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GLM 5.2 TEE

Added Jun 16, 2026Open weights

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GLM-5.2 is Z.AI's flagship model for long-horizon tasks and autonomous workflows, with a 1M-token context window. Running inside a TEE (Trusted Execution Environment), with attestation support.

  • Reasoning
  • Tools
  • Structured output
Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$1.40
Output
$4.40
Cache read
$0.70

Reference prices as published by the model provider. API example

Specifications

Context window
1M
Max output
131.1K
Average output (7 days)
1.5K tokens
Longer than 85% of 468 models

Side by side

GLM 5.2 TEE compared

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

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GLM 5.2 TEEZ.aiThis modelGLM 5.2 Thinking TEEZ.aiRemove
ProviderZ.aiZ.ai
Discovery tierOpen weightsOpen weights
AddedJun 16, 2026Jun 16, 2026
Limits
Context window1.05M1.05M
Max output131.07K131.07K
Average output1.5K tokens2K tokens
Pricing, USD per 1M tokens
Input$1.40$1.40
Output$4.40$4.40
Cache read$0.70$0.70
Performance
Request success (24h)66.6%87.5%Best
Benchmark indexes
Intelligence Index22.433.7Best
Coding Index46.568.8Best
Capabilities
ReasoningYesYes
ToolsYesYes
Structured outputYesYes
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 5.2 TEE
  • GLM 5.2 Thinking TEE

Intelligence Index

  • GLM 5.2 TEE22.4
  • GLM 5.2 Thinking TEE33.7

GLM 5.2 TEE: Better than 54% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • GLM 5.2 TEE: 46.5
    • GLM 5.2 Thinking TEE: 68.8Top

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

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

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

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

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

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

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • GLM 5.2 TEE: 1,244 Elo
    • GLM 5.2 Thinking TEE: 1,373 EloTop

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

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

  • AA-LCRv1.1

    Reasoning across long documents

    • GLM 5.2 TEE: 42.3%
    • GLM 5.2 Thinking TEE: 78.3%Top

  • MLCR-AA

    Long-context reasoning over medical records

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

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • GLM 5.2 TEE: 9.8%
    • GLM 5.2 Thinking TEE: 41.1%Top

  • GPQA Diamond

    Graduate-level science questions

    • GLM 5.2 TEE: 68.6%
    • GLM 5.2 Thinking TEE: 89.5%Top

  • IFBench

    Following precise output instructions

    • GLM 5.2 TEE: —
    • GLM 5.2 Thinking TEE: 73.3%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

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

  • SciCode

    Scientific computing in Python

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

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • GLM 5.2 TEE: —
    • GLM 5.2 Thinking TEE: 50.8%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • GLM 5.2 TEE: —
    • GLM 5.2 Thinking TEE: 99.1%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • GLM 5.2 TEE: 42.3%
    • 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.

Prompt cache hits
0.4%
About $1.40 per 1M input tokens with caching
Request success (24h)
66.6%

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.

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": "TEE/glm-5.2",
    "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="TEE/glm-5.2",
    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.