Llama vs Qwen: Which Should You Use?

In the Llama vs Qwen choice for a new project in late 2026, pick Qwen for most local and self hosted work: the family is newer, covers every size from 0.8B to a 27B dense model that fits a 24 GB GPU, ships under Apache 2.0, and needs no gated download. Pick Llama when you already run Llama 3.x in production, need a specific Llama fine tune, or want Llama 4 Scout's very long context on a large server. Meta has not shipped a new Llama since April 2025; its newer open models carry the Muse name instead.

This guide compares the two families on model lineup, licence, hardware fit, context length, languages and tooling, using the official model cards, Meta's Llama pages and the Ollama library, all checked on November 9, 2026. Nothing here was run first hand, and we quote no benchmark of our own. For tiny models only, see SmolLM vs Llama. For Google's family, see Llama vs Gemma. For a wider list, see open source LLM models to run locally.

Llama vs Qwen at a glance

The short reading of that table: Qwen is the active family, Llama is the established one.

Where each family stands in 2026

Meta's Llama 4 model card lists two models released on April 5, 2025: Scout, a mixture of experts model with 16 experts, 17B active and 109B total parameters and a 10M token context window, and Maverick, with 128 experts, 17B active and 400B total and a 1M context window. Both take text and images and were trained on data with an August 2024 cutoff. Meta's Llama 3 page still offers Llama 3.1 (8B, 70B, 405B), Llama 3.2 (1B and 3B text, 11B and 90B vision) and Llama 3.3 70B.

There is no Llama 5. Meta's open releases since then use a new brand: Muse Glimmer, a 30B model released August 10, 2026 under Apache 2.0 from the meta-models account, and Muse Spark Open in October. Meta has not said whether the Llama name will return. So a "Llama vs Qwen" decision today is really a choice between a family that stopped in 2025 and one that is still shipping.

The Qwen side moved fast. Qwen3.5 arrived in February 2026 with small dense models (0.8B, 2B, 4B and 9B on Hugging Face) and support for 201 languages and dialects, per the Qwen3.5-9B card. Qwen 3.6 added a 35B-A3B mixture of experts model and a dense 27B. The Qwen3.8-27B card describes the newest dense model: 27B parameters, a vision encoder for images and video, a native context of 262,144 tokens, and thinking mode on by default with a reasoning_effort setting. The experimental Qwen3.8 Flash Next previews the next architecture.

Licence: Llama vs Qwen terms

This is the biggest practical difference for anyone building a product.

The Llama 4 Community License is free and allows commercial use, but it is custom. Companies whose products had more than 700 million monthly active users on the release date must ask Meta for 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, that model's name must start with "Llama". Use must follow Meta's acceptable use policy. Llama 3.x carries its own community licences with similar conditions. On Hugging Face, the Llama repos are gated: you fill in a form with your legal name and organization and wait for approval.

The main Qwen cards (Qwen3.5 0.8B to 9B, Qwen3.6 35B-A3B, Qwen3.8 27B) list Apache 2.0, and the repos are not gated. Apache 2.0 has no user cap, no naming rule and no extra acceptable use policy beyond the law. One exception: Qwen3.8 Flash Next uses a separate Qwen community licence, so read the card for each model you pick. Our guide to open weight vs open source explains why neither family counts as fully open source by the OSI definition: training data is not released.

If your legal team wants the least paperwork, Qwen's Apache 2.0 models win clearly.

Hardware fit: which one runs on your machine

Size decides most local choices, so here are the default Ollama download sizes from the Llama 4 tags, the Qwen3.8 tags and the matching Llama 3.x and Qwen3.5 library pages, checked November 9, 2026. Plan a few extra gigabytes on top for context.

The middle of the table is where Qwen pulls ahead. A single 24 GB card or a 32 GB Mac runs Qwen3.8 27B comfortably, and Meta has no recent model in that slot under the Llama name (Muse Glimmer 30B fills it now, but that is a different family). Llama 4 Scout has only 17B active parameters, so it generates quickly once loaded, but you still need memory for all 109B. Our VRAM guide explains how to size any model, and can a local LLM use RAM instead of VRAM covers spilling a large model into system memory.

Context, languages and modalities

Llama 4 wins on raw context length. Scout's model card lists 10M tokens and Maverick 1M, far beyond Qwen's native 262,144. In practice, local machines rarely have the memory to fill millions of tokens of KV cache, so this matters most on servers.

Qwen wins on language coverage. Llama 4 lists 12 supported languages (Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai and Vietnamese), though Meta says pretraining covered about 200. Qwen3.5 claims support for 201 languages and dialects, including Chinese, which Llama 4 does not list as supported.

