Overview

GLM 5.3 represents the pinnacle of our enterprise-grade reasoning models, built on the Glm_dsa_moe architecture. With 753 billion parameters operating at FP8 precision, this model delivers exceptional inference efficiency without compromising on depth. Its massive 1,048,576 token context window allows developers to process entire codebases or lengthy legal documents in a single pass. Benchmarks indicate superior performance in complex logical reasoning and multilingual tasks, specifically optimized for high-fidelity Arabic and English interactions. This makes it an ideal choice for researchers requiring robust data analysis and developers building applications that demand precise, context-aware outputs.

Integration is streamlined for immediate production deployment via our standard API endpoints, enabling your first successful call within minutes. While the 18x credit multiplier reflects its premium computational demands, transparent pricing structures allow for accurate budget forecasting without hidden fees. This return on investment is clear for mission-critical workflows requiring native Arabic language support and enterprise security standards. Available exclusively on the enterprise tier, GLM 5.3 ensures compliance and reliability for high-stakes environments. Choose this model when your project necessitates unparalleled context retention and nuanced linguistic understanding across diverse operational sectors.

Specifications

Display Name GLM 5.3

Family Glm_dsa_moe

Category Thinking

Parameters 753B

Context Window 1,048,576 tokens

Quantization FP8

Min Tier Enterprise

Status Available

Pricing

18×

credits per token

1K 18,000 Credits

10K 180,000 Credits

100K 1,800,000 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-5.3",
    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-5.3",
    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-5.3",
            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-5.3",
    "messages": [
      {"role": "user", "content": "Hello!"}
    ]
  }'

Use Cases

Analyzing entire software codebases for security vulnerabilities and bugs

Summarizing extensive legal document collections for compliance reviews

Processing long form video transcripts for detailed content insights

Solving complex multi step reasoning problems in financial modeling

Enterprise knowledge base synthesis across millions of tokens

In-Depth Guide

Full Guide

Complete Guide to GLM 5.3 — LLM Resayil

GLM 5.3