LLaVA-1.5 vs Qwen2.5 VL 7B Instruct

Compare LLaVA-1.5 and Qwen2.5 VL 7B Instruct side-by-side.

Compare LLaVA-1.5 vs Qwen2.5 VL 7B Instruct live

Run the same image across every model that supports a task and compare their outputs side-by-side.

These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.

Models in this comparison

LLaVA-1.5 vs Qwen2.5 VL 7B Instruct: Overview

LLaVA-1.5

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.

Qwen2.5 VL 7B Instruct

Qwen2.5-VL-7B-Instruct is a 7-billion parameter vision-language model from Alibaba’s QwenLM team, released on January 26, 2025 under the Apache 2.0 license. It is the instruction-tuned variant of the 7B scale in the Qwen2.5-VL family, designed to process multimodal inputs such as text, images, charts, documents, and video. The model enables structured outputs—including JSON for structured content and bounding boxes for visual localization. Weights are publicly available on Hugging Face and GitHub, making it suitable for both research and applied multimodal use.

LLaVA-1.5 vs Qwen2.5 VL 7B Instruct Comparison Table

PropertyLLaVA-1.5Qwen2.5 VL 7B Instruct
OrganizationMicrosoftQwen
Categoryopenopen
Modalitymultimodalmultimodal
Release DateOct 2023Jan 2025
Context Window33K
Parameters7B, 13B7B
LicenseCustomApache 2.0
Vision Tasks
Vision Language
Visual Question AnsweringDemo
CaptioningDemo
Object Detection
OCRDemo
Model Features
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Visual Understanding
Overall Score
52.24%
Avg Response Time47.64s
Defect Detection
60%(9/15)
Document Understanding
77.8%(7/9)
Object Counting
0%(0/10)
Object Understanding
57.1%(8/14)
Spatial Understanding
57.9%(11/19)