Hub AI research
Artificial Intelligence is already part of everyday life. Since the ImageNet moment in 2012, when Deep Learning proved its great utility in combination with a lot of computing power and gigantic amounts of data, only about ten years have passed – yet there has been more progress than in half a century before.
Still, many questions remain: Will Deep Learning remain the gold standard of AI development? What are the performance limits of large AI systems? Do we need symbolic systems? What technology will lead to artificial general intelligence? Finally, what actually is intelligence, anyway?
We explore these topics and more in our Artificial Intelligence Research Content Hub.
Open-weight reasoning models often use far more tokens than closed models, making them less efficient per query, according to Nous Research. Models like DeepSeek and Qwen use 1.5 to 4 times more tokens than OpenAI and Grok-4—and up to 10 times more for simple knowledge tasks. Mistral's Magistral models stand out for especially high token use. 
Average tokens used per task by different AI models. | Image: Nous ResearchIn contrast, OpenAI's gpt-oss-120b, with very short reasoning paths, shows that open models can be efficient, especially for math problems. Token usage depends heavily on the type of task. Full details and charts are available at Nous Research.
