AllocateThe best open-weight models in 2026
The strongest open-weight models on the catalog in 2026 are GLM 5.2 (Z.ai’s open flagship at $1.40 per million input tokens), Qwen 3.5 (397B MoE, Apache 2.0), Llama 4 Scout (a 1M-token window at $0.18), DeepSeek V3.1 (MIT at $0.60), and Kimi K2.5 (256K context at $0.50). All five are fine-tunable, and the weights you train belong to you.
Z.ai’s strongest open release: frontier-adjacent quality with 256K context, cached input at $0.26 per million tokens, and open weights you can train toward a model you own. The open default when quality leads the decision.
Apache 2.0 licensed, 397B parameters with 17B active, and strong quality across languages. If your users write in more than one language, this is the open model to beat, with a license that puts no strings on the result.
A 1M-token window at $0.18 per million input tokens: whole policy books, case histories, or codebases in one prompt, at the price most models charge for short context. The open choice for whole-corpus work.
MIT licensed at $0.60 per million input tokens and $1.70 out. Strong general capability with the most permissive license there is; fine-tune it, ship it, and owe nobody an explanation.
Moonshot’s open model with 256K context at $0.50 per million input tokens. A strong second option for long-document work when you want open weights at a lower list price than the flagships.
Provider list prices from the Allocate catalog, checked 2026-07-21.
How to choose between them
Start with the license. MIT (DeepSeek V3.1) and Apache 2.0 (Qwen 3.5) place no meaningful restrictions on commercial use or fine-tuning. Llama 4’s community license is workable for almost every company, but read it once before you build on it. GLM 5.2 and Kimi K2.5 publish weights without a listed license in the catalog; check the model card.
Then match the model to the shape of the work. Top-end open quality points to GLM 5.2. Multilingual traffic points to Qwen 3.5. Whole-document work points to Llama 4 Scout’s 1M window. High-volume routes on a budget point to DeepSeek V3.1.
The reason to choose open weights at all is ownership: fine-tune any of these on your own data and the resulting model is yours, not a dependency on someone else’s API. On Allocate the weights stay inside your isolation boundary and leave with you if you go.
Common questions
Are open-weight models as good as GPT-5.5 or Claude Sonnet 5?
On the hardest reasoning problems, frontier closed models still lead. On most production traffic, support, extraction, classification, and routine agent turns, the gap is small while the list-price gap is large: GPT-5.5 lists at $5 per million input tokens against $0.18 for Llama 4 Scout. Most teams route: open models carry the volume, frontier models take the hard steps.
Can I use these models commercially?
DeepSeek V3.1 is MIT and Qwen 3.5 is Apache 2.0, both permissive for commercial use and fine-tuning. Llama 4 uses Meta’s community license, workable for almost all companies. GLM 5.2 and Kimi K2.5 do not carry a listed license in the catalog; check the lab’s model card before you commit.
What hardware do these models need?
More than most single GPUs hold: the large MoE models need hundreds of gigabytes even at 4-bit quantization because every expert must stay resident. The GPU VRAM calculator shows exact requirements per model, and managed serving removes the question entirely.
Which open model is best for fine-tuning?
Qwen 3.5 if your data spans languages, a 70B-class Llama if you want the most documented training behavior, GLM if you want the strongest open starting point. Whichever base you pick, on Allocate the trained weights stay inside your boundary and belong to you.
What does open weight actually mean?
The trained parameters are published and you can run, modify, and fine-tune the model yourself. It is not the same as open source: the training data and code usually stay private. What matters commercially is the license on the weights.
Related
Every model here sits behind one key on Allocate: route by name, meter per route, and swap the model in one click.