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: https://t.co/Od1NXYVBxU (A huge thanks to the folks at @togethercompute for making this happen so

You can now run the world’s most powerful Western open models locally! The hybrid reasoning 671B model matches o3 & Claude-4-Opus in performance. Trained on Llama 4 & DeepSeek-R1, Cogito-v2 has 4 variants—each setting new benchmarks. Guide + GGUFs: https://t.co/rHBD0mZGZH

More details in the blog post:

The Cogito v2 models can be downloaded on @Huggingface, and can be accessed via API through @TogetherAI or @runpod_io or @basetenco. You can also run them locally using @UnslothAI. Each model can answer directly (standard LLM), or self-reflect before answering (like reasoning

We plan to scale up and expect significant improvements by self improvement over the next few months using this approach. From experiments so far, this technique is far more efficient than simply “searching more” by longer reasoning chains. All models we create will be open

This seems to be a novel scaling paradigm where the models develop more “intuition”, and serves as a strong proof of concept for self-improvement. Since the Cogito models develop a better intuition of the trajectory to take while searching at inference time, they have 60% shorter

The models are built on our work on building superintelligence using Iterated Distillation and Amplification (IDA). In particular, we scale the model’s intelligence prior by the model internalizing the reasoning process using iterative policy improvement, rather than simply

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).

Data quality has the biggest impact on LLM performance - far more than most algorithmic improvements. As we build models that move towards superintelligence, the paradigm for LLM evaluation would need to evolve to increase the strength of the overseer. Very excited to see this -

All models we create will be open sourced. More details in the blog post:

The Cogito v1 models can be downloaded on @huggingface or @ollama, and can be accessed via API through @FireworksAI_HQ or @togethercompute. Each model can answer directly (standard LLM), or self-reflect before answering (like reasoning models).

From what we can tell, we're still in the early stages of this scaling curve - IDA is incredibly powerful and generalizes across domains. Most notably, our 70B model also outperforms Llama 4 Scout (109B MoE) distilled from a 2T model. As we improve and iterate on our

We use IDA to remove the intelligence ceiling. Simply put, use more computation to let the model arrive at a better solution, and then distill the expensive thinking process to the model's own parameters. As the LLM improves in intelligence, the thinking process itself becomes

Traditional LLMs are upper bounded in intelligence by their overseers (larger teacher models or human capabilities). Building superintelligence requires not only matching human-level abilities but also to uncover entirely new capabilities we have yet to imagine.

Today, we are launching @DeepCogito, where we are building general superintelligence. We are also releasing open models of 3B, 8B, 14B, 32B, and 70B sizes trained using our research on iterated distillation and amplification (IDA). From evals so far, each model outperforms the

4/ Deep Cogito You can build a more powerful language model with more data and more compute. @drishanarora showed how he & @drvdhruv are pioneering a 3rd way and building frontier intelligence LLMs. (off-the-record-ish, ping @drishanarora & @drvdhruv)