GPT-5.2 Codex

OpenAI identifies GPT-5.2 Codex as a 'dense transformer architecture' and a specialized variant of the GPT-5.2 flagship model. While it introduces specific technical features like 'Dynamic Sparse Attention' and 'context compaction' to manage its 400,000-token context window, there is no public documentation detailing the specific layer configurations, attention head counts, or the exact nature of the 'adaptive reasoning mechanism.' The transition from the general GPT-5.2 base to the Codex variant is described as 'further optimization' for agentic coding, but the specific fine-tuning or architectural divergence remains proprietary and opaque.

OpenAI provides only high-level descriptions of the training data, stating it includes 'multi-modal datasets' (text and vision) and is 'optimized for software engineering.' While a knowledge cutoff of August 31, 2025, is disclosed, there is no specific breakdown of data sources (e.g., GitHub, StackOverflow, internal repositories) or the proportions of programming languages used. The company mentions 'curating domain-specific code samples' and 'balancing the dataset,' but provides no verifiable metrics, filtering methodologies, or access to sample data, relying instead on vague marketing claims of 'high-quality' and 'advanced' curation.

The model uses a tokenizer that supports its 400k context window and multimodal inputs, but specific technical documentation for the GPT-5.2 Codex tokenizer is absent. While third-party tools like 'Tiktokenizer' are often used for general GPT models, the specific vocabulary size and tokenization logic for this variant—especially regarding its 'native compaction' and 'structured diff-based' output—are not publicly documented. There is no official disclosure of the vocabulary size or the specific training data alignment for the tokenizer itself.

The parameter count for GPT-5.2 Codex is officially 'Unknown.' While it is confirmed to be a 'dense' architecture (unlike some MoE competitors), OpenAI has not disclosed the total parameter count or any architectural breakdown (e.g., attention vs. FFN weights). The lack of transparency regarding the model's scale makes it impossible to verify efficiency claims or compare its density against other frontier models.

OpenAI acknowledges that the model was trained in collaboration with Microsoft and NVIDIA using H100, H200, and GB200-NVL72 GPUs. However, no specific compute metrics such as total GPU-hours, training duration, or energy consumption are disclosed. While the hardware type is known, the scale of the training run remains a proprietary secret, and no carbon footprint or environmental impact data has been provided for this specific model.

OpenAI reports state-of-the-art results on SWE-bench Pro (56.4%) and Terminal-Bench 2.0 (64.0%). While these benchmarks are public, the exact prompts, few-shot examples, and 'reasoning effort' settings used to achieve these scores are not fully disclosed in a reproducible format. Third-party leaderboards like Sigmabench and Kilo Code provide some independent verification, but the lack of a public evaluation harness or detailed methodology for the 'xhigh' reasoning mode limits full reproducibility.

The model consistently identifies as GPT-5.2 Codex and is transparent about its specialized role in agentic coding versus the general-purpose GPT-5.2. It provides clear versioning through API snapshots (e.g., gpt-5.2-codex-2025-12-18) and accurately reflects its capabilities, such as its 400k context window and multimodal input support. It does not appear to suffer from identity confusion or claim to be a competitor's model in official documentation.

The model is released under a strictly proprietary license. While the pricing ($1.75/1M input, $14/1M output) and usage terms for the API are clear, there is no transparency regarding the underlying weights or code. The license restricts commercial use to the OpenAI API and Microsoft Azure platforms, with no provision for derivative works or local deployment, which is standard for OpenAI but scores low on the transparency scale.

As a closed-source API-only model, there is zero public information regarding the VRAM requirements, memory scaling, or quantization tradeoffs for running the model. While OpenAI provides 'reasoning effort' controls that affect latency and cost, the actual hardware footprint required to serve the model is entirely hidden from the user. No guidance is provided for local inference as the weights are not available.

OpenAI uses semantic-like versioning and provides 'snapshots' to allow developers to lock in specific model versions, which helps mitigate issues with silent updates. However, the 'underlying model snapshot is regularly updated' for the main alias, and while changelogs exist, they often lack technical depth regarding specific behavioral shifts or 'alignment tax' impacts on coding performance over time.