GLM-4: Complete Specifications, Pricing, API Access & Use Cases (2026)

What Is GLM-4?

GLM-4 is Zhipu AI’s GLM-family large language model series, made available through the GLM-4 API on January 16, 2024. It features 128K-token context support in key GLM-4 variants and text-focused capabilities such as instruction following, reasoning, long-context processing, streaming, function calling, and structured output, with provider pricing listed in CNY per million tokens as of June 2026.

In current BigModel documentation, GLM-4 is a model family rather than one single API model ID. The GLM-4 page lists five variants:

• GLM-4-Plus
• GLM-4-Air-250414
• GLM-4-AirX
• GLM-4-FlashX-250414
• GLM-4-Flash-250414

Users usually search for GLM-4 to compare context length, pricing, API access, long-document handling, Chinese-English capability, and alternatives such as DeepSeek-V3 and Qwen2.5-72B Instruct.

GLM-4 should not be treated as Zhipu AI’s newest model generation. BigModel’s current model overview places newer GLM-5, GLM-5.1, GLM-5.2, GLM-4.7, GLM-4.6, and GLM-4.5 models above legacy GLM-4 variants. This page therefore focuses on GLM-4 as a documented production model family, not as the latest Zhipu flagship.

What Are GLM-4’s Key Specifications and Pricing?

What Can GLM-4 Do That Makes It Useful in Production?

GLM-4 is useful for long-context text workflows when teams choose a 128K variant. This can support document summarization, contract review preparation, policy extraction, knowledge-base answers, and retrieval-augmented generation. The limitation is that long context does not remove the need for source grounding, chunking strategy, and output verification.

GLM-4 can support structured application workflows because the documentation lists streaming, function calling, context caching, structured output, and MCP-style external tool or data-source access. These features matter for customer support routing, field extraction, workflow agents, and backends that need JSON-like outputs. Production teams should still validate schemas and create fallback handling.

GLM-4 is relevant for Chinese-English and multilingual text processing. BigModel’s GLM-4 page describes translation and multilingual mixed-text handling as recommended scenarios, and it states that GLM-4-Flash-250414 supports up to 26 languages.

Teams evaluating bilingual assistants may compare GLM-4 with DeepSeek-R1 for reasoning-heavy use cases and Claude 3.5 Sonnet for instruction-following workflows.

What Are GLM-4’s Supported Modalities?

Where Does GLM-4 Fall Short?

GLM-4’s main documentation issue is naming clarity. "GLM-4" refers to a family, while production use requires a specific variant and model ID, such as glm-4-plus.

Pricing comparison also requires care. The checked GLM-4 page lists variant-level token prices but does not clearly split input and output prices. This makes direct cost comparisons with models such as GPT-4o mini or Mistral Large less straightforward unless teams normalize billing assumptions.

GLM-4 language variants are text-focused in the checked documentation. Teams needing native image, audio, or video understanding should evaluate dedicated multimodal models instead of assuming GLM-4 itself supports those modalities.

This is a general AI limitation and is not model-specific unless stated by the provider: GLM-4 can hallucinate, misunderstand ambiguous prompts, or produce plausible but incorrect output. Legal, medical, financial, security, and safety-critical workflows require expert review and independent verification.

What Is GLM-4 Best Used For?

How Does GLM-4 Compare to DeepSeek-V3 and Qwen2.5-72B Instruct?

How Do I Access GLM-4?

Developers can access GLM-4 variants through Zhipu AI’s BigModel API. The official GLM-4 documentation provides chat completion examples using the endpoint /api/paas/v4/chat/completions, Bearer-token authentication, and the model ID glm-4-plus in examples.

Python Example

import os
from zai import ZhipuAiClient
client = ZhipuAiClient(api_key=os.environ["ZHIPUAI_API_KEY"])
response = client.chat.completions.create(
    model="glm-4-plus",
    messages=[
        {"role": "system", "content": "You are a concise technical assistant."},
        {"role": "user", "content": "Summarize GLM-4 in three bullet points."}
    ],
    max_tokens=1024,
    temperature=0.7
)
print(response.choices[0].message.content)

curl Example

curl -X POST "https://open.bigmodel.cn/api/paas/v4/chat/completions" \
  -H "Authorization: Bearer $ZHIPUAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "glm-4-plus",
    "messages": [
      {"role": "system", "content": "You are a concise technical assistant."},
      {"role": "user", "content": "Summarize GLM-4 in three bullet points."}
    ],
    "max_tokens": 1024,
    "temperature": 0.7
  }'

Production applications should choose the exact GLM-4 variant required by the workload. GLM-4-Plus, Air, AirX, FlashX, and Flash variants differ in context length, maximum output, speed positioning, concurrency, and price.

FAQs

What is GLM-4’s context window?

As of June 2026, BigModel lists 128K context for GLM-4-Plus, GLM-4-Air-250414, GLM-4-FlashX-250414, and GLM-4-Flash-250414. GLM-4-AirX is listed with an 8K context window.

How much does GLM-4 cost?

The checked GLM-4 page lists variant-level prices in CNY per million tokens: ¥5 for GLM-4-Plus, ¥0.5 for GLM-4-Air-250414, ¥10 for GLM-4-AirX, and ¥0.1 for GLM-4-FlashX-250414.

How do developers access GLM-4?

Developers can access GLM-4 through Zhipu AI’s BigModel API. Official examples use Bearer-token authentication, the chat completions endpoint, and model IDs such as glm-4-plus.

Is GLM-4 better than DeepSeek-V3 or Qwen2.5-72B?

There is no universal winner. GLM-4 may fit BigModel API and Chinese-English text workflows, DeepSeek-V3 may fit DeepSeek API users, and Qwen2.5-72B may fit teams that prefer open-weight deployment.