Llama 3.2 Vision 90b vs Qwen3 VL 235B A22B Instruct
Compare Llama 3.2 Vision 90b and Qwen3 VL 235B A22B Instruct side-by-side. See how these vision models stack up in Image Captioning, OCR, Open Prompt, and Classification.
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Llama 3.2 Vision 90b is deprecated and can no longer be run. Details and evals are still available on its model page.
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Llama 3.2 Vision 90b vs Qwen3 VL 235B A22B Instruct Comparison Table
Evals updated August 20, 2026Pricing updated August 24, 2026
| Property | Llama 3.2 Vision 90b | Qwen3 VL 235B A22B Instruct |
|---|---|---|
| Organization | Meta | Qwen |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Sep 2024 | Sep 2025 |
| Context Window | 128K | 256K |
| Parameters | 90B | 235B |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.210 | |
| Output $/1M | $1.90 | |
| Vision Tasks | ||
| Captioning | Demo | |
| Chart Question Answering | ||
| Classification | Demo | |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Object Detection | Demo | |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | Deprecated | 65.8% |
| Avg cost / sample | – | $0.0007 |
| Avg speed / sample | – | 9.17s |
| By task | ||
| Object Detection | – | 52.1% $0.0014 |
| Counting | – | 47.3% $0.0002 |
| Identification | – | 90.6% $0.0002 |
| OCR | – | 88.1% $0.0010 |
| Data Extraction | – | 86.6% $0.0002 |
| Reasoning (low) | – | 29.8% $0.0002 |
| Reasoning (high) | – | 33.8% $0.0002 |
Llama 3.2 Vision 90b vs Qwen3 VL 235B A22B Instruct: Overview
Llama 3.2 Vision 90B, released by Meta AI on September 25, 2024, is the largest vision-capable model in the Llama 3.2 family. With about 90 billion parameters (~88.8B) and a 128,000-token context window, it is designed for high-performance multimodal reasoning over images and text, while producing only text outputs. The model was trained on ~6 billion image–text pairs and instruction-tuned (SFT + RLHF), with a knowledge cutoff of December 2023.
It powers tasks like visual question answering, captioning, and image-grounded reasoning, and achieves strong benchmark performance compared to both open and proprietary models. The model officially supports English for multimodal (image+text) tasks, while text-only inputs extend to eight languages (including German, French, Hindi, and Spanish). Due to its large parameter size, it requires substantial compute resources but is accessible via cloud providers like Amazon Bedrock, Oracle Cloud, and Azure AI Foundry. While highly capable, it is limited to text-only outputs and has stricter multilingual support for vision-based inputs.
Qwen3 VL 235B A22B Instruct is a flagship multimodal vision-language model developed by Qwen (Alibaba Cloud), designed for instruction-following tasks that combine advanced text generation with visual understanding. It serves as a high-end open-weight model for developers and researchers building multimodal AI systems that require strong reasoning, perception, and long-context capabilities.
The model supports interleaved text and image inputs, very long context windows (up to roughly 256K tokens), and efficient inference through a mixture-of-experts architecture with about 22B active parameters out of 235B total. In today’s landscape, it competes with top-tier proprietary vision-language models while offering the advantages of open weights and flexible deployment. Typical applications include multimodal assistants, document and image analysis, visual reasoning, and large-context instruction-based workflows.