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Hy3

Tencent開源權重Apache 2.0 · 商用許可

描述

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.

發布日期
2026-07-06
參數規模
295.0B
上下文長度
262K
支援模態
text

能力雷達圖

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

排行榜排名

領域#排名分數來源
智慧體能力模型榜51
52.0
LS
程式碼能力榜57
80.0
AA
通用能力榜128
66.0
AA
科學能力61
78.0
AA

基準測試分數 (LLM Stats)

(LLM Stats (zeroeval))

Agents

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

Biology

GPQANYU + Cohere + Anthropic (2023)90.4%自報

Chemistry

SuperChem54.9%自報

Code

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

Long Context

AA-LCR73.4%自報

Math

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

Physics

PHYBench77.4%自報
CMT-Benchmark37.9%自報

Reasoning

FrontierScience Research21.3%自報

AA 評測指數

(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 分類評分

(LLM Stats (zeroeval))
Math
6
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

定價

輸入價格$0.136 / 1M tokens
輸出價格$0.557 / 1M tokens
混合價格(3:1)$0.241 / 1M tokens
快取讀取價格$0.033 / 1M tokens

速度

Tokens/秒68.0
首Token延遲1.81s
首回答延遲31.22s

供應商價格排行

供應商價格排行

8 個供應商

最便宜: NanoGPT最貴: CrossModel
供應商輸入輸出
1NanoGPT最便宜
$0.066
$0.26
2OpenRouter
$0.132
$0.528
3Tencent主要
$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

比較該模型在不同 API 供應商之間的定價。

外部連結