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Qwen vs Llama (Meta)

A plain-English comparison to help you choose between them.

01VERDICT

For new self-hosted coding work, pick Qwen. It is Alibaba's Apache-2.0 open-weight line, actively maintained, and the sensible default when you own the deployment: prototype locally with Ollama, serve production with vLLM. Llama is not a wrong choice; Meta's Llama 4 open weights remain downloadable, deployable and widely supported by the tooling ecosystem. But its open-weight line has had no major new family release since April 2025, so the momentum for fresh work sits with Qwen.

02AT A GLANCE

Side by side

	Qwen	Llama (Meta)
Summary	Qwen is Alibaba's model line, and the part this guide recommends is the open-weight family: the models you download and serve on your own hardware.	Llama is Meta's open-weight model family. The Llama 4 weights remain downloadable and deployable, and they are widely supported across the tooling ecosystem: this was the name most tools integrated first.
Best for	The current default for new self-hosted coding work Apache-2.0 open weights, free to download and run Coding agents on infrastructure you control Local prototyping with Ollama, production with vLLM An actively maintained open-weight coding line	Workloads where no vendor may touch the data Keeping an established Llama deployment in service Teams whose local tooling assumes Llama support Serving internal assistants with no external API calls Planning model spend as infrastructure, not subscriptions
Less suited to	Anyone who wants coding AI without owning infrastructure is better served elsewhere. GitHub Copilot is the broadest and cheapest entry, working in-editor with a free tier; Claude Code goes deeper when the specification is ambiguous and the work genuinely hard; and Kimi Code offers an open-source coding agent you adopt whole, with no serving stack to build first. The standing obligation is operational. The weights cost nothing to download, but the spend shifts to GPUs and the engineering time to serve them, and that commitment runs for as long as the deployment does. Treat the decision as buying infrastructure rather than software: capacity to plan, a serving stack to operate, and a team that owns it when it breaks.	Anyone starting new self-hosted coding work is better served by Qwen, the actively maintained Apache-2.0 line that has become the current default for exactly that job. And teams that simply want a capable assistant without owning any infrastructure should look at the hosted general tools instead: ChatGPT for breadth and a familiar starting point, Claude for long documents and careful prose. The standing commitment is operational: serving, capacity, monitoring and upgrades are all yours to own. The category's headline benefit also only holds while you keep it, because routing the weights through a third-party host brings that provider's terms back into the picture.
Cost	Free tier + paid plans	Free
Ease	Intermediate	Advanced
Openness	Runs privately (self-hostable)	Runs privately (self-hostable)
Data	Qwen's hosted API runs on Alibaba Cloud, which carries a jurisdiction question for some organisations. Self-hosting the open weights removes it: the model runs on your own infrastructure, so data governance stays entirely in your hands.	Self-hosted weights keep data entirely on your infrastructure with no vendor in the path, which is the cleanest data posture in the category; running through a third-party host reintroduces that provider's terms, so check them as you would any processor.

Qwen

Summary

Qwen is Alibaba's model line, and the part this guide recommends is the open-weight family: the models you download and serve on your own hardware.

Best for

The current default for new self-hosted coding work
Apache-2.0 open weights, free to download and run
Coding agents on infrastructure you control
Local prototyping with Ollama, production with vLLM
An actively maintained open-weight coding line

Less suited to

Anyone who wants coding AI without owning infrastructure is better served elsewhere. GitHub Copilot is the broadest and cheapest entry, working in-editor with a free tier; Claude Code goes deeper when the specification is ambiguous and the work genuinely hard; and Kimi Code offers an open-source coding agent you adopt whole, with no serving stack to build first.

The standing obligation is operational. The weights cost nothing to download, but the spend shifts to GPUs and the engineering time to serve them, and that commitment runs for as long as the deployment does. Treat the decision as buying infrastructure rather than software: capacity to plan, a serving stack to operate, and a team that owns it when it breaks.

Cost

Free tier + paid plans

Ease

Intermediate

Openness

Runs privately (self-hostable)

Data

Qwen's hosted API runs on Alibaba Cloud, which carries a jurisdiction question for some organisations. Self-hosting the open weights removes it: the model runs on your own infrastructure, so data governance stays entirely in your hands.

Llama (Meta)

Summary

Llama is Meta's open-weight model family. The Llama 4 weights remain downloadable and deployable, and they are widely supported across the tooling ecosystem: this was the name most tools integrated first.

Best for

Workloads where no vendor may touch the data
Keeping an established Llama deployment in service
Teams whose local tooling assumes Llama support
Serving internal assistants with no external API calls
Planning model spend as infrastructure, not subscriptions

Less suited to

Anyone starting new self-hosted coding work is better served by Qwen, the actively maintained Apache-2.0 line that has become the current default for exactly that job. And teams that simply want a capable assistant without owning any infrastructure should look at the hosted general tools instead: ChatGPT for breadth and a familiar starting point, Claude for long documents and careful prose.

The standing commitment is operational: serving, capacity, monitoring and upgrades are all yours to own. The category's headline benefit also only holds while you keep it, because routing the weights through a third-party host brings that provider's terms back into the picture.

Cost

Free

Ease

Advanced

Openness

Runs privately (self-hostable)

Data

Self-hosted weights keep data entirely on your infrastructure with no vendor in the path, which is the cleanest data posture in the category; running through a third-party host reintroduces that provider's terms, so check them as you would any processor.

03BY AREA

By area

Where each one pulls ahead, area by area.

Area	Pick Qwen when	Pick Llama (Meta) when
Software development	you are starting new self-hosted coding work and want the actively maintained line	Llama brings the broader tooling legacy and the reassurance of an estate that already works
Private, local & self-hosted	Qwen is the actively maintained line, while Meta has shipped no major new open-weight family since April 2025	an existing deployment and its wide tooling support outweigh having the freshest weights

04FAQ

Common questions

Is Llama a bad choice for self-hosting now?

No. Meta's Llama 4 open weights are still downloadable, deployable and widely supported across the tooling ecosystem, so an existing Llama deployment is not something to rush away from. The distinction is about new work: with no major new open-weight family release since April 2025, more actively maintained options now exist, and Qwen is one of them. Starting fresh, that maintenance momentum is the reason to default to Qwen rather than Llama.

Does self-hosting Qwen avoid the Alibaba Cloud question?

Yes. Qwen's hosted API runs on Alibaba Cloud, which carries a jurisdiction question for some organisations. Self-hosting the open weights removes that: the model runs on your own infrastructure, so the hosted-API jurisdiction no longer applies. Note that the current flagship Qwen 3.7 models are closed-weight and API-only; the open-weight line is the part you actually self-host, and it is what this recommendation refers to.

Can self-hosted open weights be my only model?

Treat them as your daily driver, not your only seat. On ambiguous specs and sustained multi-agent work the closed frontier models still win, so for teams whose constraint is cost or data sovereignty, the pattern is a self-hosted open-weight daily driver with a retained frontier seat for the harder jobs. That holds whichever family you pick. Qwen or Llama covers the routine coding volume you host yourself; you keep frontier access for the work that genuinely needs it.