GPT-5.3 Codex High

OpenAI identifies GPT-5.3 Codex High as a unified model merging the 'Codex' and 'GPT-5' training stacks. While it is explicitly described as a dense transformer architecture, technical documentation lacks specific details on architectural modifications beyond high-level descriptions of 'Enhanced Pre-Training Efficiency' (EPTE). The model's lineage is clear, but the exact methodology for merging the reasoning and coding branches remains proprietary.

OpenAI provides only vague descriptions of the training data, claiming it is 'carefully curated' and focused on 'modern development practices' and 'verified scientific papers.' There is no public breakdown of dataset proportions (e.g., web vs. code), no disclosure of specific data sources, and no detailed filtering methodology provided. Claims of 'high-quality' data are unverifiable marketing assertions.

The model is accessible via the Codex app, CLI, and API, allowing for empirical testing of tokenization. However, official documentation does not explicitly state the vocabulary size or provide a technical breakdown of the tokenizer's training alignment. While it is known to be more token-efficient than predecessors, the underlying tokenizer specifications (e.g., whether it uses a new 'o200k' variant or a specialized coding version) are not publicly documented.

The parameter count for GPT-5.3 Codex High is officially 'Unknown.' OpenAI uses marketing terms like 'cognitive density' and 'physically smaller' without providing any verifiable numbers for total or active parameters. This lack of disclosure makes it impossible to verify density claims or compare the model's efficiency against competitors on a parameter-adjusted basis.

OpenAI mentions the use of NVIDIA GB200 NVL72 systems for training and inference, but does not disclose the total GPU hours, energy consumption, or the specific carbon footprint of the training run. While general corporate sustainability claims exist, no model-specific environmental impact data or training cost estimates are provided for this variant.

OpenAI provides scores for several benchmarks including SWE-Bench Pro (56.8%), Terminal-Bench 2.0 (77.3%), and OSWorld-Verified (64.7%). However, the evaluation code and exact prompts used to achieve these results are not fully public. While third-party platforms like Artificial Analysis provide some verification, the lack of a complete, reproducible evaluation suite limits transparency.

The model consistently identifies itself as GPT-5.3 Codex and is transparent about its versioning through API identifiers (e.g., gpt-5.3-codex). It demonstrates awareness of its role as an agentic coding model. There are no documented instances of the model claiming a competitor's identity or denying its nature as an AI, though its self-description is limited to OpenAI-provided system prompts.

The model is governed by a proprietary license that allows commercial use of outputs but strictly prohibits reverse engineering or using outputs to develop competing models. The terms are clear but highly restrictive, and the model weights are not open. The license is 'Proprietary,' and there is no open-source component to the model itself.

OpenAI provides high-level guidance on latency and throughput (e.g., 25% faster than predecessors) but does not disclose specific VRAM requirements for local deployment, as the model is only available via API and managed apps. Information regarding context length memory scaling is provided (400k context), but technical details on quantization tradeoffs are absent.

OpenAI uses semantic versioning and provides 'snapshots' (e.g., gpt-5.3-codex-2026-02-05) to allow users to lock in specific model behaviors. While a changelog is maintained in the Model Release Notes, the specific nature of 'silent' updates to the base alias remains a concern for long-term consistency, though the snapshot system provides a mitigation path.