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GLM 4.5

Added Apr 15, 2025Open weights

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GLM-4.5 is a Mixture-of-Experts model built for agent-based applications, with 355B total / 32B active parameters and up to 128k context. It supports reasoning, code generation, and agent workflows.

Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.30
Output
$1.30

Reference prices as published by the model provider. API example

Specifications

Context window
128K
Max output
65.5K
Average output (7 days)
696 tokens
Longer than 62% of 468 models

Side by side

GLM 4.5 compared

Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.

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GLM 4.5Z.aiThis modelGLM 5.2 Thinking TEEZ.aiRemove
ProviderZ.aiZ.ai
Discovery tierOpen weightsOpen weights
AddedApr 15, 2025Jun 16, 2026
Limits
Context window128K1.05MBest
Max output65.54K131.07KBest
Average output696 tokens2K tokens
Pricing, USD per 1M tokens
Input$0.30Best$1.40
Output$1.30Best$4.40
Cache read—$0.70
Performance
Throughput35.7 tok/s—
Time to first token9.6 s—
Request success (24h)100.0%Best87.5%
Benchmark indexes
Intelligence Index12.833.7Best
Coding Index—68.8
Math Index73.7—
Capabilities
ReasoningNoYes
ToolsNoYes
Structured outputNoYes
Reasoning effort—low, medium, high, xhigh, max

Public evaluations

Benchmarks

Independent evaluation results, with how each score ranks among the models in the Noumen catalog.

  • GLM 4.5
  • GLM 5.2 Thinking TEE

Intelligence Index

  • GLM 4.512.8
  • GLM 5.2 Thinking TEE33.7

GLM 4.5: Better than 29% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • GLM 4.5: —
    • GLM 5.2 Thinking TEE: 68.8

  • Math Index

    Composite of the math evaluations

    • GLM 4.5: 73.7
    • GLM 5.2 Thinking TEE: —

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • GLM 4.5: —
    • GLM 5.2 Thinking TEE: 28.4%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • GLM 4.5: —
    • GLM 5.2 Thinking TEE: 6.2%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • GLM 4.5: —
    • GLM 5.2 Thinking TEE: 1,227 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • GLM 4.5: —
    • GLM 5.2 Thinking TEE: 1,373 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • GLM 4.5: —
    • GLM 5.2 Thinking TEE: 10.4%

  • AA-LCRv1.1

    Reasoning across long documents

    • GLM 4.5: 52.7%
    • GLM 5.2 Thinking TEE: 78.3%Top

  • MLCR-AA

    Long-context reasoning over medical records

    • GLM 4.5: —
    • GLM 5.2 Thinking TEE: 12.8%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • GLM 4.5: 13.0%
    • GLM 5.2 Thinking TEE: 41.1%Top

  • GPQA Diamond

    Graduate-level science questions

    • GLM 4.5: 78.2%
    • GLM 5.2 Thinking TEE: 89.5%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • GLM 4.5: 83.5%
    • GLM 5.2 Thinking TEE: —

  • IFBench

    Following precise output instructions

    • GLM 4.5: 44.1%
    • GLM 5.2 Thinking TEE: 73.3%Top

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • GLM 4.5: —
    • GLM 5.2 Thinking TEE: 1.0%

  • SciCode

    Scientific computing in Python

    • GLM 4.5: —
    • GLM 5.2 Thinking TEE: 51.2%

  • LiveCodeBench

    Recent competitive programming problems

    • GLM 4.5: 73.8%
    • GLM 5.2 Thinking TEE: —

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • GLM 4.5: 73.7%
    • GLM 5.2 Thinking TEE: —

  • AIME

    Competition mathematics

    • GLM 4.5: 87.3%
    • GLM 5.2 Thinking TEE: —

  • MATH-500

    Competition math problems

    • GLM 4.5: 97.9%
    • GLM 5.2 Thinking TEE: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • GLM 4.5: 22.0%
    • GLM 5.2 Thinking TEE: 50.8%Top

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • GLM 4.5: 43.0%
    • GLM 5.2 Thinking TEE: 99.1%Top

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • GLM 4.5: 52.7%
    • GLM 5.2 Thinking TEE: 78.3%Top

Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis

Live routing data

Performance and reliability

Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.

Throughput
35.7 tok/s
Faster than 25% of 254 models
Time to first token
9.6 s
Quicker than 11% of 254 models
Prompt cache hits
45.5%
Request success (24h)
100.0%
Reliable

Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.

Hourly request success · last 72 hoursLowest hour 100.0%
  • Healthy (98% or more)
  • Degraded (90–98%)
  • Down (under 90%)
  • No requests

Developers

Use it through the API

Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.

Read the quickstart
curl https://www.noumen.xyz/api/v1/chat/completions \
  -H "Authorization: Bearer $NOUMEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "z-ai/glm-4.5",
    "messages": [{ "role": "user", "content": "Explain this stack trace." }]
  }'
import os
from openai import OpenAI

client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
    model="z-ai/glm-4.5",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Z.ai

Other Z.ai models, starting with the same family.