According to developer Deep Cogito, Cogito v2 is one of the world’s most powerful open-source AI models, available in sizes ranging from 70B to 671B parameters. Thanks to its unique, iterative self-learning method (IDA), the model solves complex problems by developing its internal “intuition” rather than by searching for longer, shorter and more efficient thoughts.

• Market-leading performance: The company claims that the performance of the largest 671B-parameter MoE (Mixture of Experts) model competes with the latest DeepSeek models and approaches that of closed models such as o3 and Claude 4 Opus. The models have been trained in over 30 languages ​​and are optimized for coding, STEM tasks, instruction following, and tool calling.

• Innovative Training Method (IDA): The company uses a method called Iterated Distillation & Amplification (IDA), which it describes as a scalable and efficient strategy for achieving superintelligence. The essence of this is that the model internalizes the inference process and improves its own parameters through iterative self-improvement, rather than simply searching for the answer. According to Deep Cogito, this helps the models develop better “intuition.”

• Superior efficiency: The company emphasizes that thanks to the IDA method, their models achieve superior results with shorter “reasoning chains.” For example, their 671B model uses 60% shorter reasoning chains than DeepSeek R1. This approach is also significantly more cost-effective; they claim that training all of the Cogito models cost less than $3.5 million.

• Flexible, hybrid operation: According to the company, Cogito v2 models are hybrid models, which means that they can respond immediately (like a standard LLM), or respond after a self-reflective, “thinking” process. This thinking mode can be manually turned on.

• Size selection and local runnability: Deep Cogito has released the models in four different sizes (70B, 109B, 405B, 671B), so users can choose the model that suits their hardware

. The company highlights that with the help of Unsloth, the models can also be run locally, even in quantized (reduced size) form, with minimal loss of accuracy.

• Emergent capabilities: The company mentions as an interesting consequence that although the models were trained only on text data, thanks to the multimodal base model, they can also think on visual content through transfer learning.

LinksCogito V2 Preview: https://www.deepcogito.com/research/cogito-v2-previewHugging Face Cogito v2 preview - 671B MoE: https://huggingface.co/deepcogito/cogito-v2-preview-deepseek-671B-MoEunsloth: https://docs.unsloth.ai/basics/tutorials-how-to-fine-tune-and-run-llms/cogito-v2-how-to-run-locallyOpenRouter: https://openrouter.ai/deepcogito/cogito-v2-preview-deepseek-671b