Models & catalog
The best open-source AI models for European businesses in 2026
Published 2026-08-03 · 4 min read
Open-weight models have closed most of the gap with closed frontier labs in 2026, but "open-source" doesn't tell you which license you're under, how much VRAM you need, or whether you can run it on infrastructure that isn't subject to US jurisdiction. This is a working list of the models we actually route traffic to, what they're good at, and — where it applies — what it costs per million tokens on EU sovereign infrastructure.
If you need one takeaway: GLM-5.2 is the strongest general-purpose open-weight model available today and the one we'd default to for most business use cases; Qwen3.5 397B is the only model in this list running on two separate EU sovereign clouds at once, which matters if provider redundancy is a real requirement for you; and Mistral Small 3.2 is worth a specific look if the fact that the lab itself is European, not just the hosting, is part of your evaluation. Below is the full picture, model by model, with real pricing and licensing — not marketing claims.
GLM-5.2 — the strongest all-rounder
Built by Zhipu AI / Z.ai, GLM-5.2 is a 753-billion-parameter mixture-of-experts model released under the MIT license — about as permissive as open-source licensing gets, with no restrictions on commercial use or fine-tuning. It's widely regarded as the strongest fully open-weight generalist model available as of mid-2026, competitive with closed frontier models on long-horizon coding and reasoning benchmarks. On Frontière, it's served through Scaleway's Generative APIs at €1.80 per million input tokens and €5.50 per million output tokens — a premium price for a premium model, and the one we'd point most teams to first if budget isn't the binding constraint.
The Qwen family — a tier for every budget
Alibaba's Qwen family is the deepest open-weight lineup available, and Frontière's catalog spans five of its models across three price tiers:
- Qwen3.5 397B (403B total parameters, 17B active — a mixture-of-experts design) is the flagship: multimodal, Apache-2.0 licensed, and — unusually — available on both OVHcloud and Scaleway, the only model in our catalog with that redundancy. €0.60 / €3.60 per million tokens (input/output) on Scaleway.
- Qwen3 235B is the previous generation's flagship, still fully capable and noticeably cheaper: €0.75 / €2.25 per million tokens.
- Qwen3.6 27B is a newer, smaller multimodal model — Apache-2.0, and the one Qwen model in our catalog exclusive to OVHcloud rather than Scaleway.
- Qwen3 32B is a dense (non-MoE) mid-tier workhorse, well-priced and stable since its mid-2025 release.
- Qwen3 Coder 30B is code-specialized — the model to reach for if the workload is CI pipelines, code review, or developer tooling rather than general chat.
- Qwen3.5 9B is the budget tier: small, fast, and cheap enough to run at high volume for simple classification or lightweight chat.
All five run on OVHcloud AI Endpoints, priced in USD there and converted to EUR for billing.
Mistral Small 3.2 — the European option
Mistral AI is a Paris-based lab, which makes Mistral Small 3.2 (24B parameters, Apache-2.0) the one model in this list where the sovereignty argument extends to the model's origin, not just where it's hosted. It supports an unusually wide range of languages — 26 are listed on its model card, more than any other model in our catalog — which matters if your business genuinely operates across multiple European markets rather than English-first with translation layered on top.
gpt-oss 120B/20B — OpenAI's own open weights
OpenAI released two open-weight models, gpt-oss-120b and gpt-oss-20b, in August 2025 under Apache-2.0. They're not the newest models on this list, but they're inexpensive, well-documented, and carry brand recognition that matters in procurement conversations where "which company built this" is part of the internal sign-off. Both are available on OVHcloud, at €0.08 / €0.41 per million tokens for the 120B and roughly half that for the 20B.
DeepSeek V4 and Kimi K3 — the frontier tier
Two models push past what any EU sovereign managed cloud currently offers. DeepSeek V4 (MIT-licensed, 1M-token context) ships in two sizes: a 1.6-trillion-parameter Pro variant and a considerably smaller 158-billion-parameter Flash variant — reasonably self-hostable on a two-to-four-GPU server, unlike the Pro tier. Kimi K3, from Moonshot AI, is the largest open-weight model available anywhere at 2.8 trillion parameters, with native vision support and a 1M-token context window; its weights only became public in late July 2026.
Neither is on OVHcloud or Scaleway's managed catalogs as of writing — that's not unusual, since a managed sovereign cloud always trails the newest releases by design (certification takes time). On Frontière, both are listed as available through fast, non-sovereign access (Modal, a US company) while we evaluate dedicated EU servers for a sovereign variant — Kimi K3's size makes that a multi-node undertaking, while DeepSeek V4 Flash's smaller footprint makes it a realistic near-term candidate.
Licenses at a glance
| Model | License | Notably |
|---|---|---|
| GLM-5.2 | MIT | No restrictions on commercial use |
| Qwen3.5 397B / Qwen3 235B / Qwen3.6 27B / Qwen3 32B / Qwen3 Coder 30B / Qwen3.5 9B | Apache-2.0 | Permissive, patent grant included |
| Mistral Small 3.2 | Apache-2.0 | 26 languages documented |
| gpt-oss 120B / 20B | Apache-2.0 | OpenAI's first open-weight release since GPT-2 |
| DeepSeek V4 (Pro/Flash) | MIT | 1M-token context |
| Qwen2.5-VL 72B | Qwen's own license ("other") | Not Apache/MIT — check terms before commercial redistribution |
FAQ
FAQ
Which of these models should I use if I just want the best general-purpose option?
GLM-5.2 for capability, if budget allows it. Qwen3.5 397B if you specifically want a model available redundantly on two separate EU sovereign providers. Qwen3 32B if you want a solid, inexpensive default for everyday workloads.
Are DeepSeek V4 and Kimi K3 available today through Frontière?
They're in our catalog as fast-access, non-sovereign options while we evaluate dedicated EU infrastructure. They're not yet served through OVHcloud or Scaleway's managed sovereign clouds — no provider offers them there as of writing.
Why does the same model sometimes cost different amounts depending on the provider?
OVHcloud bills in USD, Scaleway in EUR, and each negotiates its own hosting economics — so the same open-weight model can carry different prices depending on which cloud serves it. Where a model is available on both (currently only Qwen3.5 397B), we route through the EUR-native provider to avoid introducing currency conversion into billing.
Is Apache-2.0 or MIT better for a commercial product?
Both are highly permissive and allow commercial use, modification, and redistribution with minimal obligations (attribution, typically). Apache-2.0 additionally includes an explicit patent grant, which some legal teams prefer for that reason. Neither imposes a copyleft requirement to open-source your own product.
What does 'mixture-of-experts' (MoE) mean for models like GLM-5.2 or Qwen3.5 397B?
It means the model has many more total parameters than it actually uses per token — Qwen3.5 397B has 403B total parameters but only activates about 17B per forward pass. That keeps inference cost and latency closer to a much smaller dense model while retaining the capacity of a much larger one.