The Heidelberg company's Mixture-of-Experts model supports a 1 million token context window and targets regulated sectors in English and German
Aleph Alpha has released Kolibri, a 78.1-billion-parameter AI model with open weights and a context window of up to 1,048,576 tokens.
The Heidelberg-based company built it in Europe, trained it with a heavy emphasis on German content, and pitched it at sectors where compliance matters most: public administration, industry, aerospace, and defense.
The launch came on October 3, 2026.
What Kolibri actually is #
Kolibri is a Mixture-of-Experts transformer, usually shortened to MoE. Kolibri has 78.1 billion total parameters, but only approximately 3.46 billion are active per token.
The headline feature is the context window. Kolibri is designed to work across up to 1,048,576 tokens, commonly rounded to 1 million.
The model is tuned for English and German. Its training run covered 20 to 24 trillion tokens, with German content given particular weight.
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The hardware, the license, and the fine print #
Aleph Alpha trained Kolibri on infrastructure located in Germany and Finland, using 768 NVIDIA B200 GPUs.
Kolibri ships under the Apache 2.0 license and is available on Hugging Face.
Kolibri requires roughly 78 GB of storage in FP8 format.
Aleph Alpha also published a 189-page technical report covering Kolibri’s architecture, training methods, and evaluation results.
On performance, Kolibri scored 96.9% on AIME 2025, a math competition benchmark, according to Aleph Alpha’s own reporting.
Disclosure: This article was edited by Diego Almada Lopez. For more information on how we create and review content, see our