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GPT-4.1 nano vs GPT-5 Nano

Compare GPT-4.1 nano and GPT-5 Nano side-by-side. See how these vision models stack up in Open Prompt, Image Captioning, OCR, Object Detection, and Classification.

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OpenAIGPT-4.1 nano

GPT-4.1 nano is deprecated and can no longer be run. Details and evals are still available on its model page.

OpenAIGPT-5 Nano
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GPT-4.1 nano vs GPT-5 Nano Comparison Table

Evals updated August 6, 2026Pricing updated August 7, 2026

PropertyGPT-4.1 nanoGPT-5 Nano
OrganizationOpenAIOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateApr 2025Aug 2025
Context Window1.0M400K
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.100$0.050
Output $/1M$0.400$0.400
Vision Tasks
CaptioningDemo
Chart Question Answering
ClassificationDemo
Document Question Answering
Image Tagging
Multi-Label Classification
Object DetectionDemo
OCRDemo
Vision Language
Visual Question AnsweringDemo
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision

GPT-4.1 nano vs GPT-5 Nano: Overview

GPT-4.1 nano

GPT-4.1 nano, released by OpenAI in April 2025, is the smallest and most cost-efficient member of the GPT-4.1 family. It is multimodal, supporting both text and image inputs, and retains the family’s extended 1 million-token context window—allowing it to handle large documents or codebases despite its lightweight design. Its training knowledge extends to June 2024.

GPT-4.1 nano prioritizes speed and affordability over raw reasoning power. While less capable than GPT-4.1 and GPT-4.1 mini, it is well-suited for high-volume or latency-sensitive workloads such as classification, autocomplete, content moderation, and lightweight assistants. This makes it an attractive option for developers seeking scalable deployment where efficiency is more critical than deep reasoning.

GPT-5 Nano

GPT-5 Nano, released by OpenAI on August 7, 2025, is the smallest and most cost-efficient model in the GPT-5 family. Like its larger counterparts, it is multimodal—accepting text and images, supporting tool use, structured outputs, and reasoning—but it is optimized for speed, low latency, and affordability. It features input and output token limits of roughly 272K and 128K tokens respectively, enabling large-context processing even at its compact scale. Its knowledge cutoff is around May 2024, slightly earlier than the full GPT-5 model.

GPT-5 Nano is well-suited for high-volume or cost-sensitive deployments such as mobile apps, embedded AI systems, or rapid-response APIs. While it offers less depth on complex reasoning and coding tasks compared to GPT-5 Mini or Pro, it retains core multimodal and agentic capabilities, making it an attractive option where efficiency and scale matter more than maximum performance.