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Introducing Hy4 Preview

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Introducing Hy4 Preview

TL;DR · AI 摘要

腾讯推出Hy4模型，参数达770B，支持1M token上下文，推理模式仅high和no_think。

核心要点

  • Hy4模型总参数770B，活跃参数49B，上下文窗口达1M token
  • 推理模式仅支持high（默认）和no_think两种选项
  • 相比Hy3模型，参数规模提升超160%，上下文窗口扩大40倍

结构提纲

按章节快速跳转。

  1. §引言

    介绍腾讯新推出的Hy4大语言模型及其参数规模。

  2. ·模型参数对比

    详细对比Hy4与Hy3在参数量、上下文窗口等指标的差异。

  3. ›推理机制分析

    解析Hy4的reasoning_effort参数及其实现代码逻辑。

  4. ·应用示例

    通过生成SVG图像案例展示模型推理过程与输出特征。

  5. ›技术局限性

    指出模型推理文本存在语法截断现象以优化效率。

思维导图

用一张图看清主题之间的关系。

查看大纲文本（无障碍 / 无 JS 友好）
  • Hy4模型发布
    • 核心参数
      • 总参数770B
      • 活跃参数49B
      • 上下文窗口1M token
    • 技术特性
      • 推理模式：high/no_think
      • Hugging Face 1.56TB数据
      • 推理文本语法优化

金句 / Highlights

值得收藏与分享的关键句。

#Hy4#腾讯#LLM#Hugging Face#AI模型
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Introducing Hy4 Preview

Simon Willison’s Weblog

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29th August 2026 - Link Blog

Introducing Hy4 Preview . New open weight text input (no vision) LLM from Chinese company Tencent today: 770B total parameters, 49B active parameters, 1M token context window, 1.56TB on Hugging Face .

This is a big size increase from their previous Hy3 in July, which was 295B, 21B active, 256,000 context, 598GB.

I recently started using model chat templates to better understand their capabilities. Here's Hy4's chat_template.jinja on Hugging Face, which includes this section:

code
{%
-
if
not
reasoning_effort
is
defined
%}
{%
-
set
reasoning_effort
=
'high'
%}
{%
-
elif
reasoning_effort
not
in
[
'high'
,
'no_think'
]
%}
{%
-
if
reasoning_effort
is
none
%}
{{- raise_exception('reasoning_effort error : None, should be no_think/high') }}
{%
-
else
%}
{{- raise_exception('reasoning_effort error : ' + reasoning_effort + ', should be no_think/high') }}
{%
-
endif
%}
{%
-
endif
%}

So it looks like there are just two reasoning effort levels: "high" (the default) and "no_think" (reason by disabled).

I tried my "Generate an SVG of a pelican riding a bicycle" prompt with the default high reasoning via OpenRouter and got this :

Quoting the reasoning trace:

[...] Let's maybe add a helmet? It could improve riding theme, but may obscure head. Maybe a small cycling cap or helmet? The user didn't ask; can add red helmet? Might be cute. But pelican with big beak; a helmet might obscure. Better maybe no. Maybe add sunglasses? no. Maybe add water? no.

It's interesting how the reasoning trace uses slightly truncated English, presumably because perfect grammar isn't useful or token efficient for hidden reasoning text.

Posted

29th August 2026

at 11:53 pm

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This is a link post by Simon Willison, posted on 29th August 2026 .

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