For images, both families handle pictures in their newest models. Qwen3.5, 3.6 and 3.8 also take video input on their open weights. Llama 3.2 Vision and Llama 4 take images only.

Qwen models also think before answering by default. That helps on hard coding and math tasks, but it costs tokens and time. You can turn it off per request, or lower reasoning_effort on Qwen3.8. Llama models answer directly, which some people prefer for quick chat.

Benchmarks: what the cards say

We did not run either family, so we only report what the publishers claim and point out the gaps. Meta's Llama 4 card compares Scout and Maverick with Llama 3.1 and 3.3, not with Qwen. The Qwen3.8 27B card compares it with Qwen3.6, Muse Glimmer 30B and a closed Anthropic model, not with Llama 4. Neither publisher gives a direct head to head, and each picks its own settings.

Independent leaderboards are a better source than either card. Our open weight model leaderboard guide explains which public boards to trust and how to read them. Keep the age gap in mind when you compare: Llama 4's training data stops in August 2024, while Qwen3.8 came out in 2026, so a fair test pits each family's newest model that fits your hardware. Always test on your own prompts before you commit.

Tooling and fine tuning

Both families run everywhere that matters. Ollama, llama.cpp, LM Studio and vLLM support each of them. A quick test from a terminal:

# Llama 3.2 3B, a 2.0 GB download
ollama run llama3.2:3b

# Qwen3.8 27B at 4 bit, an 18 GB download
ollama run qwen3.8

Llama's advantage is its back catalog. Llama 3.x has been a common base for fine tunes, adapters and tutorials for a long time, and many teams have production prompts and evaluations built around it. If your stack depends on a specific Llama fine tune, switching carries real cost.

Qwen's advantage is freshness and support. Unsloth publishes a run guide and quantized files for Qwen3.8, and the Hugging Face model tree for Qwen3.8 27B already lists hundreds of derived models. For training your own adapter on either family, see how to fine tune an LLM locally. Watch the licence again here: a fine tune of Llama 4 you distribute must carry "Llama" at the start of its name, while an Apache 2.0 Qwen fine tune has no such rule.

Which should you use?

• Choose Qwen for a new local assistant, coding helper or agent on a laptop, a 24 GB GPU or a Mac; for multilingual work, especially Chinese; for video input; or when you want Apache 2.0 terms with no gated download.
• Choose Llama if you already run Llama 3.x in production and it works; if you need a specific Llama based fine tune; if you need Llama 4 Scout's 10M context on server hardware; or if a client requires a US developed model and you do not want to move to Meta's newer Muse models.
• Consider Meta's Muse models if you like Meta's ecosystem but want something current. Muse Glimmer 30B is Apache 2.0 and aims at the same 24 to 32 GB class as Qwen3.8 27B.

For most readers starting fresh, Qwen3.8 27B on a 24 GB card, or Qwen3.5 4B or 9B on a smaller machine, is the practical default (ollama run qwen3.5:9b is a 6.6 GB download). Our step by step Qwen 3.8 local guide covers setup.

FAQ

Is Qwen better than Llama?

For new local projects in 2026, usually yes. Qwen's models are newer (Qwen3.8 versus Llama 4 from April 2025), cover more sizes for consumer hardware, and use Apache 2.0. Llama 4 still leads on maximum context length, and Llama 3.x has a larger back catalog of fine tunes.

Is Llama still being updated?

Not under the Llama name. The last Llama models are Llama 4 Scout and Maverick from April 2025. Meta's open releases since then are Muse Glimmer (August 2026) and Muse Spark Open (October 2026), and Meta has not said whether a Llama 5 will come.

Can I use Llama and Qwen commercially?

Yes, both allow commercial use. Qwen's main models use Apache 2.0, which has no extra conditions. Llama's community licences add conditions: a separate licence for companies above 700 million monthly active users, "Built with Llama" attribution, and naming rules for models trained with Llama.

Which is better for a small laptop, Llama 3.2 or Qwen3.5?

Both have 1B to 4B class models that run in a few gigabytes. Qwen3.5 offers more steps (0.8B, 2B, 4B and 9B), adds image and video input, and is newer. Llama 3.2 1B and 3B are text only but well supported and widely fine tuned. See SmolLM vs Llama for more tiny model options.

Do Llama and Qwen run in the same tools?

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

Why does Qwen think before it answers?

Qwen3.5 and later models run in thinking mode by default, writing a reasoning section before the final reply. It improves hard tasks but adds latency. You can disable it with enable_thinking: false in the chat template settings, or lower reasoning_effort on Qwen3.8.