Llama is the default answer to “we need to run this ourselves” — not because it always wins on capability, but because it has the deepest tooling ecosystem of any open-weight family, and on a self-hosted deployment the tooling is most of the work.
All Model Families →
Provider-published specifications as of 1 September 2026, not figures we measured. Model families move quickly — check the provider's own documentation before committing to a number.
On a hosted API the model is the product. On a self-hosted deployment the model is one component of a system that also has to do batching, KV caching, quantisation, tensor parallelism, health checks, GPU scheduling, upgrades and rollback. Llama's advantage is that every one of those has a well-trodden path, several implementations and a large body of people who have hit the failure mode before you.
That matters more than a benchmark point. A model that scores marginally higher but has one immature serving implementation will cost you more in engineering time than the difference is worth, and it will cost you more again the first time you need to debug throughput at two in the morning. When we recommend against Llama it is usually because a specific capability requirement points elsewhere, not because the deployment story is better anywhere else.
The licence deserves ten minutes. It permits commercial use and it is not restrictive for the overwhelming majority of deployments, but it is a bespoke licence rather than a standard one, it carries an attribution obligation, and it has a scale threshold. If you are building a product on top of it, have someone read it who is qualified to. If a permissive standard licence is a hard requirement — and for some procurement processes it is — Mistral publishes Apache-2.0 options.
Where a standard permissive licence is a procurement requirement. Llama's community licence permits commercial use but is bespoke, carries attribution obligations and has a scale threshold. Mistral publishes Apache-2.0 options and typically clears a standard open-source policy far faster.
Where reasoning-heavy work dominates and inference economics matter, DeepSeek's mixture-of-experts design frequently wins on throughput per unit of compute. Where the deployment has to span an edge device and a server with one tokeniser, Qwen's size ladder is the better fit.
And where nobody wants to operate inference infrastructure at all, a hosted API is cheaper once you price the people honestly. Self-hosting is a standing commitment, and we would rather say that in the first meeting than in the retrospective.
Not by the OSI definition, and the distinction is worth keeping straight because procurement sometimes cares. The weights are published and commercial use is permitted, which is what most people mean when they say open. But the licence carries conditions a standard open-source licence does not, including attribution and a user-count threshold. “Open weights” is the accurate term.
Dominated by GPU capacity and the people who look after it, not by anything per-token. That flips the economics against hosted APIs only above a certain sustained volume — below it, you are paying for idle hardware. We work the crossover out explicitly rather than assuming it; see GPU sizing and cost.
DeepSeek tends to lead on reasoning-heavy work and has favourable inference economics from its mixture-of-experts design. Llama has the deeper tooling ecosystem and a US-domiciled provider, which some organisations' governance policies prefer. Both are credible; we benchmark both when the decision is open.
Serving stack, GPU sizing, evaluation and upgrade path — the parts that decide whether it stays up.
30-minute technical call · no deck
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