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GPT-4o vs GPT-5 Nano

Compare GPT-4o and GPT-5 Nano side-by-side. See how these vision models stack up in Open Prompt, Image Captioning, OCR, Classification, and Object Detection.

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OpenAIGPT-4o

GPT-4o 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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Models in this comparison

OpenAI

GPT-4o vs GPT-5 Nano: Overview

GPT-4o

GPT-4o (“omni”), released by OpenAI in May 2024, is a multimodal flagship model designed to unify text, image, and audio processing in a single system. Unlike earlier GPT-4 variants, GPT-4o supports real-time speech-to-speech interaction, enabling natural voice conversations alongside text and image reasoning. It features a context window of ~128,000 tokens for text input, with smaller output limits (commonly ~16K tokens), and has a knowledge cutoff of October 2023.

The model is optimized for efficiency and multilingual accessibility, supporting over 50 languages and covering ~97% of the world’s speakers. GPT-4o offers a cost-effective balance of speed and capability. It powers ChatGPT across free and paid tiers, making it widely accessible for applications in conversational AI, real-time translation, multimodal assistants, and global-scale communication tools.

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.

GPT-4o vs GPT-5 Nano Comparison Table

PropertyGPT-4oGPT-5 Nano
OrganizationOpenAIOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateMay 2024Aug 2025
Context Window128K400K
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$2.50$0.050
Output $/1M$10.00$0.400
Vision Tasks
CaptioningDemo
ClassificationDemo
Object DetectionDemo
OCRDemo
Vision Language
Visual Question AnsweringDemo
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Visual Understanding
Overall Score
58.21%
Avg Response Time6.58s
Median input tokensincl. image tokens1.8K
Median output tokens591
Est. cost / taskon this benchmark$0.0003
Defect Detection
86.7%(13/15)
Document Understanding
66.7%(6/9)
Object Counting
0%(0/10)
Object Understanding
64.3%(9/14)
Spatial Understanding
57.9%(11/19)
OCR
Overall Score
69%
Avg Response Time6.15s
Median input tokensincl. image tokens122
Median output tokens539
Est. cost / taskon this benchmark$0.0002
Focused Scene OCR
64.6%(64/99)
Handwritten Math
40%(4/10)
License Plate Recognition
83.3%(25/30)
Text Recognition
70%(21/30)
VQA & Extraction
73.3%(44/60)

Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology