OpenAI identifies GPT-5.1 Codex as a specialized variant of the GPT-5.1 foundation model. While the description mentions a 'dense transformer configuration' and a 'session compaction mechanism' for long-horizon tasks, there is no public technical documentation detailing the specific architectural modifications, layer counts, or the exact pretraining/fine-tuning methodology. The 'compaction' process is described functionally but lacks technical implementation details in public papers.

Information regarding the training data is extremely vague. Official sources state it was trained on 'real-world software engineering tasks' and 'agentic workflows,' with a knowledge cutoff of September 30, 2024. However, there is no disclosure of specific data sources, percentage breakdowns (e.g., code vs. text), or the methodology for filtering and cleaning the dataset. The use of synthetic data or specific repositories is not documented.

The model uses a tokenizer consistent with the GPT-5 series, supporting a context window of up to 400,000 tokens. While the tokenizer is accessible via the API for practical use, OpenAI has not released a dedicated technical specification or vocabulary breakdown for the 5.1 Codex variant specifically. Vocabulary size and tokenization efficiency for specialized code syntax are not publicly verified.

The parameter count for GPT-5.1 Codex is officially 'Unknown.' While it is described as a 'dense' architecture, no specific figures for total or active parameters are provided. This lack of transparency makes it impossible to verify the model's efficiency or density relative to its performance.

There is zero public information regarding the compute resources used to train GPT-5.1 Codex. No data on GPU/TPU hours, hardware specifications, training duration, or carbon footprint has been disclosed by OpenAI.

OpenAI provides scores for SWE-Bench Verified (76.3% - 77.9%) and SWE-Lancer IC (79.9%), but the exact evaluation harnesses, prompts, and few-shot examples used to achieve these results are not fully public. Third-party evaluations from METR and Artificial Analysis exist, but they often rely on API access rather than a reproducible, open-source evaluation suite provided by the developer.

The model consistently identifies itself as a specialized coding variant within the GPT-5.1 family. It maintains version awareness through the API (e.g., gpt-5.1-codex) and does not exhibit the identity confusion seen in some earlier models. It is transparent about its role as an agentic tool rather than a general-purpose assistant.

The model is released under a strictly proprietary license. While the terms for API usage and commercial integration (e.g., via GitHub Copilot) are clear, there is no transparency regarding the underlying weights or code. The license is restrictive and does not allow for independent auditing or derivative works.

As a closed-source API-only model, there is no documentation on the VRAM or hardware requirements for local deployment. Guidance is limited to API-side context management and 'compaction' behavior. While the 400k context window is documented, the memory scaling and quantization trade-offs remain internal to OpenAI's infrastructure.

OpenAI uses semantic-like naming (5.1) and provides snapshots to mitigate drift. However, changelogs are often high-level and lack granular detail on weight updates or subtle behavioral shifts. The transition period for legacy models (3 months) is documented, but silent updates to the 'thinking' engine can still occur without detailed notice.