Introducing OpenAI’s GPT-5.4 mini and GPT-5.4 nano for low-latency AI

2026-03-17 · Source: Microsoft Foundry Blog articles · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Data Science & Analytics · Depth: Intermediate, short

Summary

OpenAI has introduced GPT-5.4 mini and GPT-5.4 nano, smaller variants of GPT-5.4 optimized for developer workloads prioritizing low latency, cost savings, and agentic design. GPT-5.4 mini provides efficient reasoning, multimodal understanding, tool use, and web/file search, running approximately 2X faster than GPT-5 mini, making it ideal for developer copilots and computer-use sub-agents. GPT-5.4 nano is the smallest and fastest model, designed for ultra-low latency and high-throughput tasks such as classification, extraction, ranking, and lightweight sub-agent work. These models are rolling out in Microsoft Foundry, allowing developers to deploy a multi-model approach for diverse tasks, with specific pricing details provided for each. Microsoft Foundry also offers governance and monitoring capabilities to ensure responsible AI deployment, aligning with "Microsoft's Responsible AI principles."

Key takeaway

OpenAI introduces GPT-5.4 mini and nano, smaller, faster, and more cost-effective variants of GPT-5.4, optimized for developer workloads. GPT-5.4 mini offers ~2X faster performance for agentic reasoning and multimodal tasks, while GPT-5.4 nano provides ultra-low latency and cost-efficiency (\$0.20/M input tokens) for high-throughput classification and extraction. These models enable multi-model agent architectures, allowing developers to optimize for latency and cost by routing specific subtasks to the most appropriate model within Microsoft Foundry.

Topics

• GPT-5.4 mini
• GPT-5.4 nano
• Low-latency AI
• Agentic AI
• Microsoft Foundry

Best for: AI Architect, MLOps Engineer, NLP Engineer, AI Engineer, Machine Learning Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Microsoft Foundry Blog articles.