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GLM 5

Wejล›cie:$0.8/M
Wyjล›cie:$3.2/M
Wydano:Feb 11, 2026

GLM-5 is Z.aiโ€™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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Playground dla GLM 5

Poznaj Playground GLM 5 โ€” interaktywne ล›rodowisko do testowania modeli i uruchamiania zapytaล„ w czasie rzeczywistym. Wyprรณbuj prompty, dostosuj parametry i iteruj natychmiast, aby przyspieszyฤ‡ rozwรณj i zweryfikowaฤ‡ przypadki uลผycia.

Technical specifications of GLM-5

ItemGLM-5 (reported)
Model familyGLM (Z.ai / Zhipu AI) โ€” flagship generation
ArchitectureMixture-of-Experts (MoE) + sparse attention (DeepSeek/DSA optimizations).
Total parametersโ‰ˆ744โ€“745B (MoE pool).
Active / routed params (per token)~40โ€“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 โ†’ outputs (doc/xlsx generation via tools).

What is GLM-5

GLM-5 is Zhipu AIโ€™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). Itโ€™s explicitly positioned to be an open-weights contender for agentic and engineering tasks, with enterprise accessibility via APIs and self-hosting.

๐Ÿš€ 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 โ€” 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 โ€” 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 โ€” a measure of reliability and truthfulness โ€” 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 toย cometapi.com. If you are not our user yet, please register first. Sign into yourย CometAPI console. Get the access credential API key of the interface. Click โ€œAdd Tokenโ€ at the API token in the personal center, get the token key: sk-xxxxx and submit.

Step 2: Send Requests toย glm-5ย API

Select the โ€œglm-5โ€ 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.ย Where to call it:ย Chatย format.

Insert your question or request into the content fieldโ€”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.

FAQ

Cennik dla GLM 5

Poznaj konkurencyjne ceny dla GLM 5, zaprojektowane tak, aby pasowaล‚y do rรณลผnych budลผetรณw i potrzeb uลผytkowania. Nasze elastyczne plany zapewniajฤ…, ลผe pล‚acisz tylko za to, czego uลผywasz, co uล‚atwia skalowanie w miarฤ™ wzrostu Twoich wymagaล„. Odkryj, jak GLM 5 moลผe ulepszyฤ‡ Twoje projekty przy jednoczesnym utrzymaniu kosztรณw na rozsฤ…dnym poziomie.

Comet Price (USD / M Tokens)Official Price (USD / M Tokens)Discount
Wejล›cie:$0.8/M
Wyjล›cie:$3.2/M
Wejล›cie:$1/M
Wyjล›cie:$4/M
-20%

Przykล‚adowy kod i API dla GLM 5

Uzyskaj dostฤ™p do kompleksowego przykล‚adowego kodu i zasobรณw API dla GLM 5, aby usprawniฤ‡ proces integracji. Nasza szczegรณล‚owa dokumentacja zapewnia wskazรณwki krok po kroku, pomagajฤ…c wykorzystaฤ‡ peล‚ny potencjaล‚ GLM 5 w Twoich projektach.

#!/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);