GPT-5 Nano vs SmolVLM2
Compare GPT-5 Nano and SmolVLM2 side-by-side.
Compare GPT-5 Nano vs SmolVLM2 live
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Models in this comparison
GPT-5 Nano vs SmolVLM2 Comparison Table
Evals updated October 13, 2026Pricing updated October 24, 2026
| Property | GPT-5 Nano | SmolVLM2 |
|---|---|---|
| Organization | OpenAI | Hugging Face |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Aug 2025 | Feb 2025 |
| Context Window | 400K | — |
| Parameters | Unknown | 256M – 2.2B |
| License | Proprietary | Apache 2.0 |
| Commercial use | commercial use per vendor terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $0.050 | No published price |
| Output $/1M | $0.400 | No published price |
| Vision Tasks | ||
| Captioning | Demo | Supported |
| Vision Language | Supported | Supported |
| Visual Question Answering | Demo | Supported |
| Chart Question Answering | Supported | Not listed |
| Classification | Demo | Not listed |
| Document Question Answering | Supported | Not listed |
| Image Tagging | Supported | Not listed |
| Multi-Label Classification | Supported | Not listed |
| Object Detection | Demo | Not listed |
| OCR | Demo | Not listed |
| Model Features | ||
| LLMs with Vision Capabilities | Supported | Supported |
| Multimodal Vision | Supported | Supported |
| Foundation Vision | Supported | Not listed |
GPT-5 Nano vs SmolVLM2: Overview
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.
SmolVLM2 is a compact multimodal vision-language model developed by the Hugging Face TB Research team, released in February 2025 under the Apache 2.0 license. It is designed for efficient image and video understanding on resource-constrained hardware, with model variants ranging from 256M to 2.2B parameters. SmolVLM2 processes images, multi-image inputs, and video alongside text queries to generate text outputs for tasks including visual question answering, image captioning, and OCR.
SmolVLM2 is designed for on-device and edge deployment, requiring substantially less GPU memory than comparable multimodal models. It supports standard fine-tuning pipelines via the Hugging Face transformers library and quantization through bitsandbytes. SmolVLM2 is suited for applications where a capable vision-language model is needed without full server-scale infrastructure.