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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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Models in this comparison

Tencent

Qwen3.5 397B A17B vs YOLO World Comparison Table

Evals updated July 10, 2026Pricing updated July 16, 2026

PropertyQwen3.5 397B A17BYOLO World
OrganizationQwenTencent AI Lab
Categoryopenopen
Modalitymultimodalmultimodal
Release DateFeb 2026Feb 2024
Context Window262K13.0M
Parameters397B
LicenseApache 2.0GPL v3
Pricing per 1M tokens
Input $/1M$0.450
Output $/1M$3.00
Vision Tasks
Object DetectionDemo
CaptioningDemo
OCRDemo
Open Vocabulary Object Detection
Phrase Grounding
Vision Language
Visual Question AnsweringDemo
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

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

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.