ApX 标志ApX 标志

趋近智

GPT-5.2 High

参数

-

上下文长度

400K

模态

Text

架构

Dense

许可证

Proprietary

发布日期

13 Nov 2025

训练数据截止日期

Aug 2025

技术规格

注意力结构

Multi-Head Attention

隐藏维度大小

-

层数

-

注意力头

-

键值头

-

激活函数

-

归一化

-

位置嵌入

Absolute Position Embedding

GPT-5.2 High

GPT-5.2 High is a specialized iteration within the GPT-5 family, engineered for high-precision technical reasoning and long-context processing. This model variant is designed for applications requiring advanced cognitive processing and consistent adherence to complex instructions. It serves as a sophisticated engine for tasks that involve multi-step logic, detailed technical synthesis, and the management of extensive information flows within a single session.

Technically, the model is built upon a dense transformer framework, which ensures consistent parameter utilization across its operational range. It integrates multi-head attention with absolute position embeddings, supporting a 400,000-token context window that enables the ingestion of extensive data structures. A primary technical advancement is the implementation of adjustable reasoning effort levels, allowing the system to allocate additional computation during inference to resolve non-trivial logic and mathematical problems.

In professional environments, GPT-5.2 High is frequently utilized for software engineering, mathematical verification, and automated document analysis. The model is capable of handling agentic tasks that require reliable tool-calling and long-range planning. Its high accuracy in quantitative reasoning makes it a suitable choice for scientific and financial applications where high-fidelity generation and logical consistency are essential requirements.

关于 GPT-5

OpenAI's latest generation of language models featuring advanced reasoning capabilities, extended context windows up to 400K tokens, and specialized variants for coding, general intelligence, and efficiency. GPT-5 series introduces improved thinking modes, superior performance across benchmarks, and variants optimized for different use cases from high-capacity Pro models to efficient Nano models. Features native multimodal understanding, enhanced mathematical reasoning, and state-of-the-art coding abilities through Codex variants.


其他 GPT-5 模型

评估基准

排名

#7

基准分数排名

0.93

🥇

1

Graduate-Level QA

GPQA

0.93

🥇

1

0.76

9

Agentic Coding

LiveBench Agentic

0.52

9

Web Development

WebDev Arena

1440

9

排名

排名

#7

编程排名

#4

模型透明度

总分

F

32 / 100