GLM-4.7

Mid-tier GLM reasoning route for engineering teams that need quality below flagship spend.

SWE-bench 73.8%Public model detailMoE Transformer

Params

355B / 32B active

Context

198K

Max Output

64K

License

Apache 2.0

TTFT

420ms

Throughput

58 tok/s

Why pick it

Cheaper GLM lane with strong coding profile
Useful step-down from GLM-5.1

Pricing

TierStandardCachedSiliconFlowSavings

Realtime$0.15 / $0.80$0.052$0.42 / $2.2064%

Batch$0.07 / $0.40$0.052$0.42 / $2.2064%

Quick start

OpenAI-compatible surface. Swap the base URL and ship.

Try in Playground Open pricing

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."}]
  }'

Specs

Architecture

MoE Transformer

Vendor group

Z.ai

Context window

198K

Max output

64K

Best for

reasoning

coding