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GLM 5

Енгізу:$0.8/M
Шығыс:$3.2/M
Шыққан күні:Feb 11, 2026

GLM-5 is Z.ai’s flagship open-source foundation model engineered for complex systems design and long-horizon agent workflows. Built for expert developers, it delivers production-grade performance on large-scale programming tasks, rivaling leading closed-source models. With advanced agentic planning, deep backend reasoning, and iterative self-correction, GLM-5 moves beyond code generation to full-system construction and autonomous execution.

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GLM 5 үшін Playground

GLM 5 Playground-ын зерттеңіз — модельдерді тестілеу және нақты уақытта сұрауларды орындауға арналған интерактивті орта. Промпттарды қолданып көріңіз, параметрлерді реттеңіз және дамуды жеделдету және пайдалану жағдайларын растау үшін лезде қайталаңыз.

Technical specifications of GLM-5

ItemGLM-5 (reported)
Model familyGLM (Z.ai / Zhipu AI) — flagship generation
ArchitectureMixture-of-Experts (MoE) + sparse attention (DeepSeek/DSA optimizations).
Total parameters≈744–745B (MoE pool).
Active / routed params (per token)~40–44B active (depends on routing/experts).
Pre-training tokens~28.5T tokens (reported).
Context window (input)Up to 200,000 tokens (long-context mode).
Max output tokens128,000 tokens (max generation per call reported).
Input modalitiesText only (primary); engineered for rich text → outputs (doc/xlsx generation via tools).

What is GLM-5

GLM-5 is Zhipu AI’s next-generation foundation model that scales the GLM line with an MoE routing design and sparse attention optimizations to deliver long-context reasoning and agentic workflows (multi-step planning, code & system orchestration). It’s explicitly positioned to be an open-weights contender for agentic and engineering tasks, with enterprise accessibility via APIs and self-hosting.

🚀 Main Features of GLM-5

1. Agentic Intelligence & Reasoning

GLM-5 is optimized for workflows where the model breaks down long, complex tasks into ordered steps with reduced hallucination — a major improvement over prior GLM versions. It leads certain open weights model benchmarks on knowledge reliability and task productivity.

2. Long Context Support

With a 200K token context window, GLM-5 can sustain very long conversations, large documents, and extended reasoning chains without losing coherence — an increasingly critical capability for real-world professional applications.

3. DeepSeek Sparse Attention

By integrating a sparse attention mechanism, GLM-5 efficiently scales its memory footprint, allowing longer sequences without linear cost increases.

4. Tool Integration & Output Formats

Native support for structured outputs and external tool integrations (JSON, API calls, dynamic tool use) makes GLM-5 practical for enterprise applications like spreadsheets, reports, and automated coding assistants.

5. Cost Efficiency

GLM-5 is positioned as cost-competitive compared to proprietary counterparts, with input/output pricing substantially lower than major offerings, making it attractive for large-scale deployment.

Benchmark Performance of GLM-5

Multiple independent evaluations and early industry benchmarks show GLM-5 performing strongly among open-weight models:

  • It achieved record-low hallucination rates on the Artificial Analysis Intelligence Index — a measure of reliability and truthfulness — outperforming prior models by a wide margin.
  • Agent-centric benchmarks indicate substantial gains in complex task execution compared to GLM-4.7 and other open models.
  • Cost-to-performance metrics position GLM-5 as 4th quartile for speed but top tier (best) on intelligence and price among open-weight models.

Quantitative Scores (Example from ranking platform):

  • Intelligence Index: #1 among open weights models.
  • Pricing Efficiency: High ratings for low input/output costs.

How to access and use GLM-5 API

Step 1: Sign Up for API Key

Log in to cometapi.com. If you are not our user yet, please register first. Sign into your CometAPI console. Get the access credential API key of the interface. Click “Add Token” at the API token in the personal center, get the token key: sk-xxxxx and submit.

