Open source AI models compared in 2026 mostly means: Qwen, Llama, Mistral, Gemma, and DeepSeek side by side, each with its own license, context length, and tool calling ability. There is no single best model, only the model that fits your task, your hardware, and the language you work in. Some call this field open source AI models, others open source LLM, it means the same thing: language models whose weights you can download and run on your own hardware.
I run my business with an AI workforce of eight AI employees built on Claude, that's my production environment. Alongside that I test how far open models get on my own hardware, currently a Nvidia DGX Spark. Everything I write about local AI is collected on the AI page.
Why there's no single best model
Asking for the best open source model is usually the wrong question. A model that runs well on a 24 GB GPU may be too small for a 128 GB machine. A model with strong tool calling for agents isn't automatically the model with the best German text quality. And a license that's fine for a side project can look different once you use it for paid client work.
That's why I don't rank models on a single list, I sort them by the criteria that actually matter for my work. For the five big families, Qwen, Llama, Mistral, Gemma, and DeepSeek, I name below what is backed by a source, and just as honestly, where the sources go quiet.
My selection criteria for a local model
Tool calling comes first, because it decides whether a model is even a candidate for agent work. Without reliable tool calling, a model can't read files, run commands, or hand intermediate results back to a system, that's the baseline requirement for a terminal agent like my Pi setup.
Context length decides how much a model can take in at once, an entire repository or a long client document, for instance. A small context window forces more intermediate steps and more manual steering on your part.
German quality I check with the same test prompts for every model I'm seriously considering, because benchmark numbers are almost always measured in English. I haven't run that test on the five families below yet, and nobody else has published one either: a German comparison site claims to have run its own tests but shows no results. So I'm not going to hand out German-quality grades I haven't verified myself.
License decides whether you're even allowed to use a model for client work. Apache 2.0 and MIT are comparatively unproblematic, other licenses restrict commercial use or cap the number of users.
Hardware requirements decide what actually runs in practice. A 27-billion-parameter model at Q4 quantization needs a different machine than a 1-billion-parameter model meant for a laptop without a GPU.
Open source AI models compared: what's confirmed and what isn't
For each model family, the table names only what is backed by a source. As of September 2026. This overview goes stale fast, new versions and new license terms show up in this field frequently, always check the current model card before you decide.
| Model family |
Sourced version |
Size |
License |
Context |
| Qwen 3.8 |
27B dense (one source says 28B, unresolved) |
27 billion parameters, NVFP4 checkpoint at 21.9 GB in my own setup |
Apache 2.0 (official model card) |
262,144 tokens natively, up to 1 million via YaRN |
| Llama |
3, 3.1, 3.2 mentioned |
no sourced parameter count |
no sourced figure |
no sourced figure |
| Mistral |
7B mentioned |
7 billion parameters (two sources) |
no sourced figure |
no sourced figure |
| Gemma |
example gemma3:1b |
1 billion parameters (smallest example named) |
no sourced figure |
no sourced figure |
| DeepSeek |
V4 Flash, mentioned only as a cloud API |
no local size confirmed |
no sourced figure |
no sourced figure |
Qwen 3.8 has the most solid sourcing: the official model card states Apache 2.0 as the license. The parameter count for the 27B variant isn't fully consistent either, most sources write 27 billion, one writes 28 billion, and none of them resolve the discrepancy. For Llama, Mistral, Gemma, and DeepSeek, all I find are version names or a single example model, not solid figures on license or context length. I'd rather name that gap than invent a number I haven't checked.
Apache 2.0 and friends: what the license means for you
Apache 2.0 allows commercial use, modification, and redistribution without requiring you to disclose your changes, you can read the full text at the Apache Software Foundation. For Qwen 3.8, that is the license on the official model card. Other open model families run under their own license terms, which can restrict user counts, require attribution, or limit specific use cases. Before you deploy a model for client work, it's worth checking the current model card on the relevant project page, for example on Hugging Face, license terms sometimes change with new versions.
My practical example: Qwen 3.8 on the DGX Spark
Of the five model families in this post, I currently run only one in daily use: Qwen3.8-27B as an NVFP4 checkpoint under SGLang on a Nvidia DGX Spark with 128 GB of unified memory, roughly 119 GB of it usable. Measured on my machine: 50.7 tokens per second decoding and roughly 1,500 tokens per second on prefill, with a terminal agent called Pi working against it.
I chose this combination because Qwen 3.8's model card lists tool calling, thinking, and vision, its 262k native context gives enough room for longer tasks, and it's licensed under Apache 2.0. I haven't tested Llama, Mistral, Gemma, or DeepSeek in my own setup, so I can't report firsthand experience with them, only the sparse figures that have been published. For the details of the setup and the pitfalls I ran into, see Qwen 3.8 Locally: DGX Spark Experience.
Frequently asked questions
What's the best open source model?
There's no verified answer that holds for every task. For agent work with tool calling and a long context, Qwen 3.8 is a solid choice based on the sources and my own experience, for other criteria like German quality I don't have solid comparison data for the remaining model families.
What does open weights mean, as opposed to open source?
Open weights means a model's trained weights are available for download. Open source in the strict sense would also disclose the training data and training code, few of the models named here do that in full, most of what's public is just the weights.
Can freelancers use Llama or Mistral locally for client work?
That depends on the license that actually applies, and for Llama and Mistral I found nothing solid enough to state. For Qwen 3.8, the official model card states Apache 2.0, which explicitly permits commercial use. Before you deploy any of these for client projects, always check the current license file on the relevant project page.
Which models run without dedicated server hardware, for example Llama locally on a laptop?
Smaller variants across several families are built for laptops, for instance the example model gemma3:1b or Mistral 7B at Q4 quantization, as the common guides describe it. I don't have measurements of my own for those, I currently run only Qwen 3.8, and on dedicated hardware at that.
Is Apache 2.0 usable commercially?
Yes, Apache 2.0 explicitly permits commercial use, modification, and redistribution, disclosing your own changes isn't required. Details are in the official license text.
Where you go from here
If you want to get started yourself, the practical part doesn't begin with picking a model, it begins with setup: how to get Ollama running in a few minutes and when the step up to SGLang is worth it is in Setting Up Local AI: Ollama to SGLang. For a broader look at local AI beyond individual model families, see Local AI: What Actually Works in 2026.
My suggestion: don't commit to a model before you know your own criteria. Tool calling, context length, German quality, license, and hardware requirements are the five questions I settle before every install, and more often than not, at least one of them already makes the decision for me.
