GLM-5.2 supports a larger context window and output length, with up to 1M context and 128K output tokens. It adds streaming output for tool calls via tool_stream=true, enabling real-time access to tool call arguments. Like the GLM-4.7 series, it supports deep thinking with thinking={ type: "enabled" }, where thinking is enforced once enabled, and introduces reasoning_effort to control reasoning intensity under chain-of-thought mode. It also delivers stronger coding performance and more advanced reasoning capabilities.
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74.1%$1.4/ 百万 token$4.4/ 百万 token读取:0.14-0.28/ 百万 token写入:1.75/ 百万 token1M3.19s34.3tps
供应商
在多个供应商之间路由请求。复制供应商标识以设置偏好供应商。
供应商缓存命中率上下文
alibaba
可用率
24 小时AI Gateway 直连请求及各供应商的成功率。
Oct 27, 00:54
Alibaba Cloud100.00%
Streamlake-
Baidu100.00%
TencentCloud-
BigModel-
吞吐量
24 小时AI Gateway 实时流量的 P50 吞吐量,单位为每秒 token 数(TPS)。
Oct 27, 00:00
Alibaba Cloud-
Baidu-
TencentCloud-
BigModel40.7 tps
延迟
24 小时AI Gateway 实时流量的 P50 首 token 延迟(TTFT),单位为毫秒。
Oct 27, 00:00
Alibaba Cloud-
Baidu-
TencentCloud-
BigModel1.53s
活动
该模型随时间变化的 token 用量和请求流量。
Token 消耗
Oct 25
Volcengine-
Alibaba Cloud23.1M
Streamlake-
Baidu906.0K
TencentCloud-
BigModel224.3K
按实际处理请求的供应商拆分 token 用量。区块越大,表示该供应商处理的模型流量越多。
Apps
Public apps that send the most traffic to this model. Good signal for what real production workloads look like — and a hint at which use cases this model is best suited for. View All
Token 排行
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