Step 2: Send Requests to glm-5 API

Select the “glm-5” endpoint to send the API request and set the request body. The request method and request body are obtained from our website API doc. Our website also provides Apifox test for your convenience. Replace <YOUR_API_KEY> with your actual CometAPI key from your account. Where to call it: Chat format.

Insert your question or request into the content field—this is what the model will respond to . Process the API response to get the generated answer.

Step 3: Retrieve and Verify Results

Process the API response to get the generated answer. After processing, the API responds with the task status and output data.

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GLM 5 үшін баға белгілеу

[Модель атауы] үшін әртүрлі бюджеттер мен пайдалану қажеттіліктеріне сәйкес келетін бәсекеге қабілетті баға белгілеуді зерттеңіз. Біздің икемді жоспарларымыз сіз тек пайдаланған нәрсеңіз үшін ғана төлеуіңізді қамтамасыз етеді, бұл сіздің талаптарыңыз өскен сайын масштабтауды жеңілдетеді. [Модель атауы] шығындарды басқарылатын деңгейде ұстай отырып, сіздің жобаларыңызды қалай жақсарта алатынын біліңіз.

Comet Price (USD / M Tokens)Official Price (USD / M Tokens)Discount
Енгізу:$0.8/M
Шығыс:$3.2/M
Енгізу:$1/M
Шығыс:$4/M
-20%

GLM 5 үшін үлгі код және API

[Модель атауы] үшін кешенді үлгі кодтары мен API ресурстарына қол жеткізіп, интеграция процесіңізді жеңілдетіңіз. Біздің толық құжаттама қадам-қадаммен нұсқаулық береді, жобаларыңызда [Модель атауы] мүмкіндіктерін толық пайдалануға көмектеседі.

#!/bin/bash

# Get your CometAPI key from https://api.cometapi.com/console/token
COMETAPI_KEY="${COMETAPI_KEY:-<YOUR_COMETAPI_KEY>}"

# glm-5: Zhipu GLM-5 model via chat/completions
curl -s https://api.cometapi.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $COMETAPI_KEY" \
  -d '{
    "model": "glm-5",
    "messages": [
      {
        "role": "user",
        "content": "Hello! Tell me a short joke."
      }
    ]
  }'

cURL Code Example

#!/bin/bash

# Get your CometAPI key from https://api.cometapi.com/console/token
COMETAPI_KEY="${COMETAPI_KEY:-<YOUR_COMETAPI_KEY>}"

# glm-5: Zhipu GLM-5 model via chat/completions
curl -s https://api.cometapi.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $COMETAPI_KEY" \
  -d '{
    "model": "glm-5",
    "messages": [
      {
        "role": "user",
        "content": "Hello! Tell me a short joke."
      }
    ]
  }'

Python Code Example

from openai import OpenAI
import os

# Get your CometAPI key from https://api.cometapi.com/console/token
COMETAPI_KEY = os.environ.get("COMETAPI_KEY") or "<YOUR_COMETAPI_KEY>"
BASE_URL = "https://api.cometapi.com/v1"

client = OpenAI(base_url=BASE_URL, api_key=COMETAPI_KEY)

# glm-5: Zhipu GLM-5 model via chat/completions
completion = client.chat.completions.create(
    model="glm-5",
    messages=[{"role": "user", "content": "Hello! Tell me a short joke."}],
)

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

JavaScript Code Example

import OpenAI from "openai";

// Get your CometAPI key from https://api.cometapi.com/console/token
const COMETAPI_KEY = process.env.COMETAPI_KEY || "<YOUR_COMETAPI_KEY>";
const BASE_URL = "https://api.cometapi.com/v1";

const client = new OpenAI({
  apiKey: COMETAPI_KEY,
  baseURL: BASE_URL,
});

// glm-5: Zhipu GLM-5 model via chat/completions
const completion = await client.chat.completions.create({
  model: "glm-5",
  messages: [{ role: "user", content: "Hello! Tell me a short joke." }],
});

console.log(completion.choices[0].message.content);