Hunyuan-T1

Hunyuan-T1 is built on Tencent's TurboS foundation, a Hybrid-Transformer-Mamba Mixture of Experts architecture designed for deep reasoning tasks. The architecture combines Transformer blocks for global contextual awareness with Mamba-2 state-space layers that provide linear scaling and superior memory efficiency for sequence modeling. With 16 experts and dynamic routing that activates approximately 52 billion parameters per token, the model handles context lengths up to 256,000 tokens while maintaining fast decoding speeds. This design mitigates the quadratic complexity of traditional attention mechanisms, allowing the model to process extended documents efficiently while keeping computational requirements in check. As the primary "slow-thinking" reasoning engine within the Hunyuan ecosystem, Hunyuan-T1 prioritizes structured logic and long-form consistency over creative generation.

The post-training phase placed heavy emphasis on reinforcement learning, with Tencent allocating 96.7% of computing resources to this stage. The training incorporated curriculum learning with gradually increasing difficulty, step-by-step context length expansion, classic RL strategies including data replay and periodic policy resetting, and a unified reward system with self-referencing. This approach earned Hunyuan-T1 competitive benchmark standing—scoring 87.2 on MMLU Pro, 78.2 on the AIME 2024 math test, and 91.8 on C-Eval—rivaling models like DeepSeek R1 and matching OpenAI's o1 on several reasoning tasks. The model handles long prompts by breaking things down step-by-step, maintaining clean structure and avoiding the verbose drifting common in less specialized systems. Its focus on clear logic, fast response, and minimal hallucination makes it particularly suitable for deep analytical tasks, complex instruction following, and advanced scientific inquiry.