inclusionAI: Ring-2.6-1T
inclusionai/ring-2.6-1tRing-2.6-1T is a 1T-parameter-scale thinking model with 63B active parameters, built for real-world agent workflows that require both strong capability and operational efficiency. It is optimized for coding agents, tool use, and long-horizon task execution, delivering leading results on benchmarks including PinchBench, ClawEval, TAU2-Bench, and GAIA2-search.
With adaptive reasoning effort across high and xhigh modes, Ring-2.6-1T dynamically allocates reasoning budget based on task complexity. This enables stronger performance with lower token overhead, especially in tool-heavy and multi-turn agent workflows.
Ring-2.6-1T is designed for advanced coding agents, complex reasoning pipelines, and large-scale autonomous systems where execution quality, latency, and cost efficiency all matter.
Провайдер для inclusionAI: Ring-2.6-1T
Hubris маршрутизирует запросы к лучшему доступному провайдеру с автоматическим fallback при сбоях.
Модальности
Поддерживаемые параметры
Другие модели от inclusionai
inclusionAI: Ling-2.6-1T
Ling-2.6-1T is an instant (instruct) model from inclusionAI and the company’s trillion-parameter flagship, designed for real-world agents that require fast execution and high efficiency at scale. It uses a “fast thinking” approach to reduce costs to roughly a quarter of comparable models while maintaining top-tier performance. The model achieves state-of-the-art results on benchmarks such as AIME26 and SWE-bench Verified, and is well suited for advanced coding, complex reasoning, and large-scale agent workflows where both capability and efficiency are critical.
inclusionAI: Ling-2.6-flash
Ling-2.6-flash — это мгновенная (инструктивная) модель от inclusionAI с общим количеством параметров 104B и 7.4B активных параметров, разработанная для реальных агентов, которым требуются быстрые ответы, высокая производительность и эффективность использования токенов. Она обеспечивает производительность, сравнимую с передовыми моделями аналогичного масштаба, при значительном сокращении использования токенов в рабочих процессах кодирования, обработки документов и легковесных агентов.