Z.ai · Efficient

GLM 4.1V Thinking Flash

Added Jul 9, 2025

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.

Limits
	GLM 4.1V Thinking FlashZ.aiThis model	ByteDance Seed 2.0 LitedoubaoRemove
Provider	Z.ai	doubao
Discovery tier	Efficient	Efficient
Added	Jul 9, 2025	Mar 10, 2026
Context window	64K	262.14KBest
Max output	8.19K	131.07KBest
Average output	—	132 tokens
Pricing, USD per 1M tokens
Input	$0.30	$0.25Best
Output	$0.30Best	$2.00
Capabilities
Vision	Yes	Yes
Structured output	No	Yes
Video input	No	Yes
Reasoning effort	—	—

Public evaluations

Benchmarks

There are no public benchmark results for these models yet.

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)

Z.ai

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