Kolibri

Kolibri is Aleph Alpha's mixture-of-experts (MoE) reasoning model, with a focus on German and English. The model supports an explicit reasoning mode and tool calling. It is optimized for long-context and inference efficiency.

Intended use

Kolibri is intended to process text input and output in German and English and to perform a wide range of tasks beyond natural-language generation, for example multi-step reasoning, coding, structured extraction, retrieval-augmented generation, long-document processing and agentic tool calling.

Kolibri was pre-trained on sequences of 16,384 tokens, mid-trained on 65,536 and trained on 262,144 tokens in a final long-context phase, which is its native context length. Because positional encoding is applied only in the sliding-window layers, the context can be extended beyond that length without any position scaling, in principle to arbitrary lengths. We have validated quality and serving efficiency up to 1,048,576 tokens. For latency- or throughput-sensitive deployments and for complex tasks, we recommend contexts of at most 262,144 tokens. See the technical report for details.

For the kinds of systems Kolibri is meant to be integrated into, see AI system types below; for uses that we encourage users to refrain from, see Responsible Use.

Design goals

Kolibri is designed to deliver strong German and English performance at low serving cost. Its mixture-of-experts architecture activates only a small fraction of its parameters for each token, keeping compute per token low while retaining the capacity of a much larger model. The trade-off is memory: the full model must be held in memory even though only part of it is active at any time. To keep long contexts affordable, most attention layers focus on nearby text, while a smaller number attend across the whole context. We also developed a tokenizer tailored to German word structure, so German text is processed efficiently without sacrificing English. Supporting two languages rather than many is a deliberate choice of depth over breadth.

AI system types

Kolibri is intended for integration into conversational assistants and agentic workflows in German and English, in which a person reviews the model's output before it is acted on rather than autonomous systems that act unreviewed. It suits document-processing and drafting systems, question-answering systems over an organisation's own material, and internal knowledge and research tools. Its tool-calling and structured-output capabilities make it appropriate for orchestration layers that call APIs, execute code or run searches, provided the calling system validates the results. In decision-support systems it belongs on the advisory side, surfacing evidence and drafting options for a human to weigh, and it is not intended as the deciding component. More broadly, it is built for human-AI collaboration rather than unsupervised operation.