If your app processes user-uploaded images, latency is everything. A 2-second delay in image analysis can kill user engagement. We benchmarked 6 vision-capable models on raw processing speed.
Test Setup
Three image sizes: small (200KB, 512x512), medium (1MB, 1024x1024), large (5MB, 2048x2048). Tested image description, OCR extraction, and document Q&A. 50 runs each, cold starts. Avg time in seconds.
Results
| Model | Small (200KB) | Medium (1MB) | Large (5MB) | OCR Time |
|---|---|---|---|---|
| Hunyuan-Vision | 0.8s | 1.4s | 2.8s | 1.1s |
| Qwen-VL-Plus | 1.1s | 1.8s | 3.5s | 1.5s |
| MiniMax-VL-01 | 1.3s | 2.1s | 4.2s | 1.8s |
| GLM-4V | 1.5s | 2.4s | 4.8s | 2.0s |
| Qwen-VL-Max | 1.8s | 2.9s | 5.5s | 2.3s |
Key Finding
Hunyuan-Vision is the fastest vision model by a significant margin, processing small images in 0.8 seconds. Qwen-VL-Max is the slowest but provides the highest quality output. For real-time applications where latency is critical, Hunyuan-Vision is the clear choice. For accuracy-critical applications like medical imaging, Qwen-VL-Max is worth the wait.
All tests via Global API using standard OpenAI-compatible vision API format.