Model Details
Hy-MT2-30B-A3B
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Quick answer: Hy-MT2-30B-A3B is the flagship member of Tencent Hunyuan's Hy-MT2 family — a 30B-parameter Mixture-of-Experts model (3B active parameters, 128 experts with 8 active + 1 shared) supporting 33 languages, released under Apache 2.0. It leads its family on FLORES-200's full translation matrix at 87.47 (XCOMET-XXL), while running inference at roughly the cost of a 3B dense model thanks to its MoE routing.
At a Glance
Where Hy-MT2-30B-A3B leads
- Best-in-family score on FLORES-200 (87.47 XCOMET-XXL) and on domain-specific/real-world translation evaluations (Tencent's in-house DomainMTBench and WildMTBench)
- MoE efficiency: only 3B parameters active per forward pass (128 experts, 8 active + 1 shared) despite 30B total — inference cost closer to a small dense model than its parameter count suggests
- Outperforms Tower-Plus-72B (a much larger dedicated open translation model) across every reported general-translation benchmark
Where it lags
- Narrowly behind the smaller dense Hy-MT2-7B on WMT25 specifically (62.89 vs 63.86 XCOMET-XXL) — the family's one benchmark where bigger doesn't win
- 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 highest overall translation quality in the Hy-MT2 family while keeping inference cost close to a small dense model, via MoE's sparse activation.
What Hy-MT2-30B-A3B Is
Hy-MT2-30B-A3B is the flagship size (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." Unlike its two dense siblings, it uses a Mixture-of-Experts architecture (128 experts, 8 active plus 1 shared expert per token, ~3B active parameters), trained via the same 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-MT2-30B-A3B 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). It's the strongest all-around model in the family, leading on FLORES-200 and the in-house domain/real-world suites, while its MoE design keeps per-token compute close to a 3B dense model.
Specifications
| Field | Value |
|---|---|
| Organization | Hungyuan (Tencent) |
| Architecture | Mixture-of-Experts (128 experts, 8 active + 1 shared) |
| Total parameters | 30B |
| Active parameters | ~3B |
| 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 | 87.47 (XCOMET-XXL) | Hy-MT2 technical report | 2026-05 |
| WMT25 | 62.89 (XCOMET-XXL) | Hy-MT2 technical report | 2026-05 |
Hy-MT2-30B-A3B 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-MT2-30B-A3B vs Alternatives
| Model | FLORES-200 (XCOMET-XXL) | WMT25 (XCOMET-XXL) | License | Params |
|---|---|---|---|---|
| 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 | 87.47 | 62.89 | Apache 2.0 | 30B (3B active) |
Hy-MT2-30B-A3B is the strongest all-around model in the family, only narrowly ceding the WMT25 lead to the smaller dense Hy-MT2-7B.
Related Models & Benchmarks
- Compare: Hy-MT2-1.8B · Hy-MT2-7B
- Benchmarks: FLORES-200 · WMT25
Frequently Asked Questions
What is Hy-MT2-30B-A3B?
Hy-MT2-30B-A3B is Tencent Hunyuan's flagship translation-specialist model, a 30B-parameter Mixture-of-Experts model (3B active parameters) supporting 33 languages, released under Apache 2.0.Is Hy-MT2-30B-A3B 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.What does the "A3B" in Hy-MT2-30B-A3B mean?
"A3B" denotes the model's active-parameter count under its Mixture-of-Experts architecture — 30B total parameters, but only about 3B active per forward pass (128 experts, 8 active plus 1 shared).Specs from Tencent Hunyuan's official Hy-MT2-30B-A3B Hugging Face model card and technical report (arXiv:2605.22064). Last updated 2026-08-31.