Deep Cogito
Deploy Cogito v2 models on Together AI. Iterative self-improvement, 60% shorter reasoning chains, and frontier performance under open license.
Why Deep Cogito on Together AI?
Designed for production workloads that need consistent performance and operational control.
Iterative self-improvement
First reasoning models to improve core intelligence, not just search time. Models develop stronger intuition through distillation of reasoning processes — delivering 60% shorter reasoning chains than DeepSeek R1 with superior performance.
Breakthrough efficiency
Complete model family trained for under $3.5M total cost. Significantly more efficient than capital-intensive approaches — proving superintelligence research is accessible to the broader ecosystem.
Open superintelligence
All models released under open license for commercial use. Complete transparency in the reasoning process with visible thinking tags — build on the research or deploy without restrictions.
Meet the Deep Cogito family
Explore top-performing models across text, image, video, code, and voice.
Breakthrough technical innovations
Explore all the game-changing architectural advances that make Deep Cogito models shine.
- Mixture of Experts (MoE)
Sparse expert routing activates only 37B out of 671B parameters for each token in V3. Advanced load balancing without auxiliary losses maintains performance while reducing computational cost.
- Group Relative Policy Optimization
New RL approach that removes separate value networks in RLHF, using grouped relative advantage estimation to cut compute requirements while maintaining training stability.
- Native Reasoning Transparency
First reasoning model to expose complete thinking process in <think> tags. Native reasoning capabilities built into model foundation through large-scale reinforcement learning.
- MetaP Training
First successful implementation of FP8 mixed precision training on a 671B parameter model. Pioneering reinforcement learning approach without supervised fine-tuning as preliminary step.
- Multi-Head Latent Attention
Innovative attention mechanism that reduces KV-cache memory requirements while maintaining modeling performance. Optimized for efficient inference deployment.
- Multi-Token Prediction
Novel training objective that allows the model to predict multiple tokens simultaneously. Enhanced performance and efficiency through advanced training techniques.
Deployment options
Run models using different deployment options depending on latency needs, traffic patterns, and infrastructure control.
Real-time
A fully managed inference API that automatically scales with request volume.
Best for
Batch
Process massive workloads of up to 30 billion tokens asynchronously, at up to 50% less cost.
Best for
Dedicated Model Inference
An inference endpoint backed by reserved, isolated compute resources and the Together AI inference engine.
Best for
Dedicated Container Inference
Run inference with your own engine and model on fully-managed, scalable infrastructure.
Best for