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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 modelStep 5 PreviewstepfunRemove
ProviderZ.aistepfun
Discovery tierOpen weightsBalanced
AddedAug 31, 2026Sep 20, 2026
Limits
Context window1.05MBest1M
Max output131.07KBest65.54K
Average output1.2K tokens1.5K tokens
Pricing, USD per 1M tokens
Input$1.40$1.00Best
Output$4.40$2.70Best
Cache read$0.26$0.05Best
Performance
Throughput48.0 tok/s—
Time to first token4.6 s—
Request success (24h)99.7%100.0%Best
Benchmark indexes
Intelligence Index44.8Best43.7
Coding Index74.8—
Capabilities
ReasoningYesYes
ToolsYesYes
VisionNoYes
Structured outputYesYes
Video inputNoYes
Parallel toolsYesYes
Reasoning effortlow, high, maxlow, medium, high

Public evaluations

Benchmarks

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

  • GLM 5.3 TEE
  • Step 5 Preview

Intelligence Index

  • GLM 5.3 TEE44.8
  • Step 5 Preview43.7

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
    • Step 5 Preview: —

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • GLM 5.3 TEE: 62.2%Top
    • Step 5 Preview: 51.0%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • GLM 5.3 TEE: 29.4%Top
    • Step 5 Preview: 17.7%

  • Harvey LAB-AAv1.1

    Legal tasks passed in full, with no material hallucination

    • GLM 5.3 TEE: 0.3%
    • Step 5 Preview: —

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • GLM 5.3 TEE: 1,505 EloTop
    • Step 5 Preview: 1,423 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • GLM 5.3 TEE: 1,649 EloTop
    • Step 5 Preview: 1,587 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • GLM 5.3 TEE: 11.2%
    • Step 5 Preview: 14.8%Top

  • AA-LCRv1.1

    Reasoning across long documents

    • GLM 5.3 TEE: 79.7%
    • Step 5 Preview: 88.3%Top

  • MLCR-AA

    Long-context reasoning over medical records

    • GLM 5.3 TEE: 17.2%
    • Step 5 Preview: 18.3%Top

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • GLM 5.3 TEE: 42.3%
    • Step 5 Preview: 46.5%Top

  • GPQA Diamond

    Graduate-level science questions

    • GLM 5.3 TEE: 91.7%
    • Step 5 Preview: —

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • GLM 5.3 TEE: 41.9%Top
    • Step 5 Preview: 33.3%

  • SciCode

    Scientific computing in Python

    • GLM 5.3 TEE: 59.0%Top
    • Step 5 Preview: 58.9%

Legacy evaluations

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • GLM 5.3 TEE: 79.7%
    • Step 5 Preview: 88.3%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
87.4%
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)

Z.ai

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