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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GPT-4o is deprecated and can no longer be run. Details and evals are still available on its model page.
Models in this comparison
GPT-4o vs GPT-5 Nano: Overview
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, 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
| Property | GPT-4o | GPT-5 Nano |
|---|---|---|
| Organization | OpenAI | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | May 2024 | Aug 2025 |
| Context Window | 128K | 400K |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $2.50 | $0.050 |
| Output $/1M | $10.00 | $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