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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GPT-4o mini is deprecated and can no longer be run. Details and evals are still available on its model page.
Models in this comparison
GPT-4o mini vs GPT-5 Nano: Overview
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, 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
| Property | GPT-4o mini | GPT-5 Nano |
|---|---|---|
| Organization | OpenAI | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jul 2024 | Aug 2025 |
| Context Window | 128K | 400K |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.150 | $0.050 |
| Output $/1M | $0.600 | $0.400 |
| Vision Tasks | ||
| Captioning | Demo | |
| Classification | Demo | |
| Object Detection | Demo | |
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| 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 Time | 6.58s | |
| Median input tokensincl. image tokens | 1.8K | |
| Median output tokens | 591 | |
| 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 Time | 6.15s | |
| Median input tokensincl. image tokens | 122 | |
| Median output tokens | 539 | |
| 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