Llama 4 Scout vs Qwen3 VL 30B A3B Instruct
Compare Llama 4 Scout and Qwen3 VL 30B A3B Instruct side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.
Compare Llama 4 Scout vs Qwen3 VL 30B A3B Instruct live
Run the same image across every model that supports a task and compare their outputs side-by-side.
Extract and compare text from images across multiple models.
Upload an image
Drag and drop an image here, or click to browse
Models in this comparison
Llama 4 Scout vs Qwen3 VL 30B A3B Instruct Comparison Table
Evals updated July 10, 2026Pricing updated July 21, 2026
| Property | Llama 4 Scout | Qwen3 VL 30B A3B Instruct |
|---|---|---|
| Organization | Meta | Qwen |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Apr 2025 | Oct 2025 |
| Context Window | 10.0M | 262K |
| Parameters | 109B | 31B |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.100 | $0.130 |
| Output $/1M | $0.300 | $0.520 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Object Detection | ||
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Llama 4 Scout vs Qwen3 VL 30B A3B Instruct: Overview
Llama 4 Scout, released on April 5, 2025, is one of Meta AI’s first Llama 4 multimodal models, alongside Maverick. It accepts text + image inputs and produces text outputs, with a knowledge cutoff of August 2024. Scout is notable for its extremely large context window of 10 million tokens, making it well-suited for analyzing very long documents, extended conversations, or large codebases.
Architecturally, Scout uses a Mixture-of-Experts (MoE) system with 16 experts, activating ~17B parameters per inference from a pool of ~109B total parameters, balancing capacity with efficiency. It officially supports 12 languages (including English, Arabic, French, Hindi, and Spanish), while offering multimodal reasoning for images (captioning, Q&A, recognition). Meta highlights that Scout can run on a single Nvidia H100 GPU, making it more accessible than larger-scale Llama 4 models. However, its output token limit is far smaller than its 10M input window, image input support is still constrained, and license restrictions apply for large-scale commercial deployments.
Qwen3 VL 30B A3B Instruct is an open-weight multimodal large language model developed by Alibaba as part of the Qwen family, built for instruction-following tasks that unify text generation with visual and video understanding. Released around October 2025 under the Apache-2.0 license, it targets efficient, high-fidelity vision-language reasoning across very long contexts.
The model accepts text and image inputs and produces text outputs, with strong performance in OCR, spatial reasoning, long-video understanding, and agentic or GUI-centric visual tasks. It uses a Mixture-of-Experts (A3B) design with ~31.1B total parameters and ~3B active per token, paired with Qwen3-VL’s unified multimodal stack (including Interleaved-MRoPE and DeepStack fusion) to process text, images, and video in a single architecture. OCR support expands to 32 languages, enhancing document workflows. With a native ~262K token context window (extendable further), it stands out today for its balance of scale, efficiency, long-context support, and open accessibility in multimodal systems.