Announcing Ling-2.6-1T by inclusionAI, now available on OpenRouter. 🚀
This trillion-parameter flagship instruct model is built for real-world agents. It utilizes a “fast thinking” approach to cut costs by ~75% while maintaining SOTA performance on AIME26 and SWE-bench Verified. Ideal for:
- Advanced coding
- Complex reasoning
- Large-scale agent workflows
Enjoy the high token efficiency and start saving today~🤠💰
https://t.co/Oik861ozDs
token — The chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.
SWE-bench — The standard benchmark for AI coding agents: real GitHub issues from real repositories, scored by whether the agent's patch passes the project's own tests.
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
chain-of-thought — Having a model write out intermediate reasoning steps before its answer, which markedly improves performance on hard problems.
Why it matters
Ling-2.6-1T is a trillion-parameter instruct model reachable through OpenRouter that answers without long chain-of-thoughtHaving a model write out intermediate reasoning steps before its answer, which markedly improves performance on hard problems.Full definition →, so coding and AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → workloads can be run at a fraction of the usual tokenThe chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.Full definition → spend.
