GPT-5.4 nano

OpenAI identifies GPT-5.4 nano as a 'unified frontier model' variant that integrates capabilities from previous iterations like GPT-5.3-Codex. However, it is described only as a 'dense' architecture with no public documentation regarding its specific training methodology, architectural modifications, or pretraining procedures. The relationship between the 'nano' variant and the larger GPT-5.4 models is not technically detailed beyond marketing claims of efficiency.

Data provenance is highly opaque. While a knowledge cutoff of August 31, 2025, is stated, there is no public disclosure of dataset sources, composition percentages (e.g., web vs. code), or filtering methodologies. Claims of being 'factually grounded' and '33% less likely to contain false claims' are presented without verifiable data provenance or access to training samples.

The model supports a 400k context window and users can observe tokenization behavior via the API, but the specific tokenizer vocabulary size and underlying BPE configuration for the 5.4 series are not explicitly documented in a public technical report. There is no public repository for the tokenizer weights or training alignment data.

The parameter count for GPT-5.4 nano is officially 'Unknown'. While it is described as a 'dense' model, OpenAI has not disclosed the total or active parameter counts, nor provided any architectural breakdown of attention vs. feed-forward networks.

OpenAI has not disclosed any information regarding GPU/TPU hours, hardware specifications, training duration, or carbon footprint for the GPT-5.4 nano variant. The company explicitly cites competitive reasons for withholding compute metrics.

While OpenAI provides specific scores for benchmarks like SWE-Bench Pro (52.4%) and OSWorld-Verified (39.0%), the evaluation code, exact prompts, and few-shot examples used to achieve these results are not public. Third-party analysis indicates that these scores are difficult to replicate without access to the internal evaluation harness.

The model consistently identifies itself as GPT-5.4 nano via API responses and system prompts. It maintains a clear versioning identity within the OpenAI ecosystem and accurately reflects its role as a low-latency, cost-optimized variant of the GPT-5.4 family.

The model is released under a restrictive 'Proprietary' license. While the Terms of Service for the API are public, they include vaguely defined clauses regarding 'abusive usage' and 'programmatic extraction.' There is no open-source license for weights or code, and derivative works are strictly prohibited.

As an API-only model, there is no documentation for local VRAM requirements or quantization tradeoffs. While OpenAI provides pricing and context window limits (400k), it offers no guidance for developers on the hardware resources required to run equivalent-scale models or the impact of quantization on this specific architecture.

OpenAI uses date-based snapshots for its models, allowing developers to lock in specific versions. However, the changelog lacks technical depth regarding what specifically changes between snapshots, and there is a history of 'silent' updates to safety filters and alignment layers that affect model behavior without a version increment.