Tencent model product
Hy3 is a 295B-parameter Mixture-of-Experts (MoE) model with 21B active parameters and a 3.8B MTP layer, developed by the Tencent Hy Team. Following the Hy3 Preview launch in late April, the team scaled up post-training with higher-quality data and RL, gathering feedback from 50+ products. Hy3 outperforms similar-size models and rivals flagship open-source models with 2-5x the parameters, with strong gains in reasoning, agentic, and long-context tasks. It uses 80 layers (plus 1 MTP layer), 64 GQA attention heads (8 KV heads, head dim 128), a 4096 hidden size, 192 experts with top-8 activated, a 256K context window, and BF16 precision. Hy3 is a hybrid-thinking model supporting configurable reasoning effort (no_think, low, high), and emphasizes production-grade tool-call and output-format stability, reduced hallucination, and reliable multi-turn intent tracking.
Updated Aug 10, 2026. Default version: Hy3
Structured fields from the published default version.
This profile uses the latest version under this unique model that has a calculated LLMBoard score. Arena and price are excluded.
Published benchmark records for the scored version Hy3.
| AA-LCR | 0.7 | 1 | 15 | 100.0% | C | |
| CL-bench | 0.2 | 1 | 2 | 100.0% | C | |
| CL-bench (Life) | 0.2 | 1 | 1 | 100.0% | C | |
| CMT-Benchmark | 0.4 | 1 | 1 | 100.0% | C | |
| HorizonMath | 0.1 | 1 | 3 | 100.0% | C | |
| Humanity's Last Exam (no tools, text-only) | 0.5 | 1 | 1 | 100.0% | C | |
| PHYBench | 0.8 | 1 | 1 | 100.0% | C | |
| ArXivMath | 0.5 | 2 | 2 | 0.0% | C | |
| Humanity's Last Exam (with tools, text-only) | 0.5 | 2 | 2 | 0.0% | C | |
| FrontierScience Olympiad | 0.7 | 3 | 3 | 0.0% | C | |
| IMO-AnswerBench | 0.9 | 3 | 19 | 88.9% | C | |
| SkillsBench | 0.6 | 3 | 7 | 66.7% | C | |
| SuperChem | 0.5 | 3 | 3 | 0.0% | C | |
| USAMO 2026 | 30.2 | 3 | 3 | 0.0% | C | |
| WideSearch | 0.8 | 3 | 9 | 75.0% | C | |
| WildClawBench | 0.5 | 3 | 4 | 33.3% | C | |
| Claw-Eval | 0.7 | 4 | 13 | 75.0% | C | |
| DeepSearchQA | 0.9 | 4 | 8 | 57.1% | C | |
| FrontierScience Research | 0.2 | 4 | 4 | 0.0% | C | |
| MathArena Apex | 0.4 | 4 | 7 | 50.0% | C | |
| NL2Repo | 0.5 | 6 | 14 | 61.5% | C | |
| APEX-Agents | 0.3 | 7 | 7 | 0.0% | C | |
| MCP Atlas | 0.8 | 7 | 30 | 79.3% | C | |
| DeepSWE | 0.3 | 9 | 10 | 11.1% | C | |
| SWE-bench Multilingual | 0.8 | 9 | 34 | 75.8% | C | |
| Terminal-Bench 2.1 | 0.7 | 12 | 17 | 31.3% | C | |
| BrowseComp | 0.8 | 14 | 58 | 77.2% | C | |
| Toolathlon | 0.5 | 17 | 31 | 46.7% | C | |
| SWE-Bench Pro | 0.6 | 19 | 44 | 58.1% | C | |
| SWE-Bench Verified | 0.8 | 20 | 104 | 81.5% | C | |
| GPQA | 0.9 | 21 | 233 | 91.4% | C |
Preference and agent-evaluation signals from published Arena datasets.
