查看各种 AI 大语言模型在数学推理、代码生成、创意写作及其他开放式文本任务中的综合 Elo 积分排名，数据来源于真实用户的匿名盲测投票。
每个榜单会采用官网对应的核心排序指标。通用 Arena 榜单通常是模型竞技积分；智能体榜单则展示净提升及多项任务指标。
Each leaderboard uses the official primary ranking metric. Most Arena boards use competitive scores; the Agent board uses net improvement and task-specific metrics.
主指标的完整浮点数值。对于通用 Arena 榜单可用于区分整数分相近的模型；智能体榜单以百分比和置信区间展示。
The full floating-point value of the primary metric. It helps distinguish close scores on standard boards; Agent metrics are shown as percentages with confidence intervals.
衡量结果的统计稳定程度。绿色·稳定（<5）更可信；橙色·波动（5~12）；红色·不稳（>12）说明排名还可能明显变化。
Measures statistical stability. Green·Stable (<5); Orange·Unstable (5–12); Red·Volatile (>12) means the ranking may still move significantly.
真实实力有 95% 概率落在 [lower, upper] 区间内，显示为 ±N。N 越小排名越可信，票数越多 N 越小。
The model's true strength falls within [lower, upper] with 95% probability, shown as ±N. Smaller N = more reliable ranking; more votes → smaller N.
该模型或厂商参与评测的总次数。数量越高，结果的统计置信度越高、越不容易受单次样本影响而波动。
Total evaluation volume for the model or lab. Higher counts mean higher statistical confidence and less sensitivity to individual samples.
Apache/Llama：完全开源可商用；MIT：开源限制极少；Proprietary：闭源商业模型。协议决定你能否把它集成到自己的产品里。
Apache/Llama: Fully open-source, commercial use allowed; MIT: Open-source, minimal restrictions; Proprietary: Closed-source commercial model. License determines if you can integrate it into your product.
| 🥇 | 24 - 35 |
kandinsky-5.0-t2v-pro
Kandinsky · MIT
|
1172±20.0 | 2.0K 票votes | 1172.000 [1152.0, 1192.0] | 11.00 · 波动Unstable |
| 🥈 | 36 - 38 |
kandinsky-5.0-t2v-lite
Kandinsky · MIT
|
1113±18.0 | 1.5K 票votes | 1113.000 [1095.0, 1131.0] | 2.00 · 稳定Stable |
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