Overview

GLM 5.2 represents the pinnacle of reasoning within the Glm family. With 756 billion parameters and a massive 1,000,000 token context window, this thinking model is engineered for complex, multi-step problem solving across extensive datasets. Developers integrating via LLM Resayil gain immediate access to enterprise-grade inference capable of handling full-codebase analysis and long-document synthesis without fragmentation. The architecture ensures high fidelity in both Arabic and English tasks, making it ideal for building sophisticated agents that require deep contextual retention and precise logical execution. Low-latency endpoints ensure your API calls return results efficiently even under heavy load.

Designed for production environments, GLM 5.2 operates on an enterprise tier with an 8x credit multiplier, reflecting its superior computational density and output quality. Researchers will find its bilingual proficiency exceptional for cross-lingual benchmarks, while business leaders can deploy with confidence knowing the model supports native Arabic nuance alongside global standards. This balance of raw power and linguistic flexibility allows teams to scale demanding workloads securely. By choosing this model, you prioritize accuracy and depth, ensuring your applications meet the rigorous demands of modern AI deployment without compromising on regional language requirements.

Specifications

Display Name GLM 5.2

Family Glm

Category Thinking

Parameters 756b

Context Window 1,000,000 tokens

Min Tier Enterprise

Status Available

Pricing

8×

credits per token

1K 8,000 Credits

10K 80,000 Credits

100K 800,000 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 codebases for compliance checks

Summarizing multi-hour video transcripts into key insights

Refactoring large legacy software systems automatically

Synthesizing thousands of research papers for literature reviews

Processing complete financial audit logs for anomalies

In-Depth Guide

Full Guide

Complete Guide to GLM 5.2 — LLM Resayil

GLM 5.2