Tencent

Hunyuan-3-preview

tencent/hy3-preview
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

混元 Hy3 preview 面向 Agent 工作负载设计,采用 295B/21B 激活的 MoE 架构。在同一个模型内提供 no_think(极速响应)、think_low(快速思考)、think_high(深度推理)三档模式,适配从高频交互到复杂工程任务的不同延迟与深度需求。在 SWE-bench Verified 等代码基准上接近当前最强水平,256K 上下文支持跨文件代码重构与长文档分析。适合需要可靠任务完成度、同时对推理成本敏感的开发者。

Input / output modalities
Not provided to Not provided
Reference input / output price
Input¥1.2Output¥4per 1M tokens
Context window
256K
Added to catalog
Apr 23, 2026

Providers and pricing

ProviderInput lengthInput /MOutput /MCached /MContextMax outputDetails
Tencent≤ 16K¥1.2¥4¥0.4256K128K
16K – 32K¥1.6¥6.4¥0.6
≥ 32K¥2¥8¥0.8

Tencent

Latency
1.54s
Throughput
90 tokens/s
Context
256K

Pricing

Input
¥1.2/M tokens
Output
¥4/M tokens
Cached
¥0.4/M tokens

Tiered pricing

Pricing varies by input token range.

0–16K Token

Input tier
¥1.2/M tokens
Output tier
¥4/M tokens
Cached tier
¥0.4/M tokens

16K–32K Token

Input tier
¥1.6/M tokens
Output tier
¥6.4/M tokens
Cached tier
¥0.6/M tokens

32K–∞ Token

Input tier
¥2/M tokens
Output tier
¥8/M tokens
Cached tier
¥0.8/M tokens

Specifications

Context
256K
Max output
128K
Supported APIs
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

Hunyuan-3-preview code examples and API guide

Modelmesh normalizes requests and responses across service providers behind one consistent API.

Modelmesh provides an OpenAI-compatible Completion API for more than 300 models and service providers. Call it directly, through the OpenAI SDK, or with supported third-party SDKs.

Modelmesh-specific request headers in these examples are optional. When supplied, your application can appear on the Modelmesh rankings.

Supported endpointsSelect an endpoint to switch the example below.
/v1/chat/completions
from openai import OpenAI API_KEY = "$SSY_API_KEY" client = OpenAI( base_url="https://router.shengsuanyun.com/api/v1", api_key=API_KEY, ) try: completion = client.chat.completions.create( model="tencent/hy3-preview", messages=[{"role": "user", "content": "Which number is larger, 9.11 or 9.8?"}], temperature=0.6, top_p=0.7, stream=True, ) response_text = "" for chunk in completion: if chunk.choices and chunk.choices[0].delta.content is not None: content = chunk.choices[0].delta.content print(content, end="", flush=True) response_text += content except Exception as error: print(f"Request failed: {error}")
                
              

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