| webdev | webdev-react | 15 | 1537.2 | 1,244 | N/A | |
| text factuality | industry life and physical and social science | 18 | 1496.8 | 593 | N/A | |
| text style control | industry mathematical | 18 | 1493.9 | 210 | N/A | |
| agent praise complaint | overall | 19 | 0.0 | N/A | 2K | |
| text style control | industry life and physical and social science | 20 | 1496.0 | 608 | N/A | |
| text | industry mathematical | 23 | 1481.2 | 210 | N/A | |
| webdev | overall | 24 | 1521.9 | 1,729 | N/A | |
| webdev | webdev | 24 | 1521.9 | 1,729 | N/A | |
| text style control | math | 25 | 1479.3 | 201 | N/A | |
| agent bash recovery steps | overall | 28 | 0.0 | N/A | 12.6K | |
| text | math | 29 | 1470.3 | 201 | N/A | |
| agent | overall | 30 | -0.0 | N/A | 477.6K | |
| agent task outcome explicit | overall | 31 | -0.0 | N/A | 6.7K | |
| text style control | chinese | 32 | 1516.4 | 249 | N/A | |
| text | industry life and physical and social science | 34 | 1468.7 | 608 | N/A | |
| text style control | expert | 34 | 1494.7 | 396 | N/A | |
| text | chinese | 35 | 1505.1 | 249 | N/A | |
| agent steerability | overall | 39 | -0.1 | N/A | 8.1K | |
| agent tool hallucination | overall | 41 | -0.0 | N/A | 448.2K | |
| text | russian | 42 | 1448.8 | 415 | N/A | |
| text style control | russian | 45 | 1462.7 | 415 | N/A | |
| webdev | webdev-html | 46 | 1444.8 | 181 | N/A | |
| text | expert | 50 | 1465.0 | 396 | N/A | |
| text style control | industry medicine and healthcare | 53 | 1474.7 | 272 | N/A | |
| text style control | english | 54 | 1464.8 | 1,535 | N/A | |
| text style control | longer query | 55 | 1464.2 | 1,741 | N/A | |
| text | longer query | 56 | 1442.2 | 1,741 | N/A | |
| text factuality | coding | 56 | 1497.9 | 1,118 | N/A | |
| text style control | overall | 57 | 1453.0 | 3,941 | N/A | |
| text style control | coding | 57 | 1500.2 | 1,133 | N/A | |
| text style control | exclude ties | 57 | 1454.7 | 2,936 | N/A | |
| text style control | instruction following | 57 | 1443.9 | 1,375 | N/A | |
| text factuality | industry entertainment and sports and media | 58 | 1417.8 | 864 | N/A | |
| text factuality | industry software and it services | 58 | 1482.6 | 1,572 | N/A | |
| text factuality | creative writing | 59 | 1419.5 | 646 | N/A | |
| text style control | industry software and it services | 59 | 1488.4 | 1,595 | N/A | |
| text | instruction following | 60 | 1422.8 | 1,375 | N/A | |
| text | multi turn | 60 | 1445.1 | 649 | N/A | |
| text factuality | multi turn | 60 | 1457.8 | 615 | N/A | |
| text | coding | 61 | 1463.5 | 1,133 | N/A | |
| text factuality | longer query | 61 | 1456.6 | 1,721 | N/A | |
| text style control | creative writing | 61 | 1426.9 | 674 | N/A | |
| text style control | non english | 61 | 1438.7 | 2,406 | N/A | |
| text style control | hard prompts | 62 | 1471.6 | 2,648 | N/A | |
| text style control | multi turn | 62 | 1459.9 | 649 | N/A | |
| text | english | 63 | 1446.6 | 1,535 | N/A | |
| text | industry software and it services | 63 | 1459.0 | 1,595 | N/A | |
| text style control | industry business and management and financial operations | 63 | 1447.6 | 801 | N/A | |
| text | overall | 64 | 1437.6 | 3,941 | N/A | |
| text factuality | overall | 64 | 1445.0 | 3,941 | N/A | |
| text | creative writing | 65 | 1409.0 | 674 | N/A | |
| text | exclude ties | 65 | 1431.8 | 2,936 | N/A | |
| text | non english | 65 | 1425.9 | 2,406 | N/A | |
| text factuality | exclude ties | 65 | 1444.9 | 2,936 | N/A | |
| text style control | hard prompts english | 65 | 1474.5 | 1,021 | N/A | |
| text | industry medicine and healthcare | 66 | 1449.2 | 272 | N/A | |
| text factuality | english | 66 | 1453.7 | 1,504 | N/A | |
| text | hard prompts | 67 | 1443.8 | 2,648 | N/A | |
