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Parameters
-
Context Length
400K
Modality
Text
Architecture
Dense
License
Proprietary
Release Date
13 Nov 2025
Knowledge Cutoff
Aug 2025
Attention
Attention Structure
Multi-Head Attention
Attention Heads
-
Key-Value Heads
-
Attention Head Dimension
-
Position Embedding
Absolute Position Embedding
RoPE Theta
-
Sliding Window Attention
-
Sliding Window Size
-
Normalization
-
Activation Function
-
Dimensions
Hidden Dimension Size
-
Number of Layers
-
FFN Intermediate Size (Dense)
-
Multi-Token Prediction Heads
-
Tokenizer
Vocabulary Size
-
GPT-5.2 No Thinking represents the latency-optimized configuration of OpenAI's flagship series, specifically engineered to provide immediate responses by bypassing the internal chain-of-thought processing characteristic of the Thinking and Pro variants. As part of a larger model ecosystem designed for professional knowledge work and agentic workflows, this variant balances high-fidelity output with the computational efficiency required for real-time interactions. It supports the same massive input capacity as the primary series, allowing for the ingestion of substantial codebases and technical documentation in a single inference pass.
The underlying architecture utilizes a dense transformer configuration with Multi-Head Attention (MHA) and absolute position embeddings. This design enables precise handling of long-context dependencies without the overhead of dynamic expert routing. The model is particularly optimized for industrial software engineering and structured data tasks, featuring advanced tool-calling capabilities through the Responses API. It includes technical refinements for context management, such as a specialized compaction endpoint that compresses lengthy conversational history to prevent context window saturation while maintaining semantic integrity.
In practical application, this model serves as a high-throughput engine for developers building responsive tools where user experience depends on minimal time-to-first-token. While it shares the expansive knowledge cutoff and multimodal input capabilities of the GPT-5.2 family, its lack of an explicit reasoning phase makes it most effective for tasks where the logical path is well-defined or provided within the prompt. It is frequently deployed in scenarios involving large-scale code refactoring, technical document summarization, and interactive agentic systems where speed and reliability are prioritized over deep, multi-step deliberation.
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
#89
| Benchmark | Score | Rank |
|---|---|---|
Coding Aider Coding | 0.81 | ⭐ 4 |
Coding LiveBench Coding | 0.76 | 14 |
Professional Knowledge MMLU Pro | 0.86 | 14 |
Agentic Coding LiveBench Agentic | 0.40 | 31 |
Data Analysis LiveBench Data Analysis | 0.48 | 43 |
Reasoning LiveBench Reasoning | 0.43 | 49 |
Mathematics LiveBench Mathematics | 0.58 | 52 |
Overall Rank
#89
Coding Rank
#9
Total Score
F
27 / 100
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