Overview

GLM-5 represents a significant advancement in multimodal reasoning within the GLM family, engineered specifically for complex vision-language tasks. With a massive 128,000-token context window, this model maintains high fidelity across extensive documents and intricate image sequences without losing contextual coherence. Operating at FP16 precision, it delivers robust performance for demanding applications requiring detailed visual analysis and textual synthesis. Developers integrating GLM-5 gain access to state-of-the-art interpretation capabilities, making it ideal for workflows involving document parsing, chart analysis, and detailed visual querying where standard text-only models fall short.

Accessible through the LLM Resayil platform, GLM-5 is available under a proprietary license starting at the starter tier, ensuring seamless integration into production environments. While the credit multiplier reflects the advanced computational resources required for multimodal processing, the trade-off yields superior accuracy in mixed-media scenarios. This model is designed for teams prioritizing deep visual understanding alongside natural language generation. By leveraging GLM-5, developers can build sophisticated agents capable of navigating complex visual data streams, ensuring their applications remain competitive in environments where multimodal intelligence is a critical differentiator.

Specifications

Display Name GLM-5

Family GLM

Category Vision

Parameters unknown

Context Window 128,000 tokens

Quantization FP16

License PROPRIETARY

Min Tier Starter

Status Available

Pricing

3.5×

credits per token

1K 3,500 Credits

10K 35,000 Credits

100K 350,000 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",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

print(response.choices[0].message.content)

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

Use Cases

Analyzing extensive reports with embedded visual charts and graphs

Extracting structured data from large batches of scanned invoices

Summarizing lengthy video content using detailed frame analysis

Interpreting complex scientific diagrams and detailed research figures

Visual question answering for uploaded photographic content and images

In-Depth Guide

Full Guide

Complete Guide to GLM-5 — LLM Resayil

GLM-5