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Z.ai · Efficient

GLM 4.1V Thinking Flash

Added Jul 9, 2025

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Use in the app

Vision-Language Model with thinking paradigm and reinforcement learning. Achieves state-of-the-art performance among 10B-parameter VLMs. Supports 64k context length, handles arbitrary aspect ratios and up to 4K image resolution. Bilingual Chinese/English.

  • Vision
Input
Text, Images
Output
Text
Discovery tier
Efficient

Pricing

USD per 1M tokens
Input
$0.30
Output
$0.30

Reference prices as published by the model provider. API example

Specifications

Context window
64K
Max output
8.2K

Side by side

GLM 4.1V Thinking Flash compared

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

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GLM 4.1V Thinking FlashZ.aiThis modelGLM 5.3 FlashZ.aiRemove
ProviderZ.aiZ.ai
Discovery tierEfficientOpen weights
AddedJul 9, 2025Aug 26, 2026
Limits
Context window64K1.05MBest
Max output8.19K131.07KBest
Average output—1.3K tokens
Pricing, USD per 1M tokens
Input$0.30$0.10Best
Output$0.30$0.30
Cache read—$0.025
Performance
Throughput—104.6 tok/s
Time to first token—4.0 s
Request success (24h)—99.6%
Benchmark indexes
Intelligence Index—41.8
Coding Index—71.5
Capabilities
ReasoningNoYes
ToolsNoYes
VisionYesYes
Structured outputNoYes
Video inputNoYes
Reasoning effort—low, high, max

Public evaluations

Benchmarks

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

  • GLM 4.1V Thinking Flash
  • GLM 5.3 Flash

Intelligence Index

  • GLM 4.1V Thinking Flash—
  • GLM 5.3 Flash41.8
3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • GLM 4.1V Thinking Flash: —
    • GLM 5.3 Flash: 71.5

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • GLM 4.1V Thinking Flash: —
    • GLM 5.3 Flash: 60.4%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • GLM 4.1V Thinking Flash: —
    • GLM 5.3 Flash: 25.4%

  • Harvey LAB-AAv1.1

    Legal tasks passed in full, with no material hallucination

    • GLM 4.1V Thinking Flash: —
    • GLM 5.3 Flash: 0.8%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • GLM 4.1V Thinking Flash: —
    • GLM 5.3 Flash: 1,452 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • GLM 4.1V Thinking Flash: —
    • GLM 5.3 Flash: 1,642 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • GLM 4.1V Thinking Flash: —
    • GLM 5.3 Flash: 15.4%

  • AA-LCRv1.1

    Reasoning across long documents

    • GLM 4.1V Thinking Flash: —
    • GLM 5.3 Flash: 80.0%

  • MLCR-AA

    Long-context reasoning over medical records

    • GLM 4.1V Thinking Flash: —
    • GLM 5.3 Flash: 11.7%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • GLM 4.1V Thinking Flash: —
    • GLM 5.3 Flash: 39.9%

  • GPQA Diamond

    Graduate-level science questions

    • GLM 4.1V Thinking Flash: —
    • GLM 5.3 Flash: 91.2%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • GLM 4.1V Thinking Flash: —
    • GLM 5.3 Flash: 32.8%

  • SciCode

    Scientific computing in Python

    • GLM 4.1V Thinking Flash: —
    • GLM 5.3 Flash: 51.6%

Legacy evaluations

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • GLM 4.1V Thinking Flash: —
    • GLM 5.3 Flash: 80.0%

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

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": "glm-4.1v-thinking-flash",
    "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="glm-4.1v-thinking-flash",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

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