On July 13, 2026, a German research consortium published the Soofi S technical report and began the release phase for a 31.6B-parameter language model intended for a future permissive open release. The checkpoints currently listed on Hugging Face are gated closed-beta research previews, not a general open release. In the consortium's own evaluation, Soofi S leads the fully open baselines it tested on German and English aggregates. The Soofi project is coordinated by the KI Bundesverband with Fraunhofer institutes, DFKI, universities, and companies including Ellamind and Merantix Momentum. The German Federal Ministry for Economic Affairs and Energy funded the project with roughly 20 million euros. This post looks at what is available, where the model is strong, where it is not, and what the preview could mean for production agents.
What is available today
Public materials include the technical report, training and evaluation code, and detailed data accounting. Hugging Face lists gated closed-beta checkpoints for the base model (Soofi-S-30B-A3B on Hugging Face), an instruction-tuned preview, and two reasoning previews named Isar and Rhine. Access is limited to selected beta partners while the consortium develops the final version. Training ran from late March to mid May 2026 on up to 512 Nvidia B200 GPUs in Munich, about 253,000 GPU-hours on Deutsche Telekom infrastructure, over roughly 27 trillion tokens. German content was deliberately overweighted: 7.2% of the main pretraining mix and 15.3% of the later high-quality annealing phase, compared with the roughly 5% that common training recipes reserve for all non-English data combined.
The architecture is the interesting part
Soofi S is not chasing frontier scores. It is designed to be cheap to serve. The model is a hybrid: of its 52 layers, only 6 are classic attention layers that keep a KV cache, while the rest pair Mamba-2 state-space layers with mixture-of-experts blocks. The practical consequence is that throughput barely degrades as the context grows. The consortium measured nearly constant decode speed from 4,000 out to 256,000 tokens of context: around 4,800 tokens per second per GPU at a 40,000-token context with 32 parallel requests, several times what dense models in the 14-24B range manage under the same load.
For agent workloads this is the headline feature. Agents accumulate context: system prompts, tool schemas, tool results, retrieved documents, conversation history. A model whose serving cost stays flat as that context grows is built for exactly this shape of traffic. The consortium says as much: the stated focus for the next project phase is technical documents, code generation, and agent-based systems.
The benchmarks, honestly
In the consortium's comparison, the Soofi S checkpoint scores highest among the fully open baselines tested on both German and English aggregates, ahead of OLMo 3 32B and Apertus 70B. That benchmark category describes the comparison set; it does not make the current gated Soofi checkpoint a fully open release. Its German results are the standout: an aggregate of 79.1 and the best German code aggregate in that evaluation. The technical report is unusually transparent about the rest.
Selected scores from the Soofi S technical report, pass@1 for code benchmarks.
The caveats matter just as much. Against open-weight but not fully open models, Soofi S loses clearly: Qwen3.5 scores 74.6 on the English aggregate to Soofi's 70.1, with similar gaps on abstract reasoning benchmarks like GPQA. Competition-style math in German is weak at 56 points on Minerva MATH-DE, and open-domain factual recall runs into the limits of ~3.2B active parameters. And while long-context throughput is excellent, one RULER word-extraction subtask degrades past 32,000 tokens and drops below 5% accuracy at the longest lengths, so the 1M-token context is best treated as a throughput property, not a guarantee of reliable recall across it.
Critics have also noted that the architecture overlaps heavily with Nvidia's Nemotron 3 Nano family and argue that continual pretraining on an existing base would have cost far less than the 253,000 GPU-hours spent. That is a fair critique of the economics. It does not change the central result: a German-strong model with unusually detailed documentation, public training and evaluation code, and a largely reconstructable data recipe. Access to the current checkpoints and final licensing remain separate questions.
Open ambition, gated access today
The Soofi project targets a permissive open release, but the current preview does not offer permissionless public access to its checkpoints. The model card labels access as closed beta and says the final model will be released openly without gating. About 99% of the training mixture is publicly obtainable and can be reconstructed where source licenses permit; the remainder includes commercially licensed German newspaper archive content that cannot be redistributed. The training and evaluation code and detailed data accounting are public, but the current model weights and final license terms are not generally available.
Running Soofi S behind an agent
Connic keeps BYOK and custom OpenAI-compatible endpoints as first-class options alongside its managed model catalog. If you have approved beta access, serve the instruction-tuned preview with vLLM or a compatible GGUF stack such as llama.cpp or Ollama, then register the endpoint as a custom OpenAI-compatible provider in your project settings, and reference it from the agent config:
model: vllm/soofi-s-instruct uses a Connic provider alias; it is not an official Soofi model ID. If the inference box lives inside your private network, agents reach it through the bridge instead of the public internet. Guardrails, judges, traces, and approvals work the same regardless of which model answers.The honest caveats carry through. The base model is explicitly not meant for direct use without post-training, the instruct and reasoning variants are gated previews, and we found no public commercial SLA or support offering. Beta partners may have different arrangements, but teams evaluating a self-hosted preview should plan to own patches, security updates, and incident response. That is why we would put Soofi S on a slice of real traffic behind an A/B test, with judges scoring its output, before routing anything important to it. Our DACH report found 23% of production agents in the region already run open-weight or self-hosted models, so the operational pattern is well established; what has been missing is a German-first model worth the effort.
Connect a self-hosted Soofi S endpoint as a custom provider and put guardrails, judges, and traces around it, alongside whichever commercial models you already use.
Get started freeThe sovereignty question
Soofi S will be discussed mostly in sovereignty terms, and it is worth being precise about what it does and does not settle. It does not make the strongest closed or open-weight models redundant; the benchmark gaps are real. What it provides today is evidence that a German-strong sovereign model with efficient long-context serving is technically plausible, plus a detailed report and public training and evaluation code. Approved beta testers can run preview checkpoints entirely on infrastructure they control; general access, final terms, and production maturity remain outstanding. For teams whose deployments hinge on EU AI Act readiness and keeping data inside the EU, that combination is the point, independent of leaderboard positions.
Three things to watch from here: the general ungated release and final license, the instruct model graduating from preview, and the consortium's next phase, which targets agent systems directly and is looking for industry partners. If those land, the default answer to "which model do we self-host for German workloads" could change.