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glm-5.2-fast-preview
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GLM 5.2 Fast Preview

glm-5.2-fast-preview
Z.AI
GLM-5.2-Fast-Preview is the high-speed version of Zhipu AI’s flagship model GLM-5.2, supporting a 1M ultra-long context. The model’s capabilities are aligned with the GLM-5.2 standard version, offering logical reasoning, long-text understanding, and code generation. Through inference acceleration optimizations, output TPS can reach 1.5–2× that of the GLM-5.2 standard version, significantly improving output speed. It is suitable for scenarios sensitive to output speed, such as real-time dialogue, multi-turn Agent calls, and streaming code generation.

Pricing

  • Input Tokens: $2.254 /M tokens
  • Output Tokens: $7.889 /M tokens
  • Cache Read: $0.564 /M tokens

Input Modalities

  • Text

Output Modalities

  • Text

Context length

  • 1M tokens

Max output

  • 131K tokens

Capabilities

  • Thinking
  • Streaming
  • Tool calling
  • Web search
  • URL context
  • Code interpreter
  • Computer use
  • File search
  • Memory tool
  • Structured outputs
  • Citations
  • Prompt caching
  • Background mode
  • Server-side sessions

Try this model

Python
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["AIHUBMIX_API_KEY"],
    base_url="https://aihubmix.com/v1",
)

response = client.chat.completions.create(
    model="glm-5.2-fast-preview",
    messages=[
      {
        "role": "user",
        "content": "Hello, how are you?"
      }
    ],
    max_tokens=1024,
    stream=False,
)

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

Frequently asked questions

What is glm-5.2-fast-preview?

GLM-5.2-Fast-Preview is the high-speed version of Zhipu AI’s flagship model GLM-5.2, supporting a 1M ultra-long context. The model’s capabilities are aligned with the GLM-5.2 standard version, offering logical reasoning, long-text understanding, and code generation. Through inference acceleration optimizations, output TPS can reach 1.5–2× that of the GLM-5.2 standard version, significantly improving output speed. It is suitable for scenarios sensitive to output speed, such as real-time dialogue, multi-turn Agent calls, and streaming code generation.
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