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Kling 2.1 Standard
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Kling 2.1 Standard is a closed-weights video model from Kuaishou (Kling AI), released May 2025.
Model information sources
$ how it compares
The video models ranked either side of Kling 2.1 Standard, out of 139 with a score, then more from Kuaishou (Kling AI).
| # | model | score | where | ||
|---|---|---|---|---|---|
| 49 |  Ray 3 | 55±2 | comparison only | ||
| 50 |  Vidu Q3 | 54±12 | comparison only | ||
| 51 | Kling 2.1 Standard | 54±3 | api $0.050/sec | ||
| 52 |  Hailuo 02 | 54±2 | api $0.047/sec | ||
| 53 |  LTX-2.5 | 53±6 | api $0.12/sec +local | ||
| more from Kuaishou (Kling AI) | |||||
| 22 | Kling 3.0 | 68±4 | api $0.084/sec | ||
| 31 | Kling O3 Pro | 64±4 | api $0.11/sec | ||
| 34 | Kling 2.6 Pro | 59±2 | api $0.070/sec | ||
$ what it's good at
image to video▁▂·▂▅█▅▄▅▆▃▄ #38 of 50 * · 96% of its score · 1 test
Trails Cosmos3-Nano, Kling 2.1 Master, Hailuo 02 here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| Arena image-to-video | 1228.6 ✎ | ▁▁▁▁▄▇▃▆▃▂▄█ beats 11/40 | 96% |
$ where to run it
Through an API
api · Replicate · $0.050/sec · replicate.com ↗ · price sourceUSD per second of output video, standard mode (720p): Replicate's official kwaivgi/kling-v2.1 (image-to-video) states 'Standard mode (720p) costs $0.05 per second. Pro (1080p) costs $0.09 per second.' Product: Replicate model API. API model: kwaivgi/kling-v2.1. Settings: mode=standard (default), 720p, 24 fps, image-to-video. Price basis: published per-second-of-output list rate. Flag: fal lists its Kling 2.1 standard endpoint as deprecated and Kling's own developer pricing page no longer prices 2.1; the Replicate model is online. Currency: USD. Not a Kling direct tariff. Replicate official model page, observed 2026-09-26.
$ how the score was worked out · LMArena leaderboard dataset 96%
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 |
|---|---|---|
 LMArena leaderboard dataset | 1 | 96% |
| prior · pull toward the middle | 4% | |
| weighted average | = 53.6 |
$ 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