Kimi ir melhor que Qwen-VI realmente me surpreenderia. Na verdade, o K2-VL não faria isso (afinal, é muito maior que o maior Qwen-VL), mas a equipe Qwen acumulou uma expertise incrível até agora, maldito 30B MoE compete com o Gemini. Desejo sorte a eles.

🚀 Olá, Kimi K2 Thinking! O modelo de agente de pensamento de código aberto está aqui. 🔹 SOTA em HLE (44,9%) e BrowseComp (60,2%) 🔹 Executa até 200 a 300 chamadas sequenciais de ferramentas sem interferência humana 🔹 Destaca-se em raciocínio, pesquisa agêntica e codificação 🔹 Janela de contexto de 256K Construído como um agente de pensamento, o K2 Thinking marca nossos esforços mais recentes em escalonamento de tempo de teste - dimensionando tokens de pensamento e turnos de chamada de ferramentas. O K2 Thinking agora está ativo no modo de bate-papo, com o modo de agente completo em breve. Também é acessível via API. 🔌 A API está ativa: 🔗 Blog de tecnologia: 🔗 Pesos e código:

Last week, our reasoning models took part in the 2025 International Collegiate Programming Contest (ICPC), the world’s premier university-level programming competition. Our system solved all 12 out of 12 problems, a performance that would have placed first in the world (the best human team solved 11 problems). This milestone rounds off an intense 2 months of competition performances by our models: - A second place finish in AtCoder Heuristics World Finals - Gold medal at the International Mathematical Olympiad - Gold medal at the International Olympiad in Informatics - And now, a gold medal, first place finish at the ICPC World Finals. I believe these results, coming from a family of general reasoning models rooted in our main research program, are perhaps the clearest benchmark of progress this year. These competitions are great self-contained, time-boxed tests for the ability to discover new ideas. Even before our models were proficient at simple arithmetic, we looked towards these contests as milestones of progress towards transformative artificial intelligence. Our models now rank among the top humans in these domains, when posed with well-specified questions and restricted to ~5 hours. The challenge now is moving to more open-ended problems, and much longer time horizons. This level of reasoning ability, applied over months and years to problems that really matter, is what we’re after - automating scientific discovery. This rapid progress also underscores the importance of safety & alignment research. We still need more understanding of the alignment properties of long-running reasoning models; in particular, I recommend reviewing the fascinating findings from the study of scheming in reasoning models that we released today ( Congratulations to my teammates that poured their hearts into getting these competition results, and to everyone contributing to the underlying fundamental research that enables them!