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Aleph Alpha Releases Kolibri, a 78-Billion-Parameter Apache 2.0 German-English MoE Model With 3.46 Billion Active Parameters

Aleph Alpha published Kolibri on Hugging Face on October 3 under Apache 2.0; its own tech report puts the model on the quality-versus-serving-cost Pareto frontier.

AI & Machine Learning Models
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Overview

Aleph Alpha released Kolibri, an open-weight mixture-of-experts reasoning model focused on German and English, with a release date of 3rd of October 2026, according to its Hugging Face model card. The weights are licensed under Apache 2.0. The company’s technical report describes Kolibri as a 78.1-billion-parameter model of which 3.46 billion parameters, or 4.4 %, are active per token.

All benchmark figures below come from Aleph Alpha’s own evaluations. No independent replication had been located at the time of writing.

What We Know

Architecture and serving

  • The model card lists 78,103,074,560 total parameters and 3,457,573,120 active parameters per token, with a context length of 1,048,576 tokens. It recommends contexts of at most 262,144 tokens for serving efficiency and complex tasks.
  • The card gives a model memory footprint of about 78 GB for the FP8 weights, with a minimum hardware requirement of 2x A100 80 GB, 2x H100 SXM5, 1x H200, 1x B200 or 1x B300.
  • The model supports an explicit reasoning mode. According to the model card, users can pass reasoning_effort values of low, medium and high, or disable thinking by setting the value to none.
  • The knowledge cutoff for both English and German is June 18, 2026, per the model card.

Training

  • The model card says pre-training used 20T tokens, roughly 62.5% English, 23.9% German and 13.6% code, plus 3.44T tokens of mid-training and 201B tokens for long-context extension.
  • Pre-training ran on 768 NVIDIA B200 GPUs for 21 days, a total of 392k GPU-hours, with a total of 6.4e23 FLOPS, according to the model card. The card estimates energy use at 9.5×10² MWh.
  • The technical report states that training spans 24T tokens across all stages, that teams in Germany developed the model end to end, and that training ran on infrastructure in Germany and Finland.
  • Post-training combined supervised fine-tuning with reinforcement learning on more than 1.2M internally curated tasks covering reasoning, tool use, instruction following, code and retrieval, per the technical report.

Reported results

The technical report says that among the open models Aleph Alpha evaluated, Kolibri lies on the Pareto frontier of quality and serving cost in English and German, both as a base model and as a post-trained model. Its post-training tables list an overall score of 75.5 in English and 70.8 in German, 96.0 on AIME 2026, 84.3 on GPQA Diamond and 66.4 on SWE-Bench Verified.

The report also compares the released checkpoint with the supervised fine-tuning checkpoint from which reinforcement learning starts. AIME 2026 rose from 92.1 to 96.0 and GPQA Diamond from 79.8 to 84.3. The report additionally says it trains the model to abstain when the provided context does not support an answer, and lists an AA-Omniscience non-hallucination score of 44.0 for Kolibri against 15.0 for its internal predecessor, Kolibri Origin.

Intended use

The model card says Kolibri is intended for conversational assistants and agentic workflows in which a person reviews the model’s output before it is acted on, rather than for autonomous systems that act unreviewed. It also states that Aleph Alpha is a signatory of the EU GPAI Code of Practice.

What We Don’t Know

  • Whether independent evaluators will reproduce the reported scores. The figures above are drawn from the developer’s own report and model card.
  • How Kolibri performs on workloads outside the benchmarks Aleph Alpha selected, and how the model behaves in production deployments.
  • The scale of adoption: the sources cited here do not report download counts or named deployments.

Analysis

The release pairs a small active-parameter fraction, 4.4 %, with a full memory footprint of roughly 78 GB, a trade-off the model card acknowledges, noting that the full model must be held in memory even though only part of it is active at any time. The card also describes supporting two languages rather than many as a deliberate choice of depth over breadth. The technical report says the model is built for organisations in public administration, industry and aerospace that process sensitive data under regulation. Whether the reported position on the quality-cost frontier holds up will depend on third-party testing.