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Hy3

Tencentओपन वेटApache 2.0 · व्यावसायिक उपयोग

विवरण

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.

रिलीज़ तिथि
2026-07-06
पैरामीटर
295.0B
संदर्भ लंबाई
262K
मोडैलिटीज़
text

क्षमता रडार

40
general
57
coding
90
reasoning
64
science
70
agents
0
multimodal

रैंकिंग

बेंचमार्क स्कोर (LLM Stats)

(LLM Stats (zeroeval))

Agents

DeepSearchQA91.0%स्वयं
BrowseCompOpenAI (2025)84.2%स्वयं
MCP Atlas79.1%स्वयं
WideSearch76.4%स्वयं
Terminal-Bench 2.171.7%स्वयं
Claw-Eval68.5%स्वयं
SWE-Bench ProPrinceton NLP (2024)57.9%स्वयं
SkillsBench55.3%स्वयं
WildClawBench53.6%स्वयं
Toolathlon48.5%स्वयं
NL2Repo45.6%स्वयं
DeepSWE28.0%स्वयं
APEX-Agents25.6%स्वयं
CL-bench23.8%स्वयं
CL-bench (Life)17.0%स्वयं

Biology

GPQANYU + Cohere + Anthropic (2023)90.4%स्वयं

Chemistry

SuperChem54.9%स्वयं

Code

SWE-Bench Verified78.0%स्वयं
SWE-bench Multilingual75.8%स्वयं

Long Context

AA-LCR73.4%स्वयं

Math

USAMO 202630.24 / 42स्वयं
IMO-AnswerBench90.0%स्वयं
FrontierScience Olympiad74.8%स्वयं
Humanity's Last Exam (with tools, text-only)53.2%स्वयं
ArXivMath52.2%स्वयं
Humanity's Last Exam (no tools, text-only)47.0%स्वयं
MathArena Apex38.7%स्वयं
HorizonMath7.1%स्वयं

Physics

PHYBench77.4%स्वयं
CMT-Benchmark37.9%स्वयं

Reasoning

FrontierScience Research21.3%स्वयं

AA मूल्यांकन सूचकांक

(Artificial Analysis)
Coding Index(Artificial Analysis)
58.8
Intelligence Index(Artificial Analysis)
42.2
Gpqa(NYU + Cohere + Anthropic (2023))
0.9
Lcr(Artificial Analysis)
0.7
Terminalbench V2 1
0.6
Scicode(UIUC + Argonne National Lab (2024))
0.5
Hle(Center for AI Safety + Scale AI (2025))
0.3
Tau Banking
0.2

LLM Stats श्रेणी स्कोर

(LLM Stats (zeroeval))
Math
7
Reasoning
2
General
2
Physics
90
Biology
90
Search
80
Frontend Development
80
Long Context
70
Chemistry
70
Tool Calling
70
Agents
60
Code
60
Science
50
Knowledge
50
Coding
50

मूल्य निर्धारण

इनपुट मूल्य$0.136 / 1M टोकन
आउटपुट मूल्य$0.557 / 1M टोकन
मिश्रित मूल्य (3:1)$0.241 / 1M टोकन
कैश पठन मूल्य$0.033 / 1M टोकन

गति

टोकन/सेकंड64.2
पहले टोकन में देरी1.74s
पहले उत्तर में देरी32.88s

प्रदाता मूल्य रैंकिंग

प्रदाता मूल्य रैंकिंग

8 प्रदाता

सबसे सस्ता: NanoGPTसबसे महंगा: CrossModel
प्रदाताइनपुटआउटपुट
1NanoGPTसबसे सस्ता
$0.066
$0.26
2OpenRouter
$0.132
$0.528
3Tencentप्राथमिक
$0.136
$0.557
4OpenCode Go
$0.14
$0.58
5Kilo Gateway
$0.14
$0.58
6Vercel AI Gateway
$0.14
$0.58
7LLM Gateway
$0.14
$0.58
8CrossModel
$0.16
$0.64

इस मॉडल के लिए विभिन्न API प्रदाताओं के मूल्य निर्धारण की तुलना करें।

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