關於Hy3-preview
Hy3 preview is a 295B-parameter Mixture-of-Experts (MoE) language model from Tencent Hunyuan, built for production-grade agent workloads. With only 21B parameters activated per token and native 256K context support, it handles complex tasks like cross-file code refactoring, long-document analysis, and multi-step tool use, rather than just generating fluent dialogue. Hy3 scores near state-of-the-art on SWE-bench Verified and advanced STEM benchmarks, while offering three inference modes (no_think, think_low, think_high) to dynamically trade off latency and reasoning depth. Its sparse activation architecture delivers competitive intelligence at a significantly lower token cost.
Leverage Hy3-preview’s 295B-parameter MoE architecture and 256K context for production-grade agentic workflows and deep reasoning.
Repository-Scale Refactoring
Execute complex architectural changes across massive codebases using native 256K context support.
Use Case Example:
"Migrated a distributed Go backend from REST to gRPC, updating service definitions and client libraries across 40+ repositories in a single pass."
Autonomous DevOps Agents
Power agents that navigate environments, use multi-step tools, and solve infrastructure issues autonomously.
Use Case Example:
"Deployed an agent to resolve a memory leak in a Rust-based embedded system by analyzing core dumps and applying a validated firmware patch."
PhD-Level STEM Reasoning
Tackle advanced scientific challenges in math and biology using high-depth reasoning modes.
Use Case Example:
"Formulated a formal proof for a fluid dynamics theorem using 'think_high' mode to validate complex boundary conditions for a research paper."
Intelligent Document Auditing
Analyze lengthy technical or legal documents to detect logical gaps and hidden risks with high precision.
Use Case Example:
"Scanned a 150-page semiconductor schematic and its manual to identify a power-sequencing logic error before the fabrication phase."
元數據
規格
狀態
Deprecated
架構
Mixture-of-Experts
經過校準的
否
專家並行
是
總參數
80B
啟用的參數
21B
推理
否
精度
FP8
上下文長度
262K
最大輸出長度
與其他模型比較
看看這個模型與其他模型的對比如何。
Tencent
chat
Hunyuan-MT-7B
總上下文：
33K
最大輸出：
33K
輸入：
$
0.0
/ M Tokens
輸出：
$
0.0
/ M Tokens
Tencent
chat
Hunyuan-A13B-Instruct
總上下文：
131K
最大輸出：
131K
輸入：
$
0.14
/ M Tokens
輸出：
$
0.57
/ M Tokens
Tencent
chat
Hy3
總上下文：
262K
最大輸出：
262K
輸入：
$
0.132
/ M Tokens
輸出：
$
0.528
/ M Tokens
Tencent
chat
Hy3-preview
總上下文：
262K
最大輸出：
輸入：
$
0.066
/ M Tokens
輸出：
$
0.26
/ M Tokens
