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Wan 2.6 Flash
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Wan 2.6 Flash is a closed-weights video model from Alibaba, released January 2026.
Model information sources
$ how it compares
The video models ranked either side of Wan 2.6 Flash, out of 139 with a score, then more from Alibaba.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 43 |  Seedance 1.0 Pro | 56±2 | api $0.052/sec | ||
| 44 |  Kling O1 Pro | 56±2 | api $0.11/sec | ||
| 45 | Wan 2.6 Flash | 55±7 | api $0.025/sec | ||
| 46 |  P-Video | 55±2 | api $0.020/sec | ||
| 47 |  Vidu Q2 Turbo | 55±3 | api $0.050/sec | ||
| more from Alibaba | |||||
| 2 | Wan 3.0 Prime | 80±7 | api $0.14/sec | ||
| 4 | Wan 3.0 | 79±2 | api $0.10/sec | ||
| 9 | HappyHorse 1.1 | 76±3 | api $0.14/sec | ||
$ what it's good at
image to video▁▂·▂▅█▅▄▅▆▃▄ #30 of 50 * · 80% of its score · 1 test
Beats Seedance 1.5 Pro, Wan 2.2 I2V A14B here, though they rank higher overall. Trails Cosmos3-Nano here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| VWG-Bench think-with-video reasoning (overall) | 56.0% ✎ | █··▄······▄█ beats 1/5 | 80% |
$ where to run it
Through an API
api · Alibaba Cloud Model Studio · $0.025/sec · www.alibabacloud.com ↗ · price sourceUSD per second of output video, 720P silent video (audio=false), International deployment (Singapore). Product: Model Studio Wan image-to-video (first frame). API model: wan2.6-i2v-flash (Model Studio's only Wan 2.6 Flash video model). Settings: resolution=720P, audio=false. Price basis: published list price; only output is billed. With audio (audio=true) 720P $0.05/s; 1080P $0.0375/s silent, $0.075/s with audio. Free quota and other regions excluded. Currency: USD. Alibaba Cloud Model Studio pricing page, observed 2026-09-26.
$ how the score was worked out · From Evaluation to Enhancemen… 80%
Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works
| source | results | share of the score |
|---|---|---|
 From Evaluation to Enhancement: Benchmarking and Improving Think-with-Video Reasoning for Video Generative Models (VWG-Bench) | 1 | 80% |
| prior · pull toward the middle | 20% | |
| weighted average | = 55.2 |
$ improve this page
Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.
data updated · sources and licences · json