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GLM 5.3 Flash

Added Aug 26, 2026Open weights

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ox-alpha out of stealth! GLM-5.3 Flash is Z.ai's first natively multimodal GLM-5 model, with 320B total parameters and just 18B active parameters for efficient coding, agentic work, and precise 1M-token context. Its hybrid sparse-and-linear attention architecture helps it outperform GLM-5.2 at one-tenth the price while approaching Claude Opus 4.8 on coding and agentic benchmarks.

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

Pricing

USD per 1M tokens
Input
$0.10
Output
$0.30
Cache read
$0.025

Reference prices as published by the model provider. API example

Specifications

Context window
1M
Max output
131.1K
Average output (7 days)
1.3K tokens
Longer than 80% of 465 models

Side by side

GLM 5.3 Flash 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 FlashZ.aiThis modelGLM 4.7 FlashZ.aiRemove
ProviderZ.aiZ.ai
Discovery tierOpen weightsOpen weights
AddedAug 26, 2026Jan 19, 2026
Limits
Context window1.05MBest200K
Max output131.07KBest128K
Average output1.3K tokens86 tokens
Pricing, USD per 1M tokens
Input$0.10$0.07Best
Output$0.30Best$0.40
Cache read$0.025$0.01Best
Performance
Throughput105.3 tok/sBest51.4 tok/s
Time to first token3.3 sBest3.8 s
Request success (24h)99.6%100.0%Best
Benchmark indexes
Intelligence Index41.8Best10.6
Coding Index71.5—
Capabilities
ReasoningYesYes
ToolsYesYes
VisionYesNo
Structured outputYesYes
Video inputYesNo
Reasoning effortlow, high, max—

Public evaluations

Benchmarks

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

  • GLM 5.3 Flash
  • GLM 4.7 Flash

Intelligence Index

  • GLM 5.3 Flash41.8
  • GLM 4.7 Flash10.6

GLM 5.3 Flash: Better than 86% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • GLM 5.3 Flash: 71.5
    • GLM 4.7 Flash: —

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • GLM 5.3 Flash: 60.4%
    • GLM 4.7 Flash: —

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • GLM 5.3 Flash: 25.4%
    • GLM 4.7 Flash: —

  • Harvey LAB-AAv1.1

    Legal tasks passed in full, with no material hallucination

    • GLM 5.3 Flash: 0.8%
    • GLM 4.7 Flash: —

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • GLM 5.3 Flash: 1,452 Elo
    • GLM 4.7 Flash: —

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • GLM 5.3 Flash: 1,642 Elo
    • GLM 4.7 Flash: —

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • GLM 5.3 Flash: 15.4%
    • GLM 4.7 Flash: —

  • AA-LCRv1.1

    Reasoning across long documents

    • GLM 5.3 Flash: 80.0%Top
    • GLM 4.7 Flash: 20.3%

  • MLCR-AA

    Long-context reasoning over medical records

    • GLM 5.3 Flash: 11.7%
    • GLM 4.7 Flash: —

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • GLM 5.3 Flash: 39.9%Top
    • GLM 4.7 Flash: 5.0%

  • GPQA Diamond

    Graduate-level science questions

    • GLM 5.3 Flash: 91.2%Top
    • GLM 4.7 Flash: 45.2%

  • IFBench

    Following precise output instructions

    • GLM 5.3 Flash: —
    • GLM 4.7 Flash: 46.3%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • GLM 5.3 Flash: 32.8%
    • GLM 4.7 Flash: —

  • SciCode

    Scientific computing in Python

    • GLM 5.3 Flash: 51.6%
    • GLM 4.7 Flash: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • GLM 5.3 Flash: —
    • GLM 4.7 Flash: 3.8%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • GLM 5.3 Flash: —
    • GLM 4.7 Flash: 91.8%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • GLM 5.3 Flash: 80.0%Top
    • GLM 4.7 Flash: 20.3%

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
105.3 tok/s
Faster than 62% of 251 models
Time to first token
3.3 s
Quicker than 52% of 251 models
Prompt cache hits
82.1%
About $0.0384 per 1M input tokens with caching
Request success (24h)
99.6%
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 98.2%
  • 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",
    "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",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Z.ai

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