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

RT @southpkcommons: 4/ Deep Cogito. You can build a more powerful language model with more data and more compute. @drishanarora showed how….