Overview

GLM 5.2 sets a new standard for enterprise-grade reasoning with 756 billion parameters and a massive 1,000,000 token context window. Designed for complex problem-solving, this thinking model excels at processing extensive documentation and multi-step logical tasks without losing coherence. Developers can integrate it immediately via our unified API, ensuring seamless deployment into production pipelines. The architecture supports high-precision code generation and deep analysis, making it ideal for building sophisticated applications that require sustained attention over large datasets.

For researchers and decision-makers, GLM 5.2 delivers state-of-the-art performance across bilingual workflows, demonstrating superior capability in both Arabic and English benchmarks compared to preceding generations. While operating at an 8x credit multiplier within the enterprise tier, the model justifies the investment through unmatched accuracy and reduced hallucination rates in critical operations. This ensures production-ready stability for high-stakes environments where data privacy and reliability are paramount. Accessible through LLM Resayil, you gain direct access to top-tier intelligence without infrastructure overhead, allowing teams to focus on innovation rather than management. Transparent credit pricing allows for precise budget forecasting regardless of scale.

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 case files for precedent research

Debugging large legacy codebases completely in one pass

Comprehensive financial audit across multi-year corporate reports

Cross-referencing compliance documents against company policies accurately

Complex multi-step reasoning for enterprise strategic planning

In-Depth Guide

Full Guide

Complete Guide to GLM 5.2 — LLM Resayil

GLM 5.2