Aleph Alpha releases Kolibri-1 with 1M-token context and tool calling

Kolibri-1 was trained in three phases: pre-training on 16,384-token sequences, mid-training on 65,536-token sequences, and a final long-context phase on 262,144 tokens. The model’s training consumed an estimated 9.5×10² MWh (950 MWh) across all phases, excluding fine-tuning and RL stages. It is designed for integration into conversational assistants, agentic workflows, and document-processing systems where human review is required. Aleph Alpha emphasizes its use for advisory roles, not autonomous decision-making, and includes safeguards against harmful outputs aligned with the EU AI Act. The model weights are released under the Apache 2.0 license, though Aleph Alpha retains rights to architecture, training methods, and intellectual property.