At par with DeepSeek, Claude 3.5 and GPT-4o
So after DeepSeek and Alibaba, now it’s time for the third Chinese tech, Tencent, to release state-of-the-art LLMs, Hunyuan Turbo S, which is said to be the fastest reasoning LLM right now.
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The team has used some innovative techniques in this model
https://medium.com/media/f0e4e885142b7d094cc46680ec808252/href
Fast + Slow Thinking
Hunyuan Turbo S incorporates a unique approach inspired by human cognitive processes — fast thinking and slow thinking — to optimize response efficiency and reasoning depth.
- Fast Thinking: This is like human intuition — it allows for instant responses to straightforward or common queries without requiring deep analysis. Turbo S achieves this by doubling word speed and reducing first-word latency by 44%, making it highly efficient for general conversations and quick interactions.
- Slow Thinking: Inspired by analytical reasoning, slow thinking is necessary for complex problem-solving, especially in math, logical reasoning, and science-related queries. Turbo S borrows knowledge from Hunyuan T1, Tencent’s slow-thinking model, which was trained using a technique called long-thinking chain synthesis. This helps Turbo S reason through multi-step problems while maintaining its speed advantage.
- Result: By combining both, Turbo S matches or exceeds models like GPT-4o and Claude 3.5 in reasoning-heavy tasks without compromising speed.
Hybrid-Mamba-Transformer Fusion
https://medium.com/media/5d5dc384a40725f90a97f07b6f7adc47/href
This is a groundbreaking architectural innovation in Turbo S that balances efficiency and contextual reasoning using a mix of two powerful architectures:
1. Mamba → Efficient for Long Sequences
Mamba is a state-space model (SSM) designed to efficiently process long sequences while using significantly less memory compared to Transformers.
Unlike Transformers, which struggle with handling long texts due to quadratic scaling in KV-cache memory, Mamba can process longer text without excessive computational overhead.
Use Case: Great for reading, summarizing, and generating responses for long documents (e.g., legal texts, research papers).
2. Transformer → Strong Contextual Understanding
While Mamba is efficient, it doesn’t capture complex contextual relationships as well as Transformers.
Transformers excel in understanding intricate patterns and dependencies, making them superior for reasoning-heavy tasks like math, logic, and problem-solving.
Use Case: Ideal for multi-step reasoning, code generation, and deep contextual understanding.
3. First-Ever Application of Mamba in a Super-Large MoE Model
MoE (Mixture of Experts) models activate only a subset of parameters for each query, making computation more efficient.
Turbo S is the first large-scale MoE model to successfully integrate Mamba without losing accuracy, allowing it to benefit from Mamba’s efficiency while retaining Transformer’s strong reasoning capabilities.
This breakthrough reduces training and inference costs while enhancing both speed and intelligence.
Key Features
Fast-Thinking Model
Provides instant replies, unlike slow-thinking models that require pre-processing.
Doubles word speed and reduces first-word latency by 44%.
Performance & Capabilities
Excels in knowledge, mathematics, and creation tasks.
Matches top-tier models like DeepSeek V3, GPT-4o, and Claude 3.5 on public benchmarks.
Integrates fast & slow thinking to enhance reasoning for science-related queries.
Hybrid Architecture (Hybrid-Mamba-Transformer)
Reduces KV-Cache usage, computational complexity, and costs.
Balances long-sequence efficiency (Mamba) with context understanding (Transformer).
First successful integration of Mamba into a super-large MoE model.
Core Model for Future Expansions
The foundation for derivative models focused on reasoning, long text, and code.
Powers Hunyuan T1, Tencent’s advanced reasoning model.
Deployment & Pricing
Available via API on Tencent Cloud.
Free one-week trial.
Cost: 0.8 yuan/million tokens (input), 2 yuan/million tokens (output), much cheaper than previous Turbo models.
Benchmarks and metrics
Best in Knowledge & Chinese Understanding (Top scores in MMLU, Chinese-SimpleQA, C-Eval).
Top Performer in Math (Highest scores in MATH and AIME2024).
Competitive in Reasoning (Strong BBH, DROP, and ZebraLogic performance).
Great Alignment & Ethical Responses (Excels in LiveBench, ArenaHard, and IF-Eval).
Weak in SimpleQA & LiveCodeBench (GPT-4o and Claude 3.5 do better).
Overall, Hunyuan Turbo S is one of the best models for knowledge, math, and Chinese tasks while still being competitive in reasoning and alignment.
How to use Hunyuan Turbo S?
The model isn’t released yet and can be tested by filling a form (requires Chinese number) for access
GitHub – Tencent/llm.hunyuan.turbo-s
Tencent HunYuan Turbo S: The fastest reasoning LLM was originally published in Data Science in your pocket on Medium, where people are continuing the conversation by highlighting and responding to this story.
