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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 modelQwen3 Coder FlashQwenRemove
ProviderZ.aiQwen
Discovery tierEfficientEfficient
AddedJul 9, 2025Sep 17, 2025
Limits
Context window64K128KBest
Max output8.19K65.54KBest
Pricing, USD per 1M tokens
Input$0.30$0.30
Output$0.30Best$1.50
Benchmark indexes
Intelligence Index—9.6
Math Index—29.0
Capabilities
VisionYesNo
Reasoning effort——

Public evaluations

Benchmarks

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

  • GLM 4.1V Thinking Flash
  • Qwen3 Coder Flash

Intelligence Index

  • GLM 4.1V Thinking Flash—
  • Qwen3 Coder Flash9.6
3.8Catalog models by score55.1

Indexes

  • Math Index

    Composite of the math evaluations

    • GLM 4.1V Thinking Flash: —
    • Qwen3 Coder Flash: 29.0

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • GLM 4.1V Thinking Flash: —
    • Qwen3 Coder Flash: 32.7%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • GLM 4.1V Thinking Flash: —
    • Qwen3 Coder Flash: 3.8%

  • GPQA Diamond

    Graduate-level science questions

    • GLM 4.1V Thinking Flash: —
    • Qwen3 Coder Flash: 51.6%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • GLM 4.1V Thinking Flash: —
    • Qwen3 Coder Flash: 70.6%

  • IFBench

    Following precise output instructions

    • GLM 4.1V Thinking Flash: —
    • Qwen3 Coder Flash: 32.7%

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • GLM 4.1V Thinking Flash: —
    • Qwen3 Coder Flash: 40.3%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • GLM 4.1V Thinking Flash: —
    • Qwen3 Coder Flash: 29.0%

  • AIME

    Competition mathematics

    • GLM 4.1V Thinking Flash: —
    • Qwen3 Coder Flash: 29.7%

  • MATH-500

    Competition math problems

    • GLM 4.1V Thinking Flash: —
    • Qwen3 Coder Flash: 89.3%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • GLM 4.1V Thinking Flash: —
    • Qwen3 Coder Flash: 15.2%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • GLM 4.1V Thinking Flash: —
    • Qwen3 Coder Flash: 34.5%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • GLM 4.1V Thinking Flash: —
    • Qwen3 Coder Flash: 32.7%

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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