为什么选它
- 更低成本但仍保留较强编码能力
- 适合作为 GLM-5.1 的降本替代
Z.ai
glm-4.7
GLM-4.7
适合希望降低旗舰成本但仍保持工程推理质量的中高端 GLM 路线。
SWE-bench 73.8%公开模型详情MoE Transformer
参数规模
355B / 32B active
上下文
198K
最大输出
64K
许可证
Apache 2.0
TTFT
420ms
吞吐
58 tok/s
价格与差价
层级标准缓存SiliconFlow节省
在线调用$0.15 / $0.80$0.052$0.42 / $2.2064%
批处理$0.07 / $0.40$0.052$0.42 / $2.2064%
快速开始
OpenAI 兼容接口，切换 base_url 即可。
Python
from openai import OpenAI
client = OpenAI(
base_url="https://api.luminapath.tech/v1",
api_key="BATCHIN_API_KEY"
)
resp = client.chat.completions.create(
model="glm-4.7",
messages=[{"role": "user", "content": "Summarize why this model is a fit for my workload."}]
)
print(resp.choices[0].message.content)JavaScript
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://api.luminapath.tech/v1",
apiKey: process.env.BATCHIN_API_KEY,
});
const resp = await client.chat.completions.create({
model: "glm-4.7",
messages: [{ role: "user", content: "Summarize why this model is a fit for my workload." }],
});
console.log(resp.choices[0]?.message?.content);cURL
curl https://api.luminapath.tech/v1/chat/completions \
-H "Authorization: Bearer $BATCHIN_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "glm-4.7",
"messages": [{"role":"user","content":"Summarize why this model is a fit for my workload."}]
}'规格与行为
架构
MoE Transformer
厂商家族
Z.ai
上下文
198K
最大输出
64K
适用场景
reasoning
coding
相关模型
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