GPT-5 Mini vs YOLO World
Compare GPT-5 Mini and YOLO World side-by-side. See how these vision models stack up in Object Detection.
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GPT-5 Mini vs YOLO World: Overview
GPT-5 Mini, released by OpenAI on August 7, 2025, is a mid-tier variant of the GPT-5 family that balances cost, speed, and capability. It is multimodal, supporting both text and image inputs, and offers a substantial input context window of ~400,000 tokens with output lengths up to ~128,000 tokens. While less powerful than the full GPT-5, it inherits its safety tuning, instruction-following improvements, and multimodal reasoning, making it a practical choice for developers who need large context handling without the expense of premium models.
GPT-5 Mini is optimized for affordability while retaining strong reasoning performance. Benchmarks show it outperforming earlier models such as GPT-4o on many multimodal and medical VQA tasks, though it lags behind GPT-5 on the most complex problems. Ideal use cases include prototyping, scalable content generation, document analysis, and mid-range reasoning tasks where efficiency and context capacity matter more than top-tier accuracy.
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.
GPT-5 Mini vs YOLO World Comparison Table
| Property | GPT-5 Mini | YOLO World |
|---|---|---|
| Organization | OpenAI | Tencent AI Lab |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Aug 2025 | Feb 2024 |
| Context Window | 400K | 13.0M |
| Parameters | ||
| License | Proprietary | GPL v3 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.250 | |
| Output $/1M | $2.00 | |
| Vision Tasks | ||
| Object Detection | Demo | Demo |
| Captioning | Demo | |
| Classification | Demo | |
| OCR | Demo | |
| Open Vocabulary Object Detection | ||
| Phrase Grounding | ||
| Vision Language | ||
| Visual Question Answering | Demo | |
| Model Features | ||
| Multimodal Vision | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Real-Time Vision | ||
| Zero-shot Detection | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Overall Score | 73.13% | |
| Avg Response Time | 11.72s | |
| Median input tokensincl. image tokens | 1.4K | |
| Median output tokens | 143 | |
| Est. cost / taskon this benchmark | $0.0006 | |
| Defect Detection | 80%(12/15) | |
| Document Understanding | 77.8%(7/9) | |
| Object Counting | 10%(1/10) | |
| Object Understanding | 85.7%(12/14) | |
| Spatial Understanding | 89.5%(17/19) | |
Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology