Parameters
-
Context Length
400K
Modality
Text
Architecture
Dense
License
Proprietary
Release Date
13 Nov 2025
Knowledge Cutoff
Aug 2025
Attention Structure
Multi-Head Attention
Hidden Dimension Size
-
Number of Layers
-
Attention Heads
-
Key-Value Heads
-
Activation Function
-
Normalization
-
Position Embedding
Absolute Position Embedding
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.
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.
Rank
#7
| Benchmark | Score | Rank |
|---|---|---|
Mathematics LiveBench Mathematics | 0.93 | 🥇 1 |
Graduate-Level QA GPQA | 0.93 | 🥇 1 |
Coding LiveBench Coding | 0.76 | 9 |
Agentic Coding LiveBench Agentic | 0.52 | 9 |
Web Development WebDev Arena | 1440 | ⭐ 9 |
Overall Rank
#7
Coding Rank
#4