GPT-5.4 nano is the most lightweight and cost-efficient variant of the GPT-5.4 family, optimized for speed-critical and high-volume tasks. It supports text and image inputs and is designed for low-latency use cases such as classification, data extraction, ranking, and sub-agent execution.
The model prioritizes responsiveness and efficiency over deep reasoning, making it ideal for pipelines that require fast, reliable outputs at scale. GPT-5.4 nano is well suited for background tasks, real-time systems, and distributed agent architectures where minimizing cost and latency is essential.
Evaluations
13
across 13 benchmarks
Latency
395ms
Context length
400k
tokens
Cost
$0.20 · $1.25
input · output per 1M tokens
Key takeaways
GPT-5.4 Nano is optimized for speed-critical and high-volume tasks such as classification, data extraction, ranking, and sub-agent execution, supporting text and image inputs while prioritizing responsiveness and efficiency over deep reasoning.
The model utilizes a Mixture-of-Experts (MoE) architecture, enhancing efficiency and inference speeds. It is designed for low-latency use cases, making it ideal for background tasks, real-time systems, and distributed agent architectures.
It demonstrates high proficiency in core algebraic skills (100% accuracy in Algebra levels 1-2) and good performance in calculus/function analysis (88.6% accuracy on 'MATH-500' dataset), consistently showing clear explanations for each step.
A significant limitation is its poor performance on complex reasoning benchmarks, scoring approximately 0.08 out of 25 (1.47% pass rate) on 'Humanity's Last Exam', struggling with geometric problems, nuanced interpretation, and logical oversights. It also exhibits high confidence (60-90%) in incorrect answers.
Common failure modes include misinterpretation of problem statements, algebraic errors, logical oversights, numerical precision issues, and a lack of domain-specific factual knowledge, particularly in complex scientific and medical domains.