| text factuality | industry business and management and financial operations | 67 | 1444.2 | 766 | N/A | |
| text factuality | non english | 67 | 1433.1 | 2,406 | N/A | |
| text style control | industry entertainment and sports and media | 67 | 1415.0 | 879 | N/A | |
| text style control | industry writing and literature and language | 69 | 1424.2 | 983 | N/A | |
| text factuality | instruction following | 71 | 1435.2 | 1,347 | N/A | |
| text factuality | hard prompts | 72 | 1463.6 | 2,648 | N/A | |
| text factuality | hard prompts english | 72 | 1471.7 | 998 | N/A | |
| text | industry writing and literature and language | 75 | 1409.6 | 983 | N/A | |
| text | industry business and management and financial operations | 77 | 1424.2 | 801 | N/A | |
| text | industry entertainment and sports and media | 77 | 1401.2 | 879 | N/A | |
| text factuality | industry writing and literature and language | 80 | 1414.9 | 964 | N/A | |
| text | hard prompts english | 81 | 1443.7 | 1,021 | N/A | |
| text style control | industry legal and government | 93 | 1436.8 | 289 | N/A | |
| text | industry legal and government | 119 | 1413.8 | 289 | N/A |
Official vendor API PAYG pricing is summarized first. The table then lists individual provider offerings without treating their minimum as the official price.
| Tencent TokenHub | hy3 | global | N/A | N/A | 256K | |
| Tencent Token Plan | hy3 | global | N/A | N/A | 256K | |
| OpenCode Go | hy3 | global | $0.14 | $0.58 | 256K | |
| LLM Gateway | hy3 | global | $0.14 | $0.58 | 262.1K |
Official prices use only the vendor's configured official Provider and positive standard USD PAYG rates. Third-party offers remain explicitly labeled.
All published versions linked to this unique model. The score columns identify the version used by the current overall ranking.
| Hy3 | 80.1 | 295B | 256K | 64K | Yes | Apache 2.0 |
A concise description based on the published model registry.
Hy3 is a 295B-parameter Mixture-of-Experts (MoE) model with 21B active parameters and a 3.8B MTP layer, developed by the Tencent Hy Team. Following the Hy3 Preview launch in late April, the team scaled up post-training with higher-quality data and RL, gathering feedback from 50+ products. Hy3 outperforms similar-size models and rivals flagship open-source models with 2-5x the parameters, with strong gains in reasoning, agentic, and long-context tasks. It uses 80 layers (plus 1 MTP layer), 64 GQA attention heads (8 KV heads, head dim 128), a 4096 hidden size, 192 experts with top-8 activated, a 256K context window, and BF16 precision. Hy3 is a hybrid-thinking model supporting configurable reasoning effort (no_think, low, high), and emphasizes production-grade tool-call and output-format stability, reduced hallucination, and reliable multi-turn intent tracking.
Use the benchmark, Arena and pricing sections above as separate evidence. A missing field means the current data snapshot does not support that claim.
Data snapshot: 2026-08-07. Editorial model content is not available in the backend.
Open a comparison with the three ranked models immediately above and below this model.
Recommendations prioritize the same model type and family, then the closest published LLMBoard score.
Common questions about Hy3.
Hy3's default version was released on Jul 6, 2026.
No official standard PAYG price is currently available for Hy3. The lowest tracked third-party offer starts at $0.14 input and $0.58 output via OpenCode Go.
Hy3 is published under Tencent in the model registry.
The default version has a 256K token context window.
Yes. The default version is marked as open weight under Apache 2.0.
4 published provider offerings are linked to the default version.
Nearby ranked alternatives include Gemini 3 Pro, Seed 2.1 Turbo, GLM 5.2.