LLaVA-1.5 vs Qwen3.5 9b
Compare LLaVA-1.5 and Qwen3.5 9b side-by-side.
Compare LLaVA-1.5 vs Qwen3.5 9b 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 Qwen3.5 9b Comparison Table
Evals updated September 5, 2026Pricing updated September 20, 2026
| Property | LLaVA-1.5 | Qwen3.5 9b |
|---|---|---|
| Organization | Microsoft | Qwen |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Oct 2023 | Mar 2026 |
| Context Window | — | 262K |
| Parameters | 7B, 13B | 9B |
| License | Custom | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.100 | |
| Output $/1M | $0.150 | |
| Vision Tasks | ||
| Vision Language | ||
| Visual Question Answering | Demo | |
| Captioning | Demo | |
| Chart Question Answering | ||
| Classification | ||
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | ||
| OCR | Demo | |
| Model Features | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
| Foundation Vision | ||
Vision Evalsground-truth scores across 6 vision tasks | ||
| Overall | Not evaluated | 64.3% |
| Quantizationsself-hosted | ||
| Avg cost / sample | – | $0.0017 |
| Avg speed / sample | – | 33.65s |
| By task | ||
| Object Detection | – | 46.6% ±0.6, Mean of 3 runs, range 45.8 to 47.0 |
| Counting | – | 51.8% ±2.7, Mean of 3 runs, range 48.6 to 54.0 |
| Identification | – | 83.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 |
| OCR | – | 77.5% ±7.3, Mean of 3 runs, range 68.4 to 83.0 |
| Data Extraction | – | 78.7% ±2.1, Mean of 3 runs, range 76.3 to 80.4 |
| Reasoning | – | 47.7% ±2.3, Mean of 3 runs, range 45.7 to 50.3 |
LLaVA-1.5 vs Qwen3.5 9b: 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.
Qwen3.5-9B is a 9-billion-parameter multimodal foundation model developed by Alibaba Cloud's Qwen team, released on March 2, 2026 as part of the Qwen3.5 model family. Designed for efficient multimodal reasoning and long-context language tasks, it notably outperforms the older Qwen3-30B, a model more than three times its size, on key benchmarks including GPQA Diamond, IFEval, and LongBench.
The model supports vision-language inputs through an early-fusion multimodal architecture built on a dense hybrid foundation of Gated Delta Networks and Gated Attention. It can also operate in a text-only mode by skipping the vision encoder during inference. It provides a 262,144-token context window (extensible to ~1M tokens via YaRN) and is released under the Apache License 2.0. Within the current AI landscape, Qwen3.5-9B offers a strong balance of capability and efficiency, making it well-suited for multimodal assistants, document analysis, long-context reasoning, and developer-deployed agentic systems.