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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

27
general
57
coding
90
reasoning
64
science
70
agents
0
multimodal

रैंकिंग

बेंचमार्क स्कोर (LLM Stats)

(LLM Stats (zeroeval))

Agents

WildClawBench53.6%स्वयं
Toolathlon48.5%स्वयं

Chemistry

SuperChem54.9%स्वयं

Code

Claw-Eval68.5%स्वयं
SkillsBench55.3%स्वयं
NL2Repo45.6%स्वयं
DeepSWE28.0%स्वयं
CL-bench23.8%स्वयं
CL-bench (Life)17.0%स्वयं

Math

USAMO 202630.24 / 42स्वयं
IMO-AnswerBench90.0%स्वयं
ArXivMath52.2%स्वयं
MathArena Apex38.7%स्वयं
HorizonMath7.1%स्वयं

Physics

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

Reasoning

GPQANYU + Cohere + Anthropic (2023)90.4%स्वयं
BrowseCompOpenAI (2025)84.2%स्वयं
MCP Atlas79.1%स्वयं
SWE-Bench Verified78.0%स्वयं
SWE-bench Multilingual75.8%स्वयं
FrontierScience Olympiad74.8%स्वयं
AA-LCR73.4%स्वयं
Terminal-Bench 2.171.7%स्वयं
SWE-Bench ProPrinceton NLP (2024)57.9%स्वयं
Humanity's Last Exam (with tools, text-only)53.2%स्वयं
Humanity's Last Exam (no tools, text-only)47.0%स्वयं
APEX-Agents25.6%स्वयं
FrontierScience Research21.3%स्वयं

Search

DeepSearchQA91.0%स्वयं
WideSearch76.4%स्वयं

AA मूल्यांकन सूचकांक

(Artificial Analysis)
Gpqa(NYU + Cohere + Anthropic (2023))
89.7
Lcr(Artificial Analysis)
79.0
Terminalbench V2 1
64.4
Coding Index(Artificial Analysis)
58.8
Scicode(UIUC + Argonne National Lab (2024))
48.6
Hle(Center for AI Safety + Scale AI (2025))
33.5
Intelligence Index(Artificial Analysis)
25.3
Tau Banking
22.9

LLM Stats श्रेणी स्कोर

(LLM Stats (zeroeval))
Math
4
Reasoning
2
General
2
Biology
90
Physics
80
Search
80
Frontend Development
80
Long Context
70
Chemistry
70
Tool Calling
70
Science
60
Agents
60
Code
60
Knowledge
50
Coding
50

मूल्य निर्धारण

इनपुट मूल्य$0.136 / 1M टोकन
आउटपुट मूल्य$0.555 / 1M टोकन
मिश्रित मूल्य (3:1)$0.241 / 1M टोकन
कैश पठन मूल्य$0.033 / 1M टोकन

गति

टोकन/सेकंड96.0
पहले टोकन में देरी1.99s
पहले उत्तर में देरी22.83s

प्रदाता मूल्य रैंकिंग

प्रदाता मूल्य रैंकिंग

14 प्रदाता

सबसे सस्ता: DeepInfraसबसे महंगा: OrcaRouter
प्रदाताइनपुटआउटपुट
1DeepInfraसबसे सस्ता
$0
$0
2NanoGPT
$0.066
$0.26
3Kilo Gateway
$0.13
$0.53
4OpenRouter
$0.132
$0.528
5DevPass (LLM Gateway)
$0.132
$0.528
6LLM Gateway
$0.132
$0.528
7Tencentप्राथमिक
$0.136
$0.555
8OpenCode Go
$0.14
$0.58
9Requesty
$0.14
$0.58
10Vercel AI Gateway
$0.14
$0.58
11Jalapeno Cloud
$0.14
$0.58
12AIHubMix
$0.1562
$0.6248
13CrossModel
$0.16
$0.64
14OrcaRouter
$0.18
$0.59

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

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