GPT-5.2

OpenAI identifies GPT-5.2 as a 'dense transformer' model within the GPT-5 lineage, but provides no technical documentation regarding its specific layer count, hidden dimensions, or attention mechanisms. While it introduces an 'adaptive reasoning mechanism' and 'dynamic tier routing,' these are described in functional marketing terms rather than architectural specifications. No peer-reviewed paper or technical report detailing the model's construction has been released.

Data sources are entirely undisclosed. Official communications mention 'multi-modal datasets' and an 'advanced knowledge cutoff' of August 31, 2025, but provide no breakdown of data types (e.g., web, code, books), filtering methodologies, or cleaning procedures. The company relies on vague claims of 'high-quality' and 'diverse' data without providing verifiable evidence or sample data access.

The model utilizes the 'o200k_base' (and the 'o200k_harmony' variant for agentic tasks) tokenizer, which is publicly accessible via the 'tiktoken' library. The vocabulary size is approximately 200,000 tokens, and the tokenizer's behavior is verifiable through OpenAI's public Tokenizer tool and API. Documentation on the training data alignment for the tokenizer itself remains limited, but the tool is functionally transparent.

OpenAI has not disclosed the total or active parameter count for GPT-5.2. While the model is described as 'dense,' there is no public information regarding its scale. Third-party estimates exist but are unverifiable. The lack of official data on parameter density or architectural breakdown results in a near-zero score for this criterion.

There is zero official disclosure regarding the compute resources used to train GPT-5.2. No information is provided regarding GPU/TPU hours, hardware specifications, training duration, or the model's carbon footprint. Independent researchers have attempted to estimate these values, but OpenAI provides no verifiable data to confirm or refute these claims.

While OpenAI provides scores for several benchmarks (GDPval, SWE-Bench Pro, ARC-AGI-2), it does not release the evaluation code, exact prompts, or few-shot examples used to achieve these results. Some benchmarks, like GDPval and MRCRv2, are internal or proprietary OpenAI evaluations, making independent third-party verification and reproduction impossible.

The model consistently identifies itself as GPT-5.2 and demonstrates awareness of its versioning and specific capabilities, such as the 'Thinking' and 'Pro' modes. It maintains a coherent identity across different interfaces (ChatGPT and API) and accurately reflects its knowledge cutoff and context window limitations in system-level interactions.

GPT-5.2 is a proprietary model with no open-source or open-weights availability. The license is strictly commercial and governed by OpenAI's Terms of Service. While the terms for API usage and commercial output are stated, the underlying model weights and code are entirely restricted, scoring low on the transparency scale for licensing of the model itself.

As a closed-source API-based model, OpenAI provides no documentation on the hardware requirements to run the model locally. There is no guidance on VRAM needs, quantization tradeoffs, or memory scaling for its 400,000-token context window. Users are entirely dependent on OpenAI's managed infrastructure with no transparency into the model's operational efficiency.

OpenAI uses date-based versioning for its API (e.g., 'gpt-5.2-2025-12-11') and maintains a basic changelog on its website. However, the company has a history of 'silent updates' and model drift where performance characteristics change without detailed technical documentation, making it difficult for developers to track specific behavioral shifts over time.