Llama vs Qwen: Which Open-Weight Family Should You Build On?

Choosing a base is not like choosing an API

Get an API choice wrong and switching costs you a few lines of code. Get an open-weight base wrong and switching costs you a fine-tuning dataset, an evaluation set, deployment tuning, and a whole round of pre-launch validation.

So this decision has more inputs: whether the licence works, whether the sizes cover your deployment, whether Chinese holds up, whether the community has parts you can reuse. Quality is only one input, and often not the decisive one — models of the same generation and size perform similarly on ordinary tasks, and what actually stalls projects is usually something else.

Chinese: the most concrete gap

If your product serves Chinese users, look here first.

Qwen treats Chinese as a training priority. Terminology lands correctly, word order reads naturally, long-form structure holds — output is deliverable as is.

Llama's Chinese is usable but plainly not a priority. It shows up as translated-sounding terminology, English-shaped sentence structure, and formal text needing a human polish. Using it in a Chinese product usually means a Chinese fine-tuning pass or a post-processing layer.

That fine-tuning pass is real work — data preparation, training, evaluation, iteration. If Chinese is core rather than incidental, choosing Qwen removes the whole block.

The size ladder shapes your architecture

Real systems rarely run a single model size.

Phones and edge devices can only run something small. The server workhorse is mid-sized, balancing cost and quality. A handful of hard tasks need the largest as a fallback. Ideally all three tiers come from one family, so prompt style is consistent, evaluation sets are reusable, and fine-tuning pipelines are shared — far cheaper than mixing families.

Qwen has broader size coverage, with first-party models from ones that fit on a phone up to server-class, plus a dedicated coding series and multimodal variants. Tiered deployment has more options.

Llama's official size ladder is more concentrated, but its community derivatives are extraordinarily rich — quantisations, distillations, domain fine-tunes — so obscure requirements often already have something off the shelf. That is coverage in another form.

Licensing: the gate before launch

Engineers skip this most often, and it is the thing most likely to explode a week before launch.

Llama uses a custom community licence rather than a standard open-source one. It attaches conditions to commercial use — including a separate authorisation above a very large user threshold — and has terms about how outputs may be used. Most small and mid-size projects are unaffected, but somebody has to have read it.

Qwen ships most sizes under a permissive open-source licence with much simpler commercial terms. Even so, terms are not identical across models and still need checking individually.

Whichever you choose, do not wave it through as "it's all open source." Have legal read the licence for the specific model you will actually ship. That is a matter of minutes and it avoids months of trouble.

Deployment and inference ecosystem

Both families are first-class citizens in mainstream inference frameworks, and quantisation is mature for both.

Llama's advantage is density of documentation: as the family that first brought open weights into the mainstream, it has the largest body of deployment tuning experience, failure reports, and ready-made scripts for given hardware. The odds of finding an answer when you hit a problem are higher.

Qwen's advantage is the richness of small sizes and availability in China: more options on low-spec devices, plus easier mirrors, cloud platforms, and support domestically.

If you plan to self-host, be clear on one thing: weights are free, compute is not. GPUs, power, operations, and tuning time are the real cost, and at small scale self-hosting is often more expensive than calling an API. Our local deployment review covers this.

Our recommendation

Chinese as a core requirement, a full size ladder needed, shipping inside China — choose Qwen.

Mainly English, reusing the overseas community's derivatives and research baselines, deploying overseas — choose Llama.

When in doubt, use a more practical method: assemble twenty typical real inputs from your business, run them through comparably sized models from both families, and read the output yourself. That test takes under a day and tracks your actual needs better than any leaderboard. Our guide on how to evaluate an LLM covers the method.