LLaVA-1.5 vs SmolVLM2
Compare LLaVA-1.5 and SmolVLM2 side-by-side.
Compare LLaVA-1.5 vs SmolVLM2 live
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LLaVA-1.5 vs SmolVLM2 Comparison Table
Evals updated July 10, 2026Pricing updated July 21, 2026
| Property | LLaVA-1.5 | SmolVLM2 |
|---|---|---|
| Organization | Microsoft | Hugging Face |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Oct 2023 | Feb 2025 |
| Context Window | — | — |
| Parameters | 7B, 13B | 256M – 2.2B |
| License | Custom | Apache 2.0 |
| Vision Tasks | ||
| Vision Language | ||
| Visual Question Answering | ||
| Captioning | ||
| Model Features | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
LLaVA-1.5 vs SmolVLM2: Overview
LLaVA-1.5 is an open-source large multimodal model released in October 2023 by researchers at the University of Wisconsin-Madison and Microsoft Research. It builds on the original LLaVA architecture by introducing targeted refinements: switching the vision encoder to CLIP-ViT-L at 336-pixel resolution, replacing the projection layer with a two-layer MLP, and adding academic-task-oriented visual question answering data with response formatting prompts during training. These modifications achieve state-of-the-art performance across 11 benchmarks at release, with training completing in approximately one day on a single 8-A100 node.
The model accepts an image paired with a text prompt and generates natural language responses, supporting visual question answering, image captioning, and open-ended visual conversation. LLaVA-1.5 is available in 7B and 13B parameter variants built on the Vicuna language model, and is distributed under the Llama 2 Community License due to its Llama-2-based foundation. The original LLaVA paper was presented as an oral at NeurIPS 2023. Subsequent releases in the series (LLaVA-NeXT (LLaVA-1.6), LLaVA-NeXT-Video, and LLaVA-OneVision) are separate models with their own release pages and build on this foundation with expanded OCR, video, and multi-image capabilities.
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.