HY-MT1.5-1.8B: Tencent Translation Model Beats Google Translate by 15-65%
Tencent releases WMT2025 winner distilled to 1.8B parameters. The model runs on smartphones with just 1GB RAM and supports 33 languages including underserved ones like Tibetan, Uyghur, Kazakh, and Mongolian.
Key Takeaways
- -WMT2025 Winner Lineage: Distilled from the 7B model that won 30 of 31 language pairs at WMT2025
- -Edge Deployable: 1GB RAM after quantization, 0.18s inference for 50 tokens
- -Outperforms Google: 15-65% improvement across WMT25 evaluation categories
- -Broad Language Support: 33 languages plus 5 Chinese dialects, including underserved languages
Technical Specifications
| Specification | Value |
|---|---|
| Parameters | 1.8B |
| Tensor Type | BF16 |
| Context Length | 2048 tokens |
| Pretraining Tokens | 1.3T |
| RAM (Quantized) | 1GB |
| Inference Speed | 0.18s / 50 tokens |
| Languages Supported | 33 + 5 Chinese dialects |
| Teacher Model | Hunyuan-MT-7B (WMT25 winner) |
| Distillation Method | On-policy distillation |
| Release Date | December 30, 2025 |
Benchmark Results
Hunyuan-MT demonstrates state-of-the-art performance across major translation benchmarks, with the 7B teacher model winning 30 of 31 language pairs at WMT2025.
| Benchmark | Hunyuan-MT-7B | Google Translate | Gemini-2.5-Pro |
|---|---|---|---|
| WMT24pp EN-XX | 0.8585 | - | 0.8250 |
| FLORES-200 | 0.8758 | - | - |
| WMT25 Ranking | 1st (30/31) | Lower | Lower |
Note: The 1.8B distilled model achieves approximately 90% of Gemini-3.0-Pro performance on FLORES-200, making it the most capable edge-deployable translation model available.
Language Coverage
HY-MT1.5 supports 33 major world languages plus 5 Chinese dialects, with notable strength in underserved languages often neglected by commercial translation services.
Underserved Languages
Strong performance on languages with limited training data:
- Tibetan
- Uyghur
- Kazakh
- Mongolian
Chinese Dialects
Dedicated support for major Chinese regional varieties:
- Cantonese
- Shanghainese (Wu)
- Hokkien (Min Nan)
- Hakka
- Teochew
Edge Deployment Capabilities
The defining feature of HY-MT1.5-1.8B is its ability to run entirely on-device, enabling private, offline translation without cloud dependencies.
Deployment Targets
- -Smartphones: iOS and Android devices with 2GB+ RAM (most devices from 2020 onward)
- -Embedded Systems: Raspberry Pi 4/5, Jetson Nano, and similar edge hardware
- -Offline Applications: Travel apps, fieldwork tools, privacy-focused translation
- -Browser Extensions: WebAssembly deployment for in-browser translation
Competitive Analysis: HY-MT vs. Alternatives
vs. Google Translate
HY-MT Advantages
- - 15-65% higher quality across WMT25 categories
- - Fully offline operation (no API dependency)
- - Open weights for customization
- - No usage limits or API costs
- - Privacy: data never leaves device
Google Advantages
- - 100+ languages vs. 33
- - Established ecosystem integration
- - Real-time camera translation
- - Document translation features
vs. DeepL
HY-MT Advantages
- - Edge deployment (DeepL is cloud-only)
- - Open source (DeepL is proprietary)
- - Asian language strength (Chinese dialects)
- - Zero recurring costs
DeepL Advantages
- - Superior European language pairs
- - Document formatting preservation
- - Glossary and terminology tools
- - Enterprise API with SLAs
vs. Meta NLLB-200
HY-MT Advantages
- - Higher benchmark scores on major pairs
- - Smaller model size (1.8B vs 3.3B distilled)
- - Faster inference speed
- - Better Chinese dialect coverage
NLLB Advantages
- - 200 languages vs. 33
- - Better low-resource African languages
- - Larger research community
- - More deployment tutorials available
Recommendations
Mobile/Offline Translation
HY-MT1.5-1.8B is the recommended choice for mobile and offline translation applications. The 1GB quantized footprint and 0.18s inference make it viable for real-time translation on modern smartphones without network connectivity.
Best for: Travel apps, field research tools, privacy-focused messaging, areas with limited connectivity.
High-Volume Asian Language Pairs
For applications requiring Chinese, Japanese, Korean, or Southeast Asian language translation at scale, HY-MT offers superior quality at zero marginal cost compared to commercial APIs.
Best for: E-commerce localization, content platforms, customer support automation.
When to Use Alternatives
Consider Google Translate or DeepL for: European language pairs (especially German/French), document translation with formatting, or when you need 100+ languages. Use Meta NLLB for low-resource African languages.
Key limitation: HY-MT supports 33 languages. If your use case requires broader coverage, NLLB-200 may be more appropriate despite lower benchmark scores.
How On-Policy Distillation Works
The 1.8B model is distilled from the 7B WMT25 winner using on-policy distillation, a technique that preserves more of the teacher model's capabilities than traditional knowledge distillation.
- 1Teacher Generation: The 7B model generates translation samples for the training corpus, creating high-quality target distributions.
- 2On-Policy Sampling: The student model generates its own translations, which are then compared against teacher outputs rather than fixed targets.
- 3Distribution Matching: The student learns to match the full output distribution of the teacher, not just argmax predictions, preserving translation variety.
- 4Iterative Refinement: Multiple rounds of distillation with curriculum learning, starting from easier language pairs.
Related Content
Conclusion
Tencent's HY-MT1.5-1.8B represents a significant milestone in democratizing high-quality machine translation. By distilling their WMT2025-winning 7B model into an edge-deployable package, they have created a translation system that outperforms Google Translate on major benchmarks while running entirely on-device.
The model's particular strength in underserved languages (Tibetan, Uyghur, Kazakh, Mongolian) and Chinese dialects fills an important gap in the translation landscape. For developers building offline-capable translation applications, this is now the state-of-the-art choice.
The main limitation remains language coverage: 33 languages versus Google's 100+ or NLLB's 200. However, for the languages it does support, HY-MT1.5 delivers exceptional quality at unprecedented efficiency.