Tencent is expanding its AI portfolio with the launch of Hunyuan T1, a reasoning-optimized model designed to compete with China’s top-tier large language models, including DeepSeek-R1.
Developed in-house and deployed on Tencent Cloud, the model is part of a broader strategy to provide enterprise-ready AI solutions tuned for cost efficiency, Chinese-language tasks, and stable performance.
Hunyuan T1 is now available via API, built-in access across Tencent Docs, and can be tested via a demo on Hugging Face. It is tuned using reinforcement learning and internally benchmarked on reasoning datasets such as MMLU and GPQA.
Tencent positions the model as a commercially viable tool for businesses that need high-performance reasoning without the compute burden or licensing costs tied to Western alternatives.
Turbo S Set the Stage, T1 Sharpens the Focus
Before T1 entered the spotlight, Tencent introduced Hunyuan Turbo S on February 27. Hunyuan T1 is Tencent’s most reasoning-optimized model to date, positioned to meet the needs of enterprise users requiring structured logic, consistent long-form generation, and reduced hallucination.
- Reasoning Focus: T1 is engineered specifically for complex reasoning tasks, such as structured problem-solving, mathematical analysis, and decision support. Tencent has applied reinforcement learning techniques to improve long-form consistency and minimize factual hallucination.
- Chinese Language Optimization: The model performs particularly well in Chinese-language logic and reading comprehension tasks, aligning with Tencent’s focus on domestic enterprise use cases.
- Training Data and Infrastructure: T1 was trained entirely in-house using Tencent Cloud infrastructure, ensuring data residency and compliance with Chinese regulatory standards.
Benchmark Results
Tencent’s Hunyuan T1 is positioned as a high-performance reasoning model optimized for enterprise-grade tasks in Chinese and mathematical domains. The model has been trained and hosted entirely on Tencent Cloud, with availability via API and integration into Tencent Docs.
Combined with full domestic hosting on Tencent Cloud and integration into productivity tools like Tencent Docs, Hunyuan T1 is tailored for business environments that demand robust logic, regulatory compliance, and native language fluency. Its benchmark profile suggests a clear strategic focus: excelling in reasoning and math while maintaining respectable alignment, language handling, and code generation performance.
- Knowledge: Hunyuan T1 scores 87.2 on MMLU PRO, outperforming DeepSeek R1 (84.0) and GPT-4.5 (86.1), though trailing o1 (89.3). It trails in GPQA Diamond with 69.3, lower than DeepSeek R1 (71.5) and o1 (75.7). On C–SimpleQA, T1 scores 67.9, behind DeepSeek R1 (73.4).
- Reasoning: T1 excels in this category. It achieves the highest score on DROP F1 at 93.1, ahead of DeepSeek R1 (92.2), GPT-4.5 (84.7), and o1 (90.2). On Zebra Logic, it scores 79.6, just behind o1 (87.9) but well above GPT-4.5 (53.7).
- Math: Hunyuan T1 scores 96.2 on MATH–500, just below DeepSeek R1’s 97.3 and close to o1’s 96.4. Its AIME 2024 score is 78.2, slightly under DeepSeek R1 (79.8) and o1 (79.2), but far above GPT-4.5 (50.0).
- Code: The model scores 64.9 on LiveCodeBench—marginally below DeepSeek R1 (65.9) and o1 (63.4), but significantly ahead of GPT-4.5 (46.4). This positions it as capable, though not exceptional, in code generation.
- Chinese Language Understanding: Hunyuan T1 scores 91.8 on C-Eval and 90.0 on CMMLU, tying DeepSeek R1 on both and outperforming GPT-4.5 by nearly 10 points. This confirms its strength in Chinese enterprise contexts.
- Alignment: On ArenaHard, T1 scores 91.9—slightly behind GPT-4.5 (92.5) and DeepSeek R1 (92.3), but ahead of o1 (90.7), indicating robust value alignment and instruction coherence.
- Instruction Following: The model earns 81.0 on CFBench, slightly under DeepSeek R1 (81.9) and GPT-4.5 (81.2), and 76.4 on CELLO, below both DeepSeek R1 (77.1) and GPT-4.5 (81.4). These results suggest good but not best-in-class instruction compliance.
- Tool Use: Hunyuan T1 scores 68.8 on T-Eval, which measures AI’s ability to operate external tools. It outperforms DeepSeek R1 (55.7) but falls short of GPT-4.5 (81.9) and o1 (75.7).
Model Efficiency Meets Real-World Constraints
While expanding its proprietary model suite, Tencent continues to rely on third-party models like DeepSeek to meet performance demands while lowering infrastructure costs. During its Q4 2024 earnings call, executives explained how inference efficiency—not compute scale—is guiding their deployment choices.
Tencent recently confirmed its use of DeepSeek’s architecture-optimized models to reduce GPU consumption and improve throughput. “Chinese companies are generally prioritizing efficiency and utilization—efficient utilization of the GPU servers. And that doesn’t necessarily impair the ultimate effectiveness of the technology that’s being developed,” said the company’s chief strategy officer.
This approach allows Tencent to tailor models to specific infrastructure constraints. Rather than scaling GPU clusters, it is focusing on lower-latency, inference-tuned models that are lighter to run. The strategy mirrors research-backed methods like Sample, Scrutinize and Scale, which emphasize verification at inference time instead of more resource-heavy training.
Despite this efficiency focus, Tencent isn’t backing away from hardware investments. According to a TrendForce report, the company has placed large orders for NVIDIA’s H20 chips—specialized GPUs for the Chinese market. These chips support Tencent’s integration of DeepSeek models into backend services, including those powering WeChat.
Shifting Politics, Shifting Priorities
The launch of T1 comes amid heightened scrutiny of Chinese AI tools abroad. On March 17, 2025, the U.S. Commerce Department barred DeepSeek’s applications from use on federal government devices, citing privacy risks and potential links to state-controlled infrastructure. Additional restrictions may follow, complicating cross-border AI adoption for models developed in China.
Back home, the Chinese government is actively promoting newer AI startups. Reuters reportes that Beijing is supporting Monica, the developer of Manus, an autnomous AI agent. While Tencent is not directly involved with these initiatives, its leadership in the domestic cloud and software markets ensures it remains central to the broader AI ecosystem.
That central role appears to be paying off. In Q4 2024, Tencent’s revenue rose 11% year-over-year to 172.45 billion yuan. A portion of that growth was attributed to enterprise AI development, with the company signaling further investment in 2025 to expand both consumer-facing and enterprise-ready AI infrastructure.
Model Diversification Meets Deployment Strategy
Tencent’s two-pronged AI strategy—Hunyuan T1 for structured reasoning and Turbo S for instant replies—enables it to deliver model-specific capabilities across different business verticals.
Rather than scaling up a single large model, the company is aligning each release with precise usage scenarios: complex logic for internal analytics, fast interaction for customer interfaces.
Each model is deeply integrated into Tencent’s cloud infrastructure. This approach may appeal to businesses seeking AI solutions that are fully hosted in China and compliant with national data standards.
In contrast to OpenAI’s trajectory—which saw the release of its largest and most expensive model yet, GPT-4.5, in February—Tencent’s strategy appears more calibrated.
With Hunyuan T1 now live and Turbo S already active in latency-sensitive environments, Tencent is expanding its role in China’s rapidly evolving AI sector.
The company’s combination of in-house development, selective external adoption, and integrated product rollout suggests a strategy rooted in adaptability rather than volume. As policy pressure and hardware constraints reshape the market, that approach could prove increasingly pragmatic.
