Overview

glm 5.3 flash delivers exceptional reasoning within the Glm5_next family, designed for developers demanding high performance. With 321 billion parameters optimized in FP8 quantization, it handles complex logical tasks efficiently. The massive 1,048,576 token context window allows for extensive document analysis without truncation. Developers can integrate this thinking model via our straightforward API, enabling immediate testing without deep documentation dives. Seamless integration ensures you complete your first API call within minutes. Benchmark data indicates superior performance on bilingual Arabic and English evaluations compared to similar scale alternatives, making it ideal for research pipelines requiring high accuracy across languages.

Production readiness is central to this release, offering robust Arabic language support essential for regional enterprises. The credit multiplier is set at 2x relative to the base rate, ensuring predictable cost management for high-volume applications. Transparent pricing structures accommodate regional currency preferences, eliminating the need for sales consultations before deployment. Budget planning is simplified with clear credit consumption rates. As a starter tier model, it provides enterprise-grade reliability accessible for immediate scaling. Whether building sophisticated agents or analyzing large datasets, glm 5.3 flash balances advanced cognitive capabilities with operational efficiency.

Specifications

Display Name glm 5.3 flash

Family Glm5_next

Category Thinking

Parameters 321B

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-flash",
    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-flash",
    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-flash",
            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-flash",
    "messages": [
      {"role": "user", "content": "Hello!"}
    ]
  }'

Use Cases

Analyzing entire legal contracts for compliance issues

Reviewing full repository code for security vulnerabilities

Summarizing lengthy meeting transcripts into actionable items

Solving complex mathematical problems with step reasoning

Extracting insights from multiple scientific research papers

In-Depth Guide

Full Guide

Complete Guide to glm 5.3 flash — LLM Resayil

glm 5.3 flash