GPT-5.2 High

OpenAI identifies GPT-5.2 High as a 'dense transformer' model, a departure from the Mixture-of-Experts (MoE) architecture used in previous flagship models. However, there is no public technical paper or detailed documentation describing the specific architectural modifications, layer counts, or the 'adaptive reasoning mechanism' mentioned in marketing materials. The model is described as a successor to GPT-5.1, but the exact training methodology and pretraining procedures remain undisclosed proprietary information.

OpenAI provides only vague descriptions of the training data, claiming it includes 'multi-modal datasets' and an 'advanced knowledge cutoff.' There is no public breakdown of data sources (e.g., percentages of web, code, or books), no documentation on filtering or cleaning processes, and no disclosure regarding the use of synthetic data. The claim of 'high-quality data' is an unverifiable marketing assertion without technical specifics.

While the tokenizer is accessible via the OpenAI API and compatible with existing tiktoken libraries, there is no specific documentation for the GPT-5.2 variant's vocabulary size or training alignment. The model supports a 400,000-token context window, but the underlying tokenization approach for this specific iteration is not explicitly detailed beyond general compatibility with the GPT-5 family.

The parameter count for GPT-5.2 High is officially 'Unknown.' While third-party sources speculate on its size, OpenAI has not disclosed the total or active parameter counts. The transition to a 'dense' architecture is stated, but without specific numbers or an architectural breakdown, the claim lacks transparency and verification.

There is zero public information regarding the compute resources used to train GPT-5.2 High. OpenAI has not disclosed GPU/TPU hours, hardware specifications, training duration, or the carbon footprint associated with this model. These details are withheld for competitive reasons, resulting in a total lack of transparency.

OpenAI reports high scores on benchmarks like SWE-Bench Pro (55.6%) and AIME 2025 (100%), and some third-party verification exists via platforms like LMArena and SWE-bench.org. However, the exact prompts, few-shot examples, and evaluation code used for official claims are not public. The use of 'internal benchmarks' like GDPval, which lacks a public validation set, further hinders independent reproducibility.

The model consistently identifies itself as part of the GPT-5 family and is transparent about its 'reasoning effort' levels (e.g., high, xhigh). It maintains version awareness through API identifiers like 'gpt-5.2-pro.' While it occasionally exhibits confusion regarding its specific variant in complex sessions, it generally adheres to its defined identity and capabilities.

The model is under a strictly proprietary license with no access to weights or source code. While the terms of service for API use are clear, the lack of an open-source or open-weights option, combined with restrictive commercial terms, results in a low transparency score for licensing.

Hardware requirements are only relevant for API users in terms of latency and cost ($1.75/1M input, $14/1M output). There is no documentation regarding the VRAM or compute requirements for local deployment, as the model is not available for local hosting. Guidance on quantization tradeoffs or memory scaling for the 400k context window is absent.

OpenAI uses model identifiers like 'gpt-5.2-pro' and 'gpt-5.2-chat-latest,' but the release of GPT-5.2 only weeks after GPT-5.1 suggests a rapid, potentially unstable update cycle. There is no public changelog detailing specific weight updates or behavioral drift, and previous versions are often deprecated quickly, making it difficult for developers to maintain consistent performance.