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GLM 5.3 TEE

Added Aug 31, 2026Open weights

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Use in the app

GLM-5.3 is Z.AI's open-weight reasoning model for complex software engineering, autonomous agents, vulnerability research, and long-horizon tasks. Reasoning is always on; choose low, high, or max reasoning effort (defaults to max). This text-only TEE deployment is verified through the selected provider: Redpill attestation with signed completion receipts or the official Tinfoil SDK's ATC/EHBP verification.

  • Reasoning
  • Tools
  • Structured output
  • Parallel tools
Input
Text
Output
Text
Reasoning effort
low · high · max
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$1.40
Output
$4.40
Cache read
$0.26

Reference prices as published by the model provider. API example

Specifications

Context window
1M
Max output
131.1K
Average output (7 days)
1.2K tokens
Longer than 77% of 465 models

Side by side

GLM 5.3 TEE compared

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

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GLM 5.3 TEEZ.aiThis modelDeepSeek V4.1 Flash 1017 ThinkingDeepSeekRemove
ProviderZ.aiDeepSeek
Discovery tierOpen weightsOpen weights
AddedAug 31, 2026Oct 17, 2026
Limits
Context window1.05M1.05M
Max output131.07K393.22KBest
Average output1.2K tokens1.2K tokens
Pricing, USD per 1M tokens
Input$1.40$0.10Best
Output$4.40$0.60Best
Cache read$0.26$0.003Best
Performance
Throughput48.0 tok/s230.5 tok/sBest
Time to first token4.6 s2.6 sBest
Request success (24h)99.7%99.8%Best
Benchmark indexes
Intelligence Index44.845.1Best
Coding Index74.8—
Capabilities
ReasoningYesYes
ToolsYesYes
VisionNoYes
Structured outputYesYes
Parallel toolsYesNo
Reasoning effortlow, high, maxnone, low, high, max

Public evaluations

Benchmarks

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

  • GLM 5.3 TEE
  • DeepSeek V4.1 Flash 1017 Thinking

Intelligence Index

  • GLM 5.3 TEE44.8
  • DeepSeek V4.1 Flash 1017 Thinking45.1

GLM 5.3 TEE tested at max reasoning effort

GLM 5.3 TEE: Better than 90% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • GLM 5.3 TEE: 74.8
    • DeepSeek V4.1 Flash 1017 Thinking: —

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • GLM 5.3 TEE: 62.2%
    • DeepSeek V4.1 Flash 1017 Thinking: 66.6%Top

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • GLM 5.3 TEE: 29.4%
    • DeepSeek V4.1 Flash 1017 Thinking: 36.7%Top

  • Harvey LAB-AAv1.1

    Legal tasks passed in full, with no material hallucination

    • GLM 5.3 TEE: 0.3%
    • DeepSeek V4.1 Flash 1017 Thinking: —

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • GLM 5.3 TEE: 1,505 Elo
    • DeepSeek V4.1 Flash 1017 Thinking: 1,570 EloTop

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • GLM 5.3 TEE: 1,649 Elo
    • DeepSeek V4.1 Flash 1017 Thinking: 1,746 EloTop

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • GLM 5.3 TEE: 11.2%
    • DeepSeek V4.1 Flash 1017 Thinking: 17.2%Top

  • AA-LCRv1.1

    Reasoning across long documents

    • GLM 5.3 TEE: 79.7%
    • DeepSeek V4.1 Flash 1017 Thinking: 82.7%Top

  • MLCR-AA

    Long-context reasoning over medical records

    • GLM 5.3 TEE: 17.2%
    • DeepSeek V4.1 Flash 1017 Thinking: 26.7%Top

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • GLM 5.3 TEE: 42.3%
    • DeepSeek V4.1 Flash 1017 Thinking: 43.3%Top

  • GPQA Diamond

    Graduate-level science questions

    • GLM 5.3 TEE: 91.7%
    • DeepSeek V4.1 Flash 1017 Thinking: —

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • GLM 5.3 TEE: 41.9%Top
    • DeepSeek V4.1 Flash 1017 Thinking: 33.8%

  • SciCode

    Scientific computing in Python

    • GLM 5.3 TEE: 59.0%Top
    • DeepSeek V4.1 Flash 1017 Thinking: 55.9%

Legacy evaluations

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • GLM 5.3 TEE: 79.7%
    • DeepSeek V4.1 Flash 1017 Thinking: 82.7%Top

Scores from Artificial Analysis, updated Oct 26, 2026. GLM 5.3 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.

Throughput
48.0 tok/s
Faster than 35% of 250 models
Time to first token
4.6 s
Quicker than 40% of 250 models
Prompt cache hits
87.5%
About $0.40 per 1M input tokens with caching
Request success (24h)
99.7%
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 96.8%
  • 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": "TEE/glm-5.3",
    "reasoning_effort": "low",
    "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-5.3",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

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