NOUMENOpen app
NOUMEN

Z.ai · Open weights

GLM 5.3 Flash Cybersecurity

Added Sep 19, 2026Open weights

Compare models

Choose up to 3 models to compare.1 of 3 chosen

Loading models…

Use in the app

GLM 5.3 Flash Cybersecurity is a cybersecurity-focused variant based on the uncensored model, with provider moderation for illegal activities. It supports always-on reasoning, image understanding, tool calling, and a 1,048,576-token context window.

  • Reasoning
  • Tools
  • Vision
Input
Text, Images
Output
Text
Reasoning effort
low · high · max
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.15
Output
$0.50
Cache read
$0.075

Reference prices as published by the model provider. API example

Specifications

Context window
1M
Max output
32.8K
Average output (7 days)
417 tokens
Longer than 42% of 468 models

Side by side

GLM 5.3 Flash Cybersecurity compared

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

Compare models

Choose up to 3 models to compare.1 of 3 chosen

Loading models…

GLM 5.3 Flash CybersecurityZ.aiThis modelGLM 5.3 TEEZ.aiRemove
ProviderZ.aiZ.ai
Discovery tierOpen weightsOpen weights
AddedSep 19, 2026Aug 31, 2026
Limits
Context window1.05M1.05M
Max output32.77K131.07KBest
Average output417 tokens1.2K tokens
Pricing, USD per 1M tokens
Input$0.15Best$1.40
Output$0.50Best$4.40
Cache read$0.075Best$0.26
Performance
Throughput13.3 tok/s38.5 tok/sBest
Time to first token6.7 s4.4 sBest
Request success (24h)96.1%99.7%Best
Benchmark indexes
Intelligence Index—44.8
Coding Index—74.8
Capabilities
ReasoningYesYes
ToolsYesYes
VisionYesNo
Structured outputNoYes
Parallel toolsNoYes
Reasoning effortlow, high, maxlow, high, max

Public evaluations

Benchmarks

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

  • GLM 5.3 Flash Cybersecurity
  • GLM 5.3 TEE

Intelligence Index

  • GLM 5.3 Flash Cybersecurity—
  • GLM 5.3 TEE44.8
3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • GLM 5.3 Flash Cybersecurity: —
    • GLM 5.3 TEE: 74.8

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • GLM 5.3 Flash Cybersecurity: —
    • GLM 5.3 TEE: 62.2%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • GLM 5.3 Flash Cybersecurity: —
    • GLM 5.3 TEE: 29.4%

  • Harvey LAB-AAv1.1

    Legal tasks passed in full, with no material hallucination

    • GLM 5.3 Flash Cybersecurity: —
    • GLM 5.3 TEE: 0.3%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • GLM 5.3 Flash Cybersecurity: —
    • GLM 5.3 TEE: 1,505 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • GLM 5.3 Flash Cybersecurity: —
    • GLM 5.3 TEE: 1,649 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • GLM 5.3 Flash Cybersecurity: —
    • GLM 5.3 TEE: 11.2%

  • AA-LCRv1.1

    Reasoning across long documents

    • GLM 5.3 Flash Cybersecurity: —
    • GLM 5.3 TEE: 79.7%

  • MLCR-AA

    Long-context reasoning over medical records

    • GLM 5.3 Flash Cybersecurity: —
    • GLM 5.3 TEE: 17.2%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • GLM 5.3 Flash Cybersecurity: —
    • GLM 5.3 TEE: 42.3%

  • GPQA Diamond

    Graduate-level science questions

    • GLM 5.3 Flash Cybersecurity: —
    • GLM 5.3 TEE: 91.7%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • GLM 5.3 Flash Cybersecurity: —
    • GLM 5.3 TEE: 41.9%

  • SciCode

    Scientific computing in Python

    • GLM 5.3 Flash Cybersecurity: —
    • GLM 5.3 TEE: 59.0%

Legacy evaluations

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • GLM 5.3 Flash Cybersecurity: —
    • GLM 5.3 TEE: 79.7%

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.

Throughput
13.3 tok/s
Faster than 4% of 254 models
Time to first token
6.7 s
Quicker than 23% of 254 models
Prompt cache hits
93.5%
About $0.0798 per 1M input tokens with caching
Request success (24h)
96.1%
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 50.9%
  • 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": "z-ai/glm-5.3-flash-cybersecurity",
    "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="z-ai/glm-5.3-flash-cybersecurity",
    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.