# Hy-MT2-7B ![Organization](https://img.shields.io/badge/Org-Hungyuan-6366f1) ![Context](https://img.shields.io/badge/Context-262K%20tokens-1f6feb) ![License](https://img.shields.io/badge/License-Apache%202.0-2da44e) ![Modality](https://img.shields.io/badge/Modality-Text-8957e5) ![Released](https://img.shields.io/badge/Released-May%202026-555) > **Quick answer:** Hy-MT2-7B is the mid-size member of Tencent Hunyuan's Hy-MT2 family — a 7B-parameter, Apache 2.0-licensed multilingual translation-specialist model supporting 33 languages. It scores 86.89 (XCOMET-XXL) on FLORES-200's full translation matrix and actually leads the entire Hy-MT2 family — including the larger 30B-A3B — at 63.86 on WMT25's deliberately hard, low-saturation test set. ## At a Glance **Where Hy-MT2-7B leads** - Best-in-family score on WMT25 (63.86 XCOMET-XXL), edging out the larger 30B-A3B MoE variant (62.89) — the only benchmark where the mid-size dense model outperforms its bigger sibling - Strong efficiency-to-quality ratio: dense 7B architecture with straightforward deployment (no MoE routing complexity) while still competitive with much larger general-purpose LLMs on translation-specific evaluation - Outperforms Tower-Plus-72B (a 10x larger open translation-specialist model) on FLORES-200 and WMT25 **Where it lags** - Behind Hy-MT2-30B-A3B on FLORES-200 (86.89 vs 87.47) and on domain/real-world translation benchmarks - Behind frontier general-purpose models like Gemini 3.1 Pro on raw translation quality across the board - General instruction-following (IFBench, IFEval, MaXIFE, Multi-IF) is weaker than translation-specific instruction following — it's tuned for translation, not general-purpose chat **Best for:** Deployments wanting the best balance of translation quality and dense (non-MoE) architectural simplicity — particularly strong on harder/fresher test material like WMT25. ## What Hy-MT2-7B Is Hy-MT2-7B is the mid-size member (of 1.8B, 7B, 30B-A3B) in Tencent Hunyuan's second-generation "fast-thinking" multilingual translation model family, described in the technical report "Hy-MT2: A Family of Fast, Efficient and Powerful Multilingual Translation Models in the Wild." It's a dense transformer trained via a three-stage Family-Centric Post-training (FCPT) pipeline: MT-oriented mid-training on ~1T tokens of translation-related data, followed by Reference-Guided On-Policy Distillation and Family-specific RL training organized around language families (Western European, East Asian, Middle Eastern right-to-left), then a final Cross-family distillation step into a unified model. Like its siblings, Hy-MT2-7B is evaluated across four dimensions: general translation (FLORES-200, WMT25, Mandarin⇔Minority), real-world business scenarios (Tencent's own WildMTBench), domain-specific translation (Tencent's own DomainMTBench, covering finance/law/medicine/technology/politics/education), and translation instruction-following (IFMTBench, plus general instruction benchmarks IFBench/IFEval/MaXIFE/Multi-IF). Its standout result is topping the entire Hy-MT2 family on WMT25 — a test set explicitly curated to be hard and resist saturation — suggesting the 7B size may generalize better to genuinely novel source material than the larger MoE variant's higher-capacity-but-more-specialized routing. ## Specifications | Field | Value | |---|---| | Organization | Hungyuan (Tencent) | | Architecture | Dense transformer | | Parameters | 7B | | Context length | 262,144 tokens | | Supported languages | 33 | | License | Apache 2.0 | | Release date | May 2026 | | Modality | Text | | Quantized variants | FP8, GGUF (multiple quant levels) | ## Pricing Open weights under Apache 2.0 — self-hosted via transformers, vLLM, SGLang, or llama.cpp (GGUF builds available). No API list price; cost is your own inference infrastructure. ## Public Benchmark Scores | Benchmark | Score | Source | Date | |---|---|---|---| | [FLORES-200](/benchmarks/meta-ai/flores-200) | 86.89 (XCOMET-XXL) | Hy-MT2 technical report | 2026-05 | | [WMT25](/benchmarks/wmt/wmt25) | 63.86 (XCOMET-XXL) | Hy-MT2 technical report | 2026-05 | *Hy-MT2-7B also reports scores on Tencent's own in-house evaluation suites (WildMTBench, DomainMTBench, IFMTBench, Mandarin⇔Minority testset) and on general instruction-following benchmarks (IFBench, IFEval, MaXIFE, Multi-IF) — not added as Benchgen leaderboard entries since these either lack an independent public benchmark page (WildMTBench/DomainMTBench/IFMTBench are Tencent-constructed, non-independently-reproducible test sets) or measure general instruction-following rather than translation capability specifically.* ## Hy-MT2-7B vs Alternatives | Model | FLORES-200 (XCOMET-XXL) | WMT25 (XCOMET-XXL) | License | Params | |---|---|---|---|---| | [Hy-MT2-1.8B](/models/hungyuan/hy-mt2-1-8b) | 79.77 | 50.30 | Apache 2.0 | 1.8B | | **Hy-MT2-7B** | 86.89 | **63.86** | Apache 2.0 | 7B | | [Hy-MT2-30B-A3B](/models/hungyuan/hy-mt2-30b-a3b) | **87.47** | 62.89 | Apache 2.0 | 30B (3B active) | Hy-MT2-7B sits between its siblings on overall translation quality, but its WMT25 lead over the larger MoE variant makes it worth considering specifically for harder or less-templated source material. ## Related Models & Benchmarks - Compare: [Hy-MT2-1.8B](/models/hungyuan/hy-mt2-1-8b) · [Hy-MT2-30B-A3B](/models/hungyuan/hy-mt2-30b-a3b) - Benchmarks: [FLORES-200](/benchmarks/meta-ai/flores-200) · [WMT25](/benchmarks/wmt/wmt25) ## Frequently Asked Questions

What is Hy-MT2-7B?

Hy-MT2-7B is Tencent Hunyuan's mid-size translation-specialist model, a 7B-parameter dense transformer supporting 33 languages, released under Apache 2.0.

Is Hy-MT2-7B open source?

Yes — it's released under the Apache 2.0 license with weights on Hugging Face and ModelScope, including FP8 and GGUF quantized variants.

Does Hy-MT2-7B beat the larger Hy-MT2-30B-A3B model?

Only on WMT25 (63.86 vs 62.89 XCOMET-XXL) — on FLORES-200 and the family's other evaluations, the larger 30B-A3B MoE model leads overall.

--- *Specs from Tencent Hunyuan's official Hy-MT2-7B Hugging Face model card and technical report (arXiv:2605.22064). Last updated 2026-08-31.*

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