Qwen3.5 397B A17B vs YOLO World
Compare Qwen3.5 397B A17B and YOLO World side-by-side.
Compare Qwen3.5 397B A17B vs YOLO World live
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Qwen3.5 397B A17B vs YOLO World Comparison Table
Evals updated July 10, 2026Pricing updated July 16, 2026
| Property | Qwen3.5 397B A17B | YOLO World |
|---|---|---|
| Organization | Qwen | Tencent AI Lab |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | Feb 2024 |
| Context Window | 262K | 13.0M |
| Parameters | 397B | |
| License | Apache 2.0 | GPL v3 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.450 | |
| Output $/1M | $3.00 | |
| Vision Tasks | ||
| Object Detection | Demo | |
| Captioning | Demo | |
| OCR | Demo | |
| Open Vocabulary Object Detection | ||
| Phrase Grounding | ||
| Vision Language | ||
| Visual Question Answering | Demo | |
| Model Features | ||
| Multimodal Vision | ||
| LLMs with Vision Capabilities | ||
| Real-Time Vision | ||
| Zero-shot Detection | ||
Qwen3.5 397B A17B vs YOLO World: Overview
Qwen3.5-397B-A17B is a 397B-parameter (17B active) open-weight multimodal model developed by Alibaba’s Qwen team, released on 2026-02-16 under Apache-2.0. It supports text and image inputs with text outputs, combining a sparse Mixture-of-Experts architecture with Gated Delta Networks for efficient scaling. The model provides native vision-language reasoning and a large ~262K token context window, extendable to ~1M tokens.
As the first open-weight release in the Qwen3.5 family, it positions itself as a high-capacity, long-context alternative in the large vision-language space, balancing scale and efficiency via sparse activation. It is designed for advanced reasoning, coding, agent workflows, and multimodal understanding tasks.
YOLO-World v2 Small (YOLO-World-S-v2) is the smallest variant of Tencent AI Lab’s YOLO-World v2 family, released around February 2024 under GPL-v3. With ~13 million parameters, it adopts a prompt-then-detect paradigm using offline vocabularies and is pretrained on large-scale datasets such as Objects365 and GoldG. The model processes image inputs at 640×640 or 1280×1280 resolutions and supports zero-shot open-vocabulary object detection, enabling recognition of novel categories from text prompts without retraining.
Evaluations show competitive results across benchmarks like LVIS and COCO, while maintaining real-time efficiency. On an NVIDIA V100, the small variant reaches ~74 FPS at standard resolutions. Together with larger YOLO-World v2 models, it provides a scalable framework for efficient, open-vocabulary detection across diverse deployment settings.