Overview

glm 5.2 stands as a powerhouse within the Glm family, engineered for complex reasoning tasks requiring deep contextual understanding. With 756 billion parameters and a massive 1,000,000 token context window, this thinking model excels at processing entire codebases or lengthy legal documents in a single pass. Developers can integrate it immediately via our standard API endpoints, ensuring production-ready stability from the first call. The architecture is optimized for high-fidelity output, making it ideal for applications demanding precise logic and extended memory retention without fragmentation.

For researchers and enterprise leaders, glm 5.2 delivers exceptional performance across both Arabic and English workflows, validated through rigorous internal benchmarking against leading alternatives. Its dual-language proficiency ensures seamless deployment in regional pipelines where linguistic nuance is critical. While the 2x credit multiplier reflects its advanced computational depth, the starter tier accessibility allows teams to prototype cost-effectively before scaling. Transparent credit pricing aligns with major regional currencies, ensuring budget predictability for production environments. This balance of raw capability and economic efficiency makes it a strategic choice for building robust, multilingual AI solutions that require neither compromise on accuracy nor flexibility in deployment.

Specifications

Display Name glm 5.2

Family Glm

Category Thinking

Parameters 756b

Context Window 1,000,000 tokens

Min Tier Starter

Status Available

Pricing

2×

credits per token

1K 2,000 Credits

10K 20,000 Credits

100K 200,000 Credits

View Pricing Plans

Code Examples

from openai import OpenAI

client = OpenAI(
    base_url="https://llmapi.resayil.io/v1/",
    api_key="YOUR_API_KEY"
)

response = client.chat.completions.create(
    model="glm-5.2",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

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

import anthropic

client = anthropic.Anthropic(
    base_url="https://llmapi.resayil.io/v1",
    api_key="YOUR_API_KEY"
)

message = client.messages.create(
    model="glm-5.2",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

print(message.content[0].text)

const response = await fetch(
    "https://llmapi.resayil.io/v1/chat/completions",
    {
        method: "POST",
        headers: {
            "Content-Type": "application/json",
            "Authorization": "Bearer YOUR_API_KEY"
        },
        body: JSON.stringify({
            model: "glm-5.2",
            messages: [
                { role: "user", content: "Hello!" }
            ]
        })
    }
);

const data = await response.json();
console.log(data.choices[0].message.content);

curl https://llmapi.resayil.io/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "glm-5.2",
    "messages": [
      {"role": "user", "content": "Hello!"}
    ]
  }'

Use Cases

Analyzing entire legal contracts for compliance issues

Processing full codebases for security vulnerability detection

Summarizing lengthy technical manuals into concise guides

Conducting deep research across multiple academic papers

Reviewing complete book manuscripts for editorial consistency

In-Depth Guide

Full Guide

Complete Guide to glm 5.2 — LLM Resayil

glm 5.2