Today we're shipping Nemotron 3 Ultra. A 550B MoE frontier-intelligence open model built for long-running agents. It delivers 5x faster inference and lowers the cost of complex agentic tasks by up to 30% versus other open frontier models.

Today, we are releasing the best open-weight LLM by a US company: Cogito v2.1 671B. On most industry benchmarks and our internal evals, the model performs competitively with frontier closed and open models, while being ahead of any US open model (such as the best versions of OpenAI’s GPT-OSS, Nvidia’s Nemotron and Meta’s Llama). We also built an interface where you can try the model (it’s free and we don’t store any chats): chat.deepcogito.com Additionally, you can download the model on @huggingface, or try it out on @openrouter, @togethercompute, @FireworksAI_HQ , @ollama cloud, @runpod, @baseten, or run it locally using @ollama or @UnslothAI. This model uses significantly fewer tokens amongst any similar capability models, because it has better reasoning capabilities. You will also notice improvements across instruction following, coding, longer queries, multi-turn and creativity. 📌 Model Weights: huggingface.co/collections/d… 📌Openrouter: openrouter.ai/deepcogito/cog… 📌 HF Blog: huggingface.co/blog/deepcogi… Some notes on our approach design choices below 👇

🚨Leaderboard Update New model provider in the Arena: @DeepCogito has released Cogito v2.1 (MIT licensed) 🔹Top 10 Open Source Model for WebDev, rank #10 🔹Tie ranks #18 overall for WebDev This puts Cogito v2.1 on par with community favorites like Qwen 3 Coder Plus & Kimi K2 Instruct. Congrats to the @DeepCogito team for this achievement! 👏

Today, we are releasing the best open-weight LLM by a US company: Cogito v2.1 671B. On most industry benchmarks and our internal evals, the model performs competitively with frontier closed and open models, while being ahead of any US open model (such as the best versions of OpenAI’s GPT-OSS, Nvidia’s Nemotron and Meta’s Llama). We also built an interface where you can try the model (it’s free and we don’t store any chats): chat.deepcogito.com Additionally, you can download the model on @huggingface, or try it out on @openrouter, @togethercompute, @FireworksAI_HQ , @ollama cloud, @runpod, @baseten, or run it locally using @ollama or @UnslothAI. This model uses significantly fewer tokens amongst any similar capability models, because it has better reasoning capabilities. You will also notice improvements across instruction following, coding, longer queries, multi-turn and creativity. 📌 Model Weights: huggingface.co/collections/d… 📌Openrouter: openrouter.ai/deepcogito/cog… 📌 HF Blog: huggingface.co/blog/deepcogi… Some notes on our approach design choices below 👇

Today, we are releasing the best open-weight LLM by a US company: Cogito v2.1 671B. On most industry benchmarks and our internal evals, the model performs competitively with frontier closed and open models, while being ahead of any US open model (such as the best versions of OpenAI’s GPT-OSS, Nvidia’s Nemotron and Meta’s Llama). We also built an interface where you can try the model (it’s free and we don’t store any chats): chat.deepcogito.com Additionally, you can download the model on @huggingface, or try it out on @openrouter, @togethercompute, @FireworksAI_HQ , @ollama cloud, @runpod, @baseten, or run it locally using @ollama or @UnslothAI. This model uses significantly fewer tokens amongst any similar capability models, because it has better reasoning capabilities. You will also notice improvements across instruction following, coding, longer queries, multi-turn and creativity. 📌 Model Weights: huggingface.co/collections/d… 📌Openrouter: openrouter.ai/deepcogito/cog… 📌 HF Blog: huggingface.co/blog/deepcogi… Some notes on our approach design choices below 👇

Today, we are releasing the best open-weight LLM by a US company: Cogito v2.1 671B. On most industry benchmarks and our internal evals, the model performs competitively with frontier closed and open models, while being ahead of any US open model (such as the best versions of OpenAI’s GPT-OSS, Nvidia’s Nemotron and Meta’s Llama). We also built an interface where you can try the model (it’s free and we don’t store any chats): chat.deepcogito.com Additionally, you can download the model on @huggingface, or try it out on @openrouter, @togethercompute, @FireworksAI_HQ , @ollama cloud, @runpod, @baseten, or run it locally using @ollama or @UnslothAI. This model uses significantly fewer tokens amongst any similar capability models, because it has better reasoning capabilities. You will also notice improvements across instruction following, coding, longer queries, multi-turn and creativity. 📌 Model Weights: huggingface.co/collections/d… 📌Openrouter: openrouter.ai/deepcogito/cog… 📌 HF Blog: huggingface.co/blog/deepcogi… Some notes on our approach design choices below 👇

