GLM 5.3 Prime
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GLM 5.3 Prime

glm-5.3-primellms.txt
Z.AI
New
GLM-5.3-Prime is the high-speed version of GLM-5.3, with comprehensive advancements in programming and agent capabilities that reach open-source SOTA levels. Its internal programming benchmarks are 50% better than the previous generation, and it can deliver end-to-end production-grade results. It exhibits emergent cybersecurity capabilities, with the CyberGym vulnerability-discovery benchmark achieving the current best. It supports a 1M-token context window, 128K-token output, and three tiers of deep reasoning, making it suitable for long-range, complex tasks.

Pricing

  • Input Tokens: $2.35 /M tokens
  • Output Tokens: $8.23 /M tokens
  • Cache Read: $0.59 /M tokens

Input Modalities

  • Text

Output Modalities

  • Text

Providers

Alibaba Cloud alicloud-glm-5.3-prime
Pricing$2.35$8.23
Cache$0.59
PrecisionⓘNot Disclosed
Latency-
Throughput-
Uptime
0.00% uptime 2 days ago
0.00% uptime yesterday
0.00% uptime today

Performance for glm-5.3-prime

Uptime is the percentage of requests that succeeded over the past 72 hours. AIHubMix continuously monitors every provider and automatically retries with the next-best provider when one returns an error or responds too slowly; Latency is total round-trip time (lower is better); Throughput is how fast the model writes (tokens per second, higher is better).

Uptime
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Latency
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Throughput
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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-5.3-prime",
    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 5.3 Prime?

GLM-5.3-Prime is the high-speed version of GLM-5.3, with comprehensive advancements in programming and agent capabilities that reach open-source SOTA levels. Its internal programming benchmarks are 50% better than the previous generation, and it can deliver end-to-end production-grade results. It exhibits emergent cybersecurity capabilities, with the CyberGym vulnerability-discovery benchmark achieving the current best. It supports a 1M-token context window, 128K-token output, and three tiers of deep reasoning, making it suitable for long-range, complex tasks.