches returned no usable results in my run.

Frequently Asked Questions (FAQ)

What is the newest Meta Llama model?

Llama 4, with Scout and Maverick released on April 5, 2025, is the newest Llama generation on Hugging Face. Meta’s newer releases use the Muse name.

Is Meta Llama open source?

Llama is open-weight under the Llama 4 Community License, not open source in the OSI sense. The license caps use above 700 million monthly users and requires “Built with Llama” attribution.

What is the difference between Llama 4 Scout and Maverick?

Scout has 109B parameters, 16 experts, and a 10M-token window, while Maverick has about 400B parameters, 128 experts, and a 1M-token window. Both use 17B active parameters.

Is Llama 4 Behemoth available?

No, Behemoth has not been released, and Meta has not published weights or a date. Third-party trackers describe it as a roughly 2T-parameter teacher model.

Is Meta still making Llama models?

Meta states current Llama models stay available as open source but has not announced new Llama versions. Its newest models use the Muse name, and Muse Spark launched as a proprietary model in April 2026.

Can I use Llama for commercial projects?

Yes, the Llama 4 Community License allows commercial use below the 700-million monthly-user threshold, with attribution and naming conditions. Read the full agreement on the model card.

Where can I download Llama models?

Download them from Meta’s Llama site or the meta-llama organization on Hugging Face, which lists about 70 models.

Final Verdict

Llama 4 Scout is the best Llama for long-context and easy hosting, Llama 4 Maverick is the best for quality, and Meta’s Muse open-weight releases are the models to watch for anything new. The April 2025 release date and the 700-million monthly-user license cap make Llama 4 a stable but aging choice.

Confirm model status on Meta AI and Hugging Face before you commit, since Meta changed its open-weight direction in 2026. For the licensing trade-offs behind this choice, read Open Source vs Closed Source LLMs. If you are weighing options, see our LLaMA Alternatives roundup, the best AI models hub, or the AI Comparison homepage.