GPT-5.4 Nano
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OpenAIOpenAIGPTReleased 2026-03-17

GPT-5.4 Nano

400K context$0.200/M input$1.25/M output

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 both 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 real-time systems and distributed agent architectures where minimizing cost and latency is essential.

What is GPT-5.4 Nano?

GPT-5.4 Nano is an AI model from OpenAI that Agent Mag tracks for pricing, context window, modalities, benchmarks, and API compatibility. Builders can use this page to compare GPT-5.4 Nano against other models for agent workflows and production deployments.

Model ID

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 both 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 real-time systems and distributed agent architectures where minimizing cost and latency is essential.

Architecture & Specifications
Tokenizer
GPT
Training Data
Knowledge cutoff Aug 31, 2025
License
Proprietary
Released
2026-03-17
Modalities
Input
fileimagetext
Output
text
Supported Parameters
include_reasoningmax_completion_tokensmax_tokensreasoningresponse_formatseedstructured_outputstool_choicetools
Strengths
  • Optimized for speed-critical and high-volume tasks
  • Supports text and image inputs
  • Low-latency performance for real-time systems
  • Cost-efficient for large-scale operations
  • Designed for distributed agent architectures
Limitations
  • Prioritizes responsiveness over deep reasoning
  • Limited suitability for tasks requiring complex reasoning
  • Hallucination rate of 26.4% in knowledge benchmarks
  • Lower accuracy in research-level physics reasoning (9.3%)
  • Moderate coding capability compared to other models
Recommended Use Cases
Classification tasks
Data extraction
Ranking systems
Sub-agent execution
Real-time distributed systems

Related content

Data enriched Apr 24, 2026. Pricing from OpenRouter API.