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GLM 4.6V

Added Dec 11, 2025Open weights

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GLM-4.6V scales its context window to 128k tokens in training, and achieves SoTA performance in visual understanding among models of similar parameter scales. Integrates native Function Calling capabilities, bridging 'visual perception' and 'executable action' for multimodal agents. Quantized at FP8.

  • Vision
  • Video input
Input
Text, Images, Video
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.30
Output
$0.90

Reference prices as published by the model provider. API example

Specifications

Context window
128K
Max output
24K
Average output (7 days)
139 tokens
Longer than 14% of 468 models

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GLM 4.6V compared

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

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GLM 4.6VZ.aiThis modelGLM 5.2 Thinking TEEZ.aiRemove
ProviderZ.aiZ.ai
Discovery tierOpen weightsOpen weights
AddedDec 11, 2025Jun 16, 2026
Limits
Context window128K1.05MBest
Max output24K131.07KBest
Average output139 tokens2K tokens
Pricing, USD per 1M tokens
Input$0.30Best$1.40
Output$0.90Best$4.40
Cache read—$0.70
Performance
Request success (24h)99.7%Best87.5%
Benchmark indexes
Intelligence Index8.433.7Best
Coding Index—68.8
Math Index26.3—
Capabilities
ReasoningNoYes
ToolsNoYes
VisionYesNo
Structured outputNoYes
Video inputYesNo
Reasoning effort—low, medium, high, xhigh, max

Public evaluations

Benchmarks

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

  • GLM 4.6V
  • GLM 5.2 Thinking TEE

Intelligence Index

  • GLM 4.6V8.4
  • GLM 5.2 Thinking TEE33.7

GLM 4.6V: Better than 13% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • GLM 4.6V: —
    • GLM 5.2 Thinking TEE: 68.8

  • Math Index

    Composite of the math evaluations

    • GLM 4.6V: 26.3
    • GLM 5.2 Thinking TEE: —

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • GLM 4.6V: —
    • GLM 5.2 Thinking TEE: 28.4%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • GLM 4.6V: —
    • GLM 5.2 Thinking TEE: 6.2%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • GLM 4.6V: —
    • GLM 5.2 Thinking TEE: 1,227 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • GLM 4.6V: —
    • GLM 5.2 Thinking TEE: 1,373 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • GLM 4.6V: —
    • GLM 5.2 Thinking TEE: 10.4%

  • AA-LCRv1.1

    Reasoning across long documents

    • GLM 4.6V: 17.0%
    • GLM 5.2 Thinking TEE: 78.3%Top

  • MLCR-AA

    Long-context reasoning over medical records

    • GLM 4.6V: —
    • GLM 5.2 Thinking TEE: 12.8%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • GLM 4.6V: 3.7%
    • GLM 5.2 Thinking TEE: 41.1%Top

  • GPQA Diamond

    Graduate-level science questions

    • GLM 4.6V: 56.6%
    • GLM 5.2 Thinking TEE: 89.5%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • GLM 4.6V: 75.2%
    • GLM 5.2 Thinking TEE: —

  • IFBench

    Following precise output instructions

    • GLM 4.6V: 27.9%
    • GLM 5.2 Thinking TEE: 73.3%Top

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • GLM 4.6V: —
    • GLM 5.2 Thinking TEE: 1.0%

  • SciCode

    Scientific computing in Python

    • GLM 4.6V: —
    • GLM 5.2 Thinking TEE: 51.2%

  • LiveCodeBench

    Recent competitive programming problems

    • GLM 4.6V: 41.1%
    • GLM 5.2 Thinking TEE: —

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • GLM 4.6V: 26.3%
    • GLM 5.2 Thinking TEE: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • GLM 4.6V: 3.0%
    • GLM 5.2 Thinking TEE: 50.8%Top

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • GLM 4.6V: 30.7%
    • GLM 5.2 Thinking TEE: 99.1%Top

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • GLM 4.6V: 17.0%
    • GLM 5.2 Thinking TEE: 78.3%Top

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.

Prompt cache hits
28.4%
Request success (24h)
99.7%
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 96.4%
  • 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-4.6v",
    "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-4.6v",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Z.ai

Other Z.ai models, starting with the same family.