Kolibri-1

Model overview

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. 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.

Key features

特点摘要待依据来源补齐，不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

Source prices & cost analysis

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

Frequently asked questions

Sources & traceability

许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。