Llama (Meta's Open Weights) vs Qwen: Which Open-Weight Family Should You Build On?

First, what happened on Meta's side

For the past two years, "Llama vs Qwen" was the most common pairing when choosing an open base. Meta's strategy changed this year, so that comes first.

In April 2026, Meta released its first closed flagship, Muse Spark, widely read as a farewell to the open Llama era. Meta's official line is that existing Llama models remain available as open source, but it has not committed to new Llama versions.

In August, Meta swung partway back: it released the weights of Muse Glimmer, a 30B dense model under Apache 2.0, with roughly 130K tokens of context, tool calling, and image input. With the official 4-bit quantisation it runs on a single 24GB or 32GB GPU, and it is aimed at always-on local agent workflows. Meta has also said it will open larger Muse Spark weights; whether that happens depends on an official release.

So "choosing Meta's open weights" today means choosing between two things: Llama, with the most mature ecosystem but no longer updated often, and Muse Glimmer, with a cleaner license and a narrower role.

Choosing a base is not choosing an API

Pick the wrong API and switching costs a few lines of code. Pick the wrong open base and the cost is redoing a whole set of fine-tuning data, evaluation sets, deployment tuning, and launch validation.

So this decision weighs more things: whether the license works, whether the sizes fit, whether the Chinese is good enough, whether the community has ready-made tools, and whether the family will keep being updated. Quality is only one item, and often not the decisive one.

Chinese: the most concrete gap

If your product serves Chinese users, check this first.

Chinese is a native training priority for Qwen. Terminology is right, word order is natural, and long-form structure holds, so it delivers usable quality out of the box.

Llama-family Chinese is usable but clearly not a training priority: translated-sounding terms, English-shaped sentences, and formal text that needs human polishing. Using it in a Chinese product usually means an extra round of Chinese fine-tuning or a post-processing layer. Muse Glimmer's Chinese performance still lacks thorough third-party evaluation, so test it on your own Chinese samples first.

The size ladder shapes your architecture

Real systems rarely use a single model size.

Phones and edge devices can only run very small models; the main server tier uses a mid-size model to balance cost and quality; a few hard tasks need the largest as a backstop. Ideally all three come from one family — the same prompting style, reusable evaluation sets, and a shared fine-tuning pipeline.

Qwen covers more sizes, with official models from phone-sized to server-sized, a dedicated coding series, multimodal variants, and open flagship weights now released. That gives more options for tiered deployment.

Meta's open lineup is now patchier: existing Llama models plus a single-size Muse Glimmer. The upside is Llama's enormous body of community derivatives — quantised, distilled, and domain-tuned versions that cover many niche needs; the cost is that the official ladder will no longer be filled in generation by generation as it used to be.

Licensing: the gate before launch

This is the step engineers most often skip, and the one most likely to blow up a week before launch. And this year, both families' licensing got more complicated.

Llama uses a custom community license, not a standard open-source one. Commercial use comes with conditions — very large user counts need a separate grant, and some multimodal models are restricted in the EU. Muse Glimmer switched to Apache 2.0: commercial use, modification, and redistribution are allowed without Llama's extra clauses — the most concrete change Meta made.

Qwen can no longer be summed up as "permissive": some small and mid-size models use Apache 2.0, some use a community license, the downloadable flagship weights use a dedicated license with revenue and user-count thresholds, and at least one image model was tightened from Apache 2.0 to research-only.

Whichever you pick, don't settle for "they're all open." Have legal read the license for the exact model you will use before launch — it takes tens of minutes and can save months of trouble.

Deployment and inference ecosystem

Both families are first-class citizens in mainstream inference frameworks, with mature quantisation.

Llama's advantage is information density: the most deployment and tuning experience, war stories, and ready-made scripts for any given hardware. Muse Glimmer launched with support in Ollama, LM Studio, llama.cpp, vLLM, and other mainstream runtimes, and Meta ships quantised versions and a speculative-decoding setup, so single-GPU deployment is easy.

Qwen's advantage is the range of small models and availability in China: more options for low-end hardware, and easier mirrors, cloud platforms, and support inside China.

If you plan to self-host, be clear on one thing: the weights are free, the compute is not. GPUs, power, operations, and tuning time are the real costs, and at small scale self-hosting is often more expensive than calling an API — see our local offline assistant guide. Also, once a local agent is connected to a file system or the internet, you still need sandboxing and human approval steps, whichever family you choose.

Our recommendation

If Chinese is a core requirement, you need a full size ladder, or you deploy inside China — choose Qwen, but check the license model by model.

If you work mainly in English and want to reuse the overseas community's many Llama derivatives and research baselines — stay with Llama; if what you want is a cleanly licensed local agent base that runs on a single GPU, go straight to Muse Glimmer.

When in doubt, decide with a practical test: prepare twenty typical real inputs from your business, run them through same-size models from both families, and review the outputs by hand. It takes less than a day and reflects your needs better than any leaderboard. For evaluation methods, see how to evaluate an LLM.