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Hy3

TencentOpen WeightApache 2.0 · Uso Comercial

Descripción

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.

Fecha de lanzamiento
2026-07-06
Parámetros
295.0B
Longitud del contexto
262K
Modalidades
text

Radar de capacidades

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

Rankings

Dominio#PosiciónPuntuaciónFuente
Capacidad agéntica51
52.0
LS
Ranking de codificación57
80.0
AA
Ranking general128
66.0
AA
Ciencia61
78.0
AA

Puntuaciones de benchmarks (LLM Stats)

(LLM Stats (zeroeval))

Agents

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

Biology

GPQANYU + Cohere + Anthropic (2023)90.4%Aut.

Chemistry

SuperChem54.9%Aut.

Code

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

Long Context

AA-LCR73.4%Aut.

Math

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

Physics

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

Reasoning

FrontierScience Research21.3%Aut.

Índices de evaluación AA

(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

Puntuaciones por categoría LLM Stats

(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

Precios

Precio de entrada$0.136 / 1M tokens
Precio de salida$0.557 / 1M tokens
Precio mixto (3:1)$0.241 / 1M tokens
Precio de lectura caché$0.033 / 1M tokens

Velocidad

Tokens/seg68.0
Retraso del primer token1.81s
Tiempo hasta la respuesta31.22s

Ranking de Precios por Proveedor

Ranking de Precios por Proveedor

8 proveedores

Más barato: NanoGPTMás caro: CrossModel
ProveedorEntradaSalida
1NanoGPTMás barato
$0.066
$0.26
2OpenRouter
$0.132
$0.528
3TencentPRINCIPAL
$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

Comparar precios entre diferentes proveedores de API para este modelo.

Fuentes externas