OpenAI provides only high-level conceptual descriptions of the GPT-5.2 architecture, identifying it as a 'dense transformer' with Multi-Head Attention (MHA). There is no public documentation detailing the specific layer counts, attention heads, or the exact nature of the 'adaptive reasoning mechanism' mentioned in marketing materials. While the model is part of a named family, the technical methodology for its 'No Thinking' (Instant) optimization—whether achieved through distillation, pruning, or specific architectural changes—remains entirely undisclosed and proprietary.

Information regarding the training data is limited to vague marketing descriptors such as 'multi-modal datasets' and 'high-quality data.' OpenAI has not disclosed specific data sources, the proportions of web, code, or academic data, nor the methodology for filtering and cleaning. The only verifiable detail is a knowledge cutoff date of August 31, 2025. Claims of 'advanced knowledge' and 'professional-grade' data are unverifiable assertions without a public breakdown or sample data availability.

The tokenizer is accessible via the OpenAI API and compatible libraries (e.g., tiktoken), allowing for some empirical verification of vocabulary size and tokenization behavior. However, official documentation specifically detailing the training data alignment for the GPT-5.2 series tokenizer or any architectural modifications to the tokenization process for the 400K context window is absent. It scores moderately because it is functional and testable, but lacks comprehensive technical disclosure.

OpenAI maintains a strict policy of non-disclosure regarding parameter counts for the GPT-5.2 family. There is no verifiable information on the total parameters or the active parameters for the 'No Thinking' variant. Marketing materials focus on 'intelligence tiers' rather than technical density, leaving the model's scale entirely to speculation.

No information has been released regarding the compute resources used to train GPT-5.2. There are no disclosures on GPU/TPU hours, hardware specifications, training duration, or the environmental impact/carbon footprint of the model's development. This is a total lack of transparency typical of proprietary frontier models.

While OpenAI reports scores on several benchmarks (e.g., SWE-Bench Pro, GPQA Diamond, GDPval), the evaluation code, exact prompts, and few-shot examples used to achieve these results are not public. Third-party verification is limited to independent leaderboards like LiveBench, which show different results than official claims. The use of 'internal benchmarks' like GDPval, which lack public validation sets, further hinders reproducibility.

The model consistently identifies itself as a member of the GPT-5 family in API responses and system prompts. It correctly distinguishes its capabilities (e.g., lack of internal chain-of-thought in the 'No Thinking' variant) when queried. Versioning is tracked via specific API identifiers (e.g., gpt-5.2-2025-12-11), providing a clear and consistent identity for users and developers.

The model is governed by a highly restrictive proprietary license. While the terms of use are publicly accessible, they include broad prohibitions against reverse engineering and the use of output to train competing models. There is no open-source or open-weights component, and the license terms are subject to change at OpenAI's discretion, offering minimal transparency for derivative works or long-term commercial stability.

As a closed-source API-based model, there is no official documentation on the VRAM or hardware requirements for local deployment. Guidance is limited to API-side metrics like latency and 'time-to-first-token.' While some third-party analysis exists regarding API costs and token efficiency, the actual computational footprint required to run the model remains a 'black box.'

OpenAI uses dated snapshots (e.g., gpt-5.2-2025-12-11) which allows for some level of version tracking. However, the 'latest' pointers (e.g., gpt-5.2-chat-latest) are subject to silent updates and behavioral drift. There is no detailed public changelog describing specific weight updates or fine-tuning adjustments, making it difficult for developers to audit changes in model behavior over time.