GPT-5.4 is OpenAI’s latest frontier model, unifying the Codex and GPT lines into a single system. It features a 1M+ token context window (922K input, 128K output) with support for text and image inputs, enabling high-context reasoning, coding, and multimodal analysis within the same workflow.
The model delivers improved performance in coding, document understanding, tool use, and instruction following. It is designed as a strong default for both general-purpose tasks and software engineering, capable of generating production-quality code, synthesizing information across multiple sources, and executing complex multi-step workflows with fewer iterations and greater token efficiency.
Evaluations
16
across 14 benchmarks
Latency
494ms
Context length
1M
tokens
Cost
$2.50 · $15
input · output per 1M tokens
Key takeaways
GPT-5.4 is OpenAI’s latest frontier model, unifying the Codex and GPT lines into a single system, designed for high-context reasoning, coding, and multimodal analysis with text and image inputs. It serves as a strong default for general-purpose tasks and software engineering.
The model utilizes a proprietary Mixture-of-Experts (MoE) architecture with a 1M+ token context window (922K input, 128K output). It is trained to generate production-quality code, synthesize information across multiple sources, and execute complex multi-step workflows.
GPT-5.4 demonstrates strong arithmetic and algebraic reasoning (94.6% accuracy on MATH-500) and adheres to complex output formatting. It also shows strong code generation capabilities with 95.7% accuracy on human evaluation tasks.
GPT-5.4 struggles with precision in numerical tasks, nuanced factual recall in biology/medicine, complex logical interpretation, and image-based analysis, often showing overconfidence in incorrect answers. It failed all tests (0% accuracy) on the ARC-AGI-2 dataset.
Minor discrepancies in passing coding tasks typically arise from subtle differences in interpreting problem details or expectations for exact algorithmic approach, rather than fundamental coding errors.
