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GLM 4.1V Thinking FlashX

Added Jul 9, 2025

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Use in the app

Enhanced version of GLM 4.1V Thinking Flash. Vision-Language Model with advanced reasoning for complex visual tasks, multimodal problem solving, and intelligent agents. Supports 64k context length and handles arbitrary aspect ratios up to 4K resolution.

  • Vision
Input
Text, Images
Output
Text
Discovery tier
Balanced

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 FlashX 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 FlashXZ.aiThis modelQwen3 VL 235B A22B Instruct OriginalQwenRemove
ProviderZ.aiQwen
Discovery tierBalancedBalanced
AddedJul 9, 2025Sep 25, 2025
Limits
Context window64K—
Max output8.19K32.77KBest
Average output—288 tokens
Pricing, USD per 1M tokens
Input$0.30$0.30
Output$0.30Best$1.90
Performance
Request success (24h)—100.0%
Benchmark indexes
Intelligence Index—9.9
Math Index—70.7
Capabilities
VisionYesYes
Reasoning effort——

Public evaluations

Benchmarks

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

  • GLM 4.1V Thinking FlashX
  • Qwen3 VL 235B A22B Instruct Original

Intelligence Index

  • GLM 4.1V Thinking FlashX—
  • Qwen3 VL 235B A22B Instruct Original9.9
3.8Catalog models by score55.1

Indexes

  • Math Index

    Composite of the math evaluations

    • GLM 4.1V Thinking FlashX: —
    • Qwen3 VL 235B A22B Instruct Original: 70.7

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • GLM 4.1V Thinking FlashX: —
    • Qwen3 VL 235B A22B Instruct Original: 32.7%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • GLM 4.1V Thinking FlashX: —
    • Qwen3 VL 235B A22B Instruct Original: 6.6%

  • GPQA Diamond

    Graduate-level science questions

    • GLM 4.1V Thinking FlashX: —
    • Qwen3 VL 235B A22B Instruct Original: 71.2%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • GLM 4.1V Thinking FlashX: —
    • Qwen3 VL 235B A22B Instruct Original: 82.3%

  • IFBench

    Following precise output instructions

    • GLM 4.1V Thinking FlashX: —
    • Qwen3 VL 235B A22B Instruct Original: 42.7%

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • GLM 4.1V Thinking FlashX: —
    • Qwen3 VL 235B A22B Instruct Original: 59.4%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • GLM 4.1V Thinking FlashX: —
    • Qwen3 VL 235B A22B Instruct Original: 70.7%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • GLM 4.1V Thinking FlashX: —
    • Qwen3 VL 235B A22B Instruct Original: 6.8%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • GLM 4.1V Thinking FlashX: —
    • Qwen3 VL 235B A22B Instruct Original: 35.1%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • GLM 4.1V Thinking FlashX: —
    • Qwen3 VL 235B A22B Instruct Original: 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-flashx",
    "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-flashx",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

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