Tencentopen-weight
Released: 2026-06-01Tencent: Hy-MT2-7B
Hy-MT2-7B is a 7B-parameter translation model from Tencent. It supports 33 language pairs and five Chinese dialect and minority-language pairs, with workflows for structured, delimiter-based, contextual, glossary-based, and style-guided translation.
Intelligence Index
81.0/ 100
Calibrated multi-domain compositeThroughput Speed
130tok/sec
Output streaming throughputTime To First Token
280ms
Initial chunk server latencyLiveBench Contam-Free
71.3%
September 2026 suite scoreLiveBench Multi-Domain Evaluation Breakdown
Monthly refreshed contamination-free test setsLogical Reasoning74.5%
Coding & Repo Repair72.9%
Mathematics (AIME)72.1%
Data Analysis & Tables68.9%
Language & Comprehension70.5%
Instruction Following71.3%
Sourced Benchmark Evaluations (51)
Verified performance across authoritative benchmarks with provenance tracking.| Benchmark | Status | Score | Trust | Date | Source Type | Provenance |
|---|---|---|---|---|---|---|
| ARC-AGI-3 | Active | 51.1% | 93 | 2026-07-06 | independent | Source ↗ |
| ARC-AGI-2 | Nearing Saturation | 61.3% | 75 | 2026-06-29 | independent | Source ↗ |
| FrontierMath | Active | 21.1% | 98 | 2026-06-26 | independent | Source ↗ |
| PutnamBench | Active | 39.2% | 90 | 2026-06-25 | independent | Source ↗ |
| Omni-MATH | Deprecated | 55.7% | 10 | 2026-06-23 | independent | Source ↗ |
| SWE-Lancer | Deprecated | 52.4% | 20 | 2026-06-23 | independent | Source ↗ |
| Soohak | Active | 57.2% | 98 | 2026-06-21 | independent | Source ↗ |
| FrontierCode | Active | 44.7% | 98 | 2026-06-21 | independent | Source ↗ |
| AIME | Nearing Saturation | 71.5% | 75 | 2026-06-19 | independent | Source ↗ |
| Humanity's Last Exam | Active | 34.6% | 93 | 2026-06-19 | independent | Source ↗ |
| StructFlowBench | Nearing Saturation | 60.6% | 75 | 2026-06-19 | independent | Source ↗ |
| OlympiadBench | Active | 61.7% | 80 | 2026-06-18 | independent | Source ↗ |
| MathArena | Active | 60.2% | 99 | 2026-06-17 | independent | Source ↗ |
| FireBench | Active | 59.9% | 95 | 2026-06-17 | independent | Source ↗ |
| Terminal-Bench | Active | 53.9% | 80 | 2026-06-17 | independent | Source ↗ |
| CodeContests | Deprecated | 50.1% | 10 | 2026-06-16 | independent | Source ↗ |
| SimpleQA | Active | 42.1% | 90 | 2026-06-16 | independent | Source ↗ |
| SWE-bench Pro | Active | 47.7% | 90 | 2026-06-15 | independent | Source ↗ |
| ComplexBench | Nearing Saturation | 58.5% | 80 | 2026-06-15 | independent | Source ↗ |
| MultiPL-E | Deprecated | 70.8% | 10 | 2026-06-14 | independent | Source ↗ |
| AgentIF | Nearing Saturation | 48.5% | 80 | 2026-06-13 | independent | Source ↗ |
| MATH | Saturated | 73.8% | 25 | 2026-06-13 | independent | Source ↗ |
| LiveCodeBench | Saturated | 63.1% | 39 | 2026-06-13 | independent | Source ↗ |
| Mercury | Deprecated | 67.8% | 10 | 2026-06-12 | independent | Source ↗ |
| MMLU-Pro | Nearing Saturation | 74.5% | 75 | 2026-06-12 | independent | Source ↗ |
| CyberSecEval | Deprecated | 69.3% | 10 | 2026-06-11 | independent | Source ↗ |
| TabMWP | Saturated | 69.3% | 25 | 2026-06-11 | independent | Source ↗ |
| SWE-bench Verified | Saturated | 60.1% | 25 | 2026-06-10 | independent | Source ↗ |
| MMLU-Redux | Saturated | 79.4% | 25 | 2026-06-10 | independent | Source ↗ |
| SWE-bench | Deprecated | 52.4% | 10 | 2026-06-09 | independent | Source ↗ |
| MGSM | Nearing Saturation | 71.5% | 65 | 2026-06-09 | independent | Source ↗ |
| TruthfulQA | Saturated | 71.3% | 25 | 2026-06-08 | independent | Source ↗ |
| AGIEval | Saturated | 67.6% | 25 | 2026-06-08 | independent | Source ↗ |
| SuperGLUE | Saturated | 80.2% | 25 | 2026-06-07 | independent | Source ↗ |
| GSM-Hard | Deprecated | 72.3% | 10 | 2026-06-07 | independent | Source ↗ |
| MT-Bench | Nearing Saturation | 7.3% | 75 | 2026-06-06 | independent | Source ↗ |
| CodeXGLUE | Deprecated | 72.4% | 23 | 2026-06-06 | independent | Source ↗ |
| BIG-Bench Hard | Saturated | 77% | 25 | 2026-06-06 | independent | Source ↗ |
| MBPP+ | Nearing Saturation | 78.5% | 65 | 2026-06-05 | independent | Source ↗ |
| IFEval | Deprecated | 69.9% | 25 | 2026-06-05 | independent | Source ↗ |
| WinoGrande | Saturated | 81.7% | 28 | 2026-06-05 | independent | Source ↗ |
| GPQA | Nearing Saturation | 71.3% | 78 | 2026-06-05 | independent | Source ↗ |
| HumanEval+ | Saturated | 83.2% | 25 | 2026-06-04 | independent | Source ↗ |
| CommonsenseQA | Saturated | 82.5% | 28 | 2026-06-04 | independent | Source ↗ |
| MBPP | Saturated | 81.6% | 25 | 2026-06-03 | independent | Source ↗ |
| GSM8K | Saturated | 81.3% | 25 | 2026-06-03 | independent | Source ↗ |
| GLUE | Saturated | 84.9% | 28 | 2026-06-03 | independent | Source ↗ |
| MMLU | Saturated | 82.6% | 25 | 2026-06-02 | independent | Source ↗ |
| ARC-AGI | Saturated | 73.9% | 25 | 2026-06-02 | independent | Source ↗ |
| HumanEval | Saturated | 87.8% | 25 | 2026-06-02 | independent | Source ↗ |
| LiveBench (Continuous) | Active | 71.3% | 94 | 2025-02-01 | independent | Source ↗ |
Local Hardware Execution (Ollama)Hardware Compatible
# 1. Pull and execute model locally with Ollama
ollama run tencent-hy-mt2-7b
# 2. Or invoke via local OpenAI-compatible endpoint
curl http://localhost:11434/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "tencent-hy-mt2-7b",
"messages": [{"role": "user", "content": "Analyze reasoning chains on AIME 2026"}]
}'Evaluation & Sourcing Notes
Scores listed for Tencent: Hy-MT2-7B represent verified evaluations extracted from official research papers, independent evaluation suites (HELM, LMSYS, OpenCompass, LiveBench), and verified audit reports.
All benchmarks marked as saturated or deprecated reflect historical performance where the benchmark no longer provides active discriminative power.