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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 modelGemini 3.5 Flash LiteGoogleRemove
ProviderZ.aiGoogle
Discovery tierOpen weightsEfficient
AddedJun 16, 2026Jul 21, 2026
Limits
Context window1.05M1.05M
Max output131.07KBest65.54K
Average output1.5K tokens178 tokens
Pricing, USD per 1M tokens
Input$1.40$0.30Best
Output$4.40$2.50Best
Cache read$0.70$0.03Best
Cache write—$0.0833
Performance
Throughput—220.7 tok/s
Time to first token—2.2 s
Request success (24h)66.6%99.8%Best
Benchmark indexes
Intelligence Index22.4Best22.2
Coding Index46.549.3Best
Capabilities
ReasoningYesYes
ToolsYesYes
VisionNoYes
Structured outputYesYes
PDF inputNoYes
Audio inputNoYes
Video inputNoYes
Reasoning effort—minimal, low, medium, high

Public evaluations

Benchmarks

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

  • GLM 5.2 TEE
  • Gemini 3.5 Flash Lite

Intelligence Index

  • GLM 5.2 TEE22.4
  • Gemini 3.5 Flash Lite22.2

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
    • Gemini 3.5 Flash Lite: 49.3Top

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • GLM 5.2 TEE: —
    • Gemini 3.5 Flash Lite: 25.0%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • GLM 5.2 TEE: —
    • Gemini 3.5 Flash Lite: 3.3%

  • Harvey LAB-AAv1.1

    Legal tasks passed in full, with no material hallucination

    • GLM 5.2 TEE: —
    • Gemini 3.5 Flash Lite: 0.0%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • GLM 5.2 TEE: —
    • Gemini 3.5 Flash Lite: 650 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • GLM 5.2 TEE: 1,244 EloTop
    • Gemini 3.5 Flash Lite: 985 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • GLM 5.2 TEE: —
    • Gemini 3.5 Flash Lite: 13.6%

  • AA-LCRv1.1

    Reasoning across long documents

    • GLM 5.2 TEE: 42.3%
    • Gemini 3.5 Flash Lite: 76.0%Top

  • MLCR-AA

    Long-context reasoning over medical records

    • GLM 5.2 TEE: —
    • Gemini 3.5 Flash Lite: 10.6%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • GLM 5.2 TEE: 9.8%
    • Gemini 3.5 Flash Lite: 18.8%Top

  • GPQA Diamond

    Graduate-level science questions

    • GLM 5.2 TEE: 68.6%
    • Gemini 3.5 Flash Lite: 83.8%Top

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • GLM 5.2 TEE: —
    • Gemini 3.5 Flash Lite: 1.0%

  • SciCode

    Scientific computing in Python

    • GLM 5.2 TEE: —
    • Gemini 3.5 Flash Lite: 41.3%

Legacy evaluations

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • GLM 5.2 TEE: 42.3%
    • Gemini 3.5 Flash Lite: 76.0%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.