Ant Group has just released Ling 2.6 1T， an open weights， non-reasoning model with high cost efficiency and a reasonable intelligence tradeoff. Ling 2.6 1T scores 34 on the Artificial Analysis Intelligence Index， a 15-point jump from Ling-1T

Ling 2.6 1T is the latest model from Ant Group's @TheInclusionAI lab. Ant Group recently released Ling 2.6 Flash， a 104B total parameter non-reasoning model. Ling 2.6 1T's weights have been publicly released on Hugging Face.

Key takeaways：

➤ Comparable intelligence to similarly sized non-reasoning models： At 1T total parameters， Ling 2.6 1T sits near DeepSeek V3.2 （non-reasoning， 32） and Kimi K2.5 （non-reasoning， 37） in intelligence. This is a marked improvement from Ling-1T， which scores 19 on the Intelligence Index. However， there remains a ~10-point gap to frontier non-reasoning open weights models such as GLM-5.1 （non-reasoning， 44） and Kimi K2.6 （non-reasoning， 43）.

➤ Strong performance in scientific reasoning and knowledge： Ling 2.6 1T scores 75% on GPQA and 8% on Humanity's Last Exam （HLE）， indicating solid performance on graduate-level reasoning and knowledge recall tasks. This is comparable to DeepSeek V3.2 （non-reasoning）， which achieves 75% on GPQA and 11% on HLE.

➤ Efficient token usage： Ling 2.6 1T uses ~16M output tokens to run the Artificial Analysis Intelligence Index， making it more efficient than MiMo V2 Flash （non-reasoning， ~17M）， and significantly more efficient than GLM-5.1 （non-reasoning， ~75M） and Kimi K2.6 （non-reasoning， ~27M）

➤ Strong cost-to-intelligence positioning： At $0.30 per million input tokens and $2.50 per million output tokens on InclusionAI's first-party API， Ling 2.6 1T costs only ~$95 to run the full Artificial Analysis Intelligence Index. This positions it competitively for large-scale workloads relative to models in a similar intelligence tier.