Llama 3.2 Vision 90b vs Qwen3.5 122B A10B
Compare Llama 3.2 Vision 90b and Qwen3.5 122B A10B side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.
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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 Qwen3.5 122B A10B Comparison Table
Evals updated August 20, 2026Pricing updated August 24, 2026
| Property | Llama 3.2 Vision 90b | Qwen3.5 122B A10B |
|---|---|---|
| Organization | Meta | Qwen |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Sep 2024 | Feb 2026 |
| Context Window | 128K | 256K |
| Parameters | 90B | 122B |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.260 | |
| Output $/1M | $2.08 | |
| Vision Tasks | ||
| Captioning | Demo | |
| Chart Question Answering | ||
| Classification | ||
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Object Detection | ||
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Llama 3.2 Vision 90b vs Qwen3.5 122B A10B: 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.
Qwen3.5-122B-A10B is a high-capacity multimodal Mixture-of-Experts (MoE) model developed by Alibaba’s Qwen team as part of the Qwen3.5 model family. The architecture contains 122 billion total parameters while activating roughly 10 billion per token through sparse expert routing, allowing the model to balance large-scale reasoning ability with relatively efficient inference compared to dense models of similar size.
The model is designed to process both text and visual inputs within a unified multimodal framework, enabling tasks that require reasoning across images, documents, charts, and natural language. This makes it suitable for applications such as document understanding, diagram interpretation, and complex visual question answering.
Qwen3.5-122B-A10B supports a native context window of approximately 256,000 tokens, which can be extended further through techniques such as YaRN scaling to support very long-context workloads. Released under the Apache 2.0 license, it builds on earlier Qwen multimodal systems and provides developers with an open-weight model capable of handling demanding multimodal reasoning and analysis tasks.