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GLM 5.2 Thinking TEE

Added Jun 16, 2026Open weights

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GLM-5.2 with thinking enabled for harder long-horizon coding, autonomous agent workflows, and complex engineering tasks. Running inside a TEE (Trusted Execution Environment), with attestation support.

  • Reasoning
  • Tools
  • Structured output
Input
Text
Output
Text
Reasoning effort
low · medium · high · xhigh · max
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)
2K tokens
Longer than 92% of 468 models

Side by side

GLM 5.2 Thinking 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 Thinking TEEZ.aiThis modelMuse Spark 1.1MetaRemove
ProviderZ.aiMeta
Discovery tierOpen weightsBalanced
AddedJun 16, 2026Jul 9, 2026
Limits
Context window1.05MBest1M
Max output131.07KBest65.54K
Average output2K tokens460 tokens
Pricing, USD per 1M tokens
Input$1.40$1.25Best
Output$4.40$4.25Best
Cache read$0.70$0.15Best
Performance
Request success (24h)87.5%—
Benchmark indexes
Intelligence Index33.733.7
Coding Index68.871.3Best
Capabilities
ReasoningYesYes
ToolsYesNo
VisionNoYes
Structured outputYesNo
Audio inputNoYes
Video inputNoYes
Reasoning effortlow, medium, high, xhigh, maxminimal, low, medium, high, xhigh

Public evaluations

Benchmarks

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

  • GLM 5.2 Thinking TEE
  • Muse Spark 1.1

Intelligence Index

  • GLM 5.2 Thinking TEE33.7
  • Muse Spark 1.133.7

GLM 5.2 Thinking TEE tested at max reasoning effort

GLM 5.2 Thinking TEE: Better than 76% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • GLM 5.2 Thinking TEE: 68.8
    • Muse Spark 1.1: 71.3Top

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • GLM 5.2 Thinking TEE: 28.4%
    • Muse Spark 1.1: 38.8%Top

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • GLM 5.2 Thinking TEE: 6.2%
    • Muse Spark 1.1: 13.7%Top

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • GLM 5.2 Thinking TEE: 1,227 EloTop
    • Muse Spark 1.1: 850 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • GLM 5.2 Thinking TEE: 1,373 EloTop
    • Muse Spark 1.1: 1,211 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • GLM 5.2 Thinking TEE: 10.4%
    • Muse Spark 1.1: 14.4%Top

  • AA-LCRv1.1

    Reasoning across long documents

    • GLM 5.2 Thinking TEE: 78.3%Top
    • Muse Spark 1.1: 77.7%

  • MLCR-AA

    Long-context reasoning over medical records

    • GLM 5.2 Thinking TEE: 12.8%Top
    • Muse Spark 1.1: 7.2%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • GLM 5.2 Thinking TEE: 41.1%
    • Muse Spark 1.1: 46.2%Top

  • GPQA Diamond

    Graduate-level science questions

    • GLM 5.2 Thinking TEE: 89.5%
    • Muse Spark 1.1: 89.8%Top

  • IFBench

    Following precise output instructions

    • GLM 5.2 Thinking TEE: 73.3%
    • Muse Spark 1.1: —

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • GLM 5.2 Thinking TEE: 1.0%
    • Muse Spark 1.1: 6.1%Top

  • SciCode

    Scientific computing in Python

    • GLM 5.2 Thinking TEE: 51.2%
    • Muse Spark 1.1: 58.8%Top

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • GLM 5.2 Thinking TEE: 50.8%
    • Muse Spark 1.1: —

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • GLM 5.2 Thinking TEE: 99.1%
    • Muse Spark 1.1: —

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • GLM 5.2 Thinking TEE: 78.3%Top
    • Muse Spark 1.1: 77.7%

Scores from Artificial Analysis, updated Oct 26, 2026. GLM 5.2 Thinking TEE was tested at max reasoning effort; compare scores measured at the same effort.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
45.3%
About $1.08 per 1M input tokens with caching
Request success (24h)
87.5%

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:thinking",
    "reasoning_effort": "medium",
    "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:thinking",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Z.ai

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