GLM Models Guide

Use Zhipu AI’s GLM-5.1, GLM-5, and GLM-4.7 models via the Runcrate API for chat completions and reasoning.

Zhipu AI’s GLM family offers strong multilingual chat models with competitive reasoning. All three generations are available through the Runcrate API.

Available GLM models

Model	Context	Strengths
GLM-5.1	128K	Latest generation, strongest reasoning
GLM-5	128K	Strong general-purpose chat
GLM-4.7	128K	Cost-effective, fast inference

---

Basic chat completion

Python

from runcrate import Runcrate

client = Runcrate(api_key="rc_live_YOUR_API_KEY")

response = client.models.chat_completion(
    model="zai-org/GLM-5.1",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain the difference between TCP and UDP for a beginner."},
    ],
    max_tokens=512,
)

print(response.choices[0].message.content)

---

Streaming with Vercel AI SDK

// app/api/chat/route.ts
import { runcrate } from '@runcrate/ai';
import { streamText } from 'ai';

export async function POST(req: Request) {
  const { messages } = await req.json();

  const result = streamText({
    model: runcrate('zai-org/GLM-5.1'),
    system: 'You are a helpful assistant specializing in technical explanations.',
    messages,
  });

  return result.toDataStreamResponse();
}

---

Structured output

import { runcrate } from '@runcrate/ai';
import { generateText, Output } from 'ai';
import { z } from 'zod';

const AnalysisSchema = z.object({
  topic: z.string(),
  keyPoints: z.array(z.string()).describe('3–5 main arguments'),
  conclusion: z.string(),
  confidence: z.number().min(0).max(1),
});

const { output } = await generateText({
  model: runcrate('zai-org/GLM-5.1'),
  output: Output.object({ schema: AnalysisSchema }),
  prompt: 'Analyze the impact of remote work on software engineering productivity.',
});

---

Comparing GLM generations

from runcrate import Runcrate

client = Runcrate(api_key="rc_live_YOUR_API_KEY")

models = ["zai-org/GLM-5.1", "zai-org/GLM-5", "zai-org/GLM-4.7"]
prompt = "Write a SQL query for the top 10 customers by order value in the last 30 days."

for model in models:
    response = client.models.chat_completion(
        model=model,
        messages=[{"role": "user", "content": prompt}],
        max_tokens=512,
        temperature=0.3,
    )
    print(f"\n--- {model} ---")
    print(response.choices[0].message.content)

---

Choosing the right GLM model

Use case	Model	Reason
Complex reasoning	GLM-5.1	Strongest in the family
General chat	GLM-5	Good balance of quality and speed
High-volume, cost-sensitive	GLM-4.7	Fastest, lowest cost per token

---

Tips

• GLM-5.1 is the recommended default unless you need cost savings.
• Multilingual: GLM models handle Chinese and English equally well.
• Temperature 0.3–0.5 works best for factual tasks; 0.7–0.9 for creative writing.

Next steps

• Chat completions reference
• Model catalog
• AI Chatbot with Next.js — build a chat UI with GLM