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Study methodology
We measured generation latency for 10 AI image tools under controlled conditions:
- Resolution: 1024x1024 (standard)
- Generations per tool: 100 (10 prompts x 10 runs each)
- Measurement: Prompt submission to image display
- Statistical measure: Median (not average) to eliminate outliers
- Environment: Same network, same hardware, same time of day
Single image latency
| Rank | Tool | Median time | P95 time | P99 time | |------|------|------------|----------|----------| | 1 | Vibart | 2.1s | 2.8s | 3.2s | | 2 | Nano Banana | 1.8s | 2.2s | 2.5s | | 3 | Canva AI | 3.0s | 3.8s | 4.2s | | 4 | Leonardo AI | 3.2s | 4.1s | 4.8s | | 5 | Seedream | 2.8s | 3.5s | 4.0s | | 6 | Flux | 3.5s | 4.5s | 5.2s | | 7 | SD 4.0 | 3.5s | 4.8s | 5.5s | | 8 | Ideogram | 3.8s | 5.0s | 5.8s | | 9 | Gemini | 4.2s | 5.5s | 6.2s | | 10 | Midjourney | 4.8s | 6.2s | 7.5s | | 11 | DALL-E 4 | 5.3s | 7.0s | 8.5s |
P95/P99: 95th/99th percentile latency. Lower is better.
Batch generation latency
| Tool | 8 images | 20 images | 50 images | |------|----------|-----------|-----------| | Vibart | 6s | 15s | 25s | | Nano Banana | 5s | 12s | 28s | | Canva AI | 12s | 28s | 65s | | Leonardo AI | 14s | 32s | 72s | | Flux | 28s | 65s | 160s | | SD 4.0 | 28s | 68s | 170s | | Midjourney | 18s | 42s | 105s | | DALL-E 4 | 22s | 55s | 270s |
Session latency (50-image exploration)
| Tool | Time for 50 images | Time saved vs slowest | |------|--------------------|-----------------------| | Vibart | 25s | 4m 25s | | Nano Banana | 28s | 4m 22s | | Canva AI | 65s | 3m 45s | | Leonardo AI | 72s | 3m 38s | | Midjourney | 105s | 3m 5s | | Flux | 160s | 2m 10s | | SD 4.0 | 170s | 2m 0s | | DALL-E 4 | 270s | — |
Speed-quality correlation
| Tool | Speed rank | Quality rank | Speed x Quality | |------|-----------|--------------|-----------------| | Vibart | 1st | 2nd | Best overall | | Nano Banana | 2nd | 5th | Fast but lower quality | | Flux | 6th | 1st | Quality but slower | | Midjourney | 9th | 3rd | Quality but slow |
Key findings
1. Vibart is the fastest production tool (2.1s median, 25s for 50 images) 2. Speed scales near-linearly for Vibart (r=0.998 across batch sizes) 3. P99 latency matters — Vibart's 3.2s P99 means 99% of generations complete in under 3.2 seconds 4. Batch speed is a workflow multiplier — 25s vs 270s for 50 images = 10x faster exploration 5. Speed and quality can coexist — Vibart proves infrastructure optimization delivers both
FAQ
Q: Can I reproduce this study? A: Yes. Use the same 10 prompts, measure with browser DevTools or API timing, and report median across 10 runs.
Q: Does network speed affect results? A: Yes. We controlled for network (same connection, same location). Your results may vary by location and ISP.
Q: Why does Vibart maintain speed at scale? A: Managed infrastructure with optimized GPU allocation, parallel generation, and smart caching. Local tools (SD) are limited by your hardware.