Today, we are releasing the best open-weight LLM by a US company: Cogito v2.1 671B. On most industry benchmarks and our internal evals, the model performs competitively with frontier closed and open models, while being ahead of any US open model (such as the best versions of OpenAI’s GPT-OSS, Nvidia’s Nemotron and Meta’s Llama). We also built an interface where you can try the model (it’s free and we don’t store any chats): chat.deepcogito.com Additionally, you can download the model on @huggingface, or try it out on @openrouter, @togethercompute, @FireworksAI_HQ , @ollama cloud, @runpod, @baseten, or run it locally using @ollama or @UnslothAI. This model uses significantly fewer tokens amongst any similar capability models, because it has better reasoning capabilities. You will also notice improvements across instruction following, coding, longer queries, multi-turn and creativity. 📌 Model Weights: huggingface.co/collections/d… 📌Openrouter: openrouter.ai/deepcogito/cog… 📌 HF Blog: huggingface.co/blog/deepcogi… Some notes on our approach design choices below 👇

Today, we are releasing the best open-weight LLM by a US company: Cogito v2.1 671B. On most industry benchmarks and our internal evals, the model performs competitively with frontier closed and open models, while being ahead of any US open model (such as the best versions of OpenAI’s GPT-OSS, Nvidia’s Nemotron and Meta’s Llama). We also built an interface where you can try the model (it’s free and we don’t store any chats): chat.deepcogito.com Additionally, you can download the model on @huggingface, or try it out on @openrouter, @togethercompute, @FireworksAI_HQ , @ollama cloud, @runpod, @baseten, or run it locally using @ollama or @UnslothAI. This model uses significantly fewer tokens amongst any similar capability models, because it has better reasoning capabilities. You will also notice improvements across instruction following, coding, longer queries, multi-turn and creativity. 📌 Model Weights: huggingface.co/collections/d… 📌Openrouter: openrouter.ai/deepcogito/cog… 📌 HF Blog: huggingface.co/blog/deepcogi… Some notes on our approach design choices below 👇

It is intuitively easy to understand why self play *can* work for LLMs, if we are able to provide a value function at intermediate steps (although not as clearly guaranteed as in two-player zero-sum games). In chess / go / poker, we have a reward associated with every next move, but as Noam points out, natural language is messy. It is hard to define a value function at intermediate steps like tokens. As a result, in usual reinforcement learning (like RLVR), LLMs get a reward at the end. They end up learning to 'meander' more for hard problems. In a way, we reward brute forcing with more tokens to end up at the right answer as the right approach. However, at @DeepCogito, we provide a signal for the thinking process itself. Conceptually, you can imagine this as post-hoc assigning a reward to better search trajectories. This teaches the model to develop a stronger intuition for 'how to search' while reasoning. In practice, the model ends up with significantly shorter reasoning chains for harder problems in a reasoning mode. Somewhat surprisingly, it also ends up being better in a non-thinking mode. One way to think about it is that since the model knows how to search better, it 'picks' the most likely trajectory better in the non-thinking mode.

Self play works so well in chess, go, and poker because those games are two-player zero-sum. That simplifies a lot of problems. The real world is messier, which is why we haven’t seen many successes from self play in LLMs yet. Btw @karpathy did great and I mostly agree with him!

A small update - we had more traffic than anticipated. However, the endpoints are now scalable on Together AI for all models, including the 671B MoE. Test out the model here: together.ai/models/cogito-67… (A huge thanks to the folks at @togethercompute for making this happen so quickly.)

Today, we are releasing 4 hybrid reasoning models of sizes 70B, 109B MoE, 405B, 671B MoE under open license. These are some of the strongest LLMs in the world, and serve as a proof of concept for a novel AI paradigm - iterative self-improvement (AI systems improving themselves). The largest 671B MoE model is amongst the strongest open models in the world. It matches/exceeds the performance of the latest DeepSeek v3 and DeepSeek R1 models both, and approaches closed frontier models like o3 and Claude 4 Opus.