Local models in 2026: Llama, Qwen and Gemma compared fairly
On-prem AI needs an open model that fits your own hardware. An honest snapshot as of mid-2026: licensing, sizes and context of Llama 4, Qwen 3.5/3.6 and Gemma 4 — and why "which one is best" is the wrong question.
Short answer: there's no overall winner, only the best choice for your situation. Qwen and Gemma are lighter to work with thanks to a genuine Apache 2.0 licence with no usage restrictions; Llama has the largest ecosystem adoption but a licence with conditions that only become relevant at extreme scale. Which model actually wins depends on your hardware, the languages you need, and how much context a task requires — and that shifts every few months given how fast this field moves.
This comparison was updated on 25 July 2026. We flag that deliberately: no "best model of 2026" overview stays current for longer than a quarter at this pace. Before committing to a concrete choice, always check the most recent releases.
The licence matters more than the benchmark
For an on-prem decision, the licence is often more important than half a percentage point on a benchmark. Qwen (Alibaba) and Gemma (Google DeepMind) ship under Apache 2.0 — a genuine open-source licence with no usage cap, no mandatory attribution in your product, and full commercial freedom. Llama (Meta) uses its own "Community License": free to use, modify and redistribute, with an acceptable-use policy, a mandatory "Built with Llama" notice, and a threshold of 700 million monthly active users above which you need separate permission. For virtually every SME, that threshold is theoretical — but it isn't an OSI-recognised open-source licence, and that makes a legal check worthwhile for sensitive applications.
Which model fits which hardware
For on-prem, size matters just as much as quality: a model that doesn't fit your hardware is useless no matter how good the benchmark looks.
Gemma 4 explicitly targets that spectrum: four sizes (E2B, E4B, 26B and 31B), from a phone or laptop to a single GPU. The smaller variants are built to run where there's no server rack; the 26B MoE variant reaches a score competitive with models twenty times its size using only 3.8 billion active parameters.
Qwen offers the widest spread: the 3.5 family runs from 0.8 to 397 billion parameters, with a dense 27B and an MoE variant (35B-A3B) specifically suited to single-GPU deployment. Anyone needing something smaller than Gemma's smallest variant is more likely to find it at Qwen than at Llama.
Llama 4's newest generation (Scout and Maverick) aims at the top end: Scout, the smaller of the two, still just fits on a single high-end GPU, with a 10 million token context window — impressive, but not a lightweight option for modest hardware. Anyone looking for a small, efficient model is more likely to find it in older, still widely used Llama generations than in the newest release.
Context, language and specialisation
Beyond size, the three families differ in what they bring along. Qwen 3.5 natively supports 201 languages and a 262,000-token context window — relevant if you work with multilingual or very long documents. Qwen 3.6 shifts some focus toward code generation. Gemma 4 ships with vision processing across all variants and function calling for agent work, making it a logical candidate for local agent applications. Llama 4 was the first of the three with a natively multimodal, mixture-of-experts architecture and remains the family with the broadest ecosystem adoption — more ready-made tooling, more existing integrations.
"Which model is best" is the wrong question. The right question is: which model fits my hardware, under a licence I can use without a legal question mark, with the language and context my task requires?
Why this overview has a shelf life
While this article was being written, Alibaba announced a preview of an even larger Qwen model — its weights haven't been released yet. That's not an exception but the norm in this field: a new version ships almost monthly, and the pecking order on benchmarks shifts just as often. Any overview claiming a "definitive winner" is, by definition, already outdated by the time you read it. So don't base your choice on today's leaderboard, but on the fixed criteria that don't change every month: licence, hardware fit, and whether the model is actively maintained.
How this fits an on-prem decision
Whichever model you choose, the question of whether to run locally at all is separate from which model wins. We cover that trade-off — when on-prem is worth the investment — in on-prem AI: when is it worth the investment?. And because "your own model" is often confused with "just pick a bigger language model", we explain in training your own model? Start with RAG why model choice is rarely the first problem you need to solve.
Conclusion
Qwen and Gemma win on licence freedom and the availability of small, efficient variants; Llama wins on ecosystem adoption and tooling, with a licence that's a non-issue in practice for most SMEs but still worth a glance. None of the three is objectively "the best" — the right choice follows from your hardware, your language and context requirements, and how much legal certainty you need. And because this field moves faster than most overviews can track: verify the current state before you commit, not after.