Overview

glm 5.3 represents a significant leap in reasoning capabilities within the Glm_dsa_moe family. Built with 753 billion parameters and optimized using FP8 quantization, this thinking model delivers exceptional performance for complex problem-solving tasks without compromising efficiency. Developers can integrate it immediately via our standard API endpoints, enabling first calls within minutes of setup. The massive 1,048,576 token context window allows for processing entire codebases or lengthy documents in a single pass, making it ideal for advanced retrieval augmentation generation pipelines. Researchers will appreciate the architecture's balance between depth and speed, suitable for rigorous evaluation across multiple languages.

Production readiness is central to this release, featuring robust native Arabic support alongside English capabilities to serve diverse user bases effectively. The model operates on a starter tier with a transparent 2x credit multiplier, ensuring predictable costing for enterprise deployments. We provide comprehensive benchmark data comparing glm 5.3 against leading alternatives on bilingual tasks, confirming its superiority in regional linguistic nuances. This ensures decision makers can validate performance and budget requirements without needing direct sales intervention. Deploy with confidence knowing you have a scalable solution designed for high-stakes environments.

Specifications

Display Name glm 5.3

Family Glm_dsa_moe

Category Thinking

Parameters 753B

Context Window 1,048,576 tokens

Quantization FP8

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-5.3",
    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 book manuscripts for comprehensive editing and feedback

Reviewing massive legacy codebases for strategic refactoring opportunities

Summarizing long legal contracts or complex case files

Processing multi-hour meeting transcripts for actionable business insights

Solving complex mathematical problems with detailed step-by-step reasoning

In-Depth Guide

Full Guide

Complete Guide to glm 5.3 — LLM Resayil

glm 5.3