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GPT-4o mini vs GPT-5 Nano

Compare GPT-4o mini 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-4o mini

GPT-4o mini 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-4o mini vs GPT-5 Nano: Overview

GPT-4o mini

GPT-4o mini, launched by OpenAI in July 2024, is a lightweight, cost-efficient variant of GPT-4o designed for developers who need strong multimodal reasoning at scale. It supports text and vision inputs (with audio/video support planned) and offers a 128,000-token input context window with outputs up to ~16,000 tokens. Like GPT-4o, it has a knowledge cutoff of October 2023 and integrates the same safety mitigations against misuse and prompt attacks.

GPT-4o mini is significantly cheaper than full GPT-4o while outperforming older models such as GPT-3.5 Turbo. It achieves around 82% on MMLU, reflecting solid reasoning, math, and coding capabilities despite its efficiency focus. The model replaced GPT-3.5 Turbo as the default in ChatGPT for many users, making it widely accessible for everyday conversational AI, educational tools, content generation, and scalable multimodal applications where affordability and speed are priorities.

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 mini vs GPT-5 Nano Comparison Table

PropertyGPT-4o miniGPT-5 Nano
OrganizationOpenAIOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateJul 2024Aug 2025
Context Window128K400K
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.150$0.050
Output $/1M$0.600$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