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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 5.3 ThinkingZ.aiRemove
ProviderZ.aiZ.ai
Discovery tierOpen weightsOpen weights
AddedDec 11, 2025Aug 14, 2026
Limits
Context window128K1.05MBest
Max output24K131.07KBest
Average output139 tokens2.3K tokens
Pricing, USD per 1M tokens
Input$0.30Best$0.60
Output$0.90Best$1.10
Cache read—$0.20
Performance
Throughput—119.4 tok/s
Time to first token—4.5 s
Request success (24h)99.7%99.7%
Benchmark indexes
Intelligence Index8.444.8Best
Coding Index—74.8
Math Index26.3—
Capabilities
ReasoningNoYes
ToolsNoYes
VisionYesNo
Structured outputNoYes
Video inputYesNo
Reasoning effort—high, max

Public evaluations

Benchmarks

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

  • GLM 4.6V
  • GLM 5.3 Thinking

Intelligence Index

  • GLM 4.6V8.4
  • GLM 5.3 Thinking44.8

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 5.3 Thinking: 74.8

  • Math Index

    Composite of the math evaluations

    • GLM 4.6V: 26.3
    • GLM 5.3 Thinking: —

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • GLM 4.6V: —
    • GLM 5.3 Thinking: 62.2%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • GLM 4.6V: —
    • GLM 5.3 Thinking: 29.4%

  • Harvey LAB-AAv1.1

    Legal tasks passed in full, with no material hallucination

    • GLM 4.6V: —
    • GLM 5.3 Thinking: 0.3%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • GLM 4.6V: —
    • GLM 5.3 Thinking: 1,505 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • GLM 4.6V: —
    • GLM 5.3 Thinking: 1,649 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • GLM 4.6V: —
    • GLM 5.3 Thinking: 11.2%

  • AA-LCRv1.1

    Reasoning across long documents

    • GLM 4.6V: 17.0%
    • GLM 5.3 Thinking: 79.7%Top

  • MLCR-AA

    Long-context reasoning over medical records

    • GLM 4.6V: —
    • GLM 5.3 Thinking: 17.2%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • GLM 4.6V: 3.7%
    • GLM 5.3 Thinking: 42.3%Top

  • GPQA Diamond

    Graduate-level science questions

    • GLM 4.6V: 56.6%
    • GLM 5.3 Thinking: 91.7%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • GLM 4.6V: 75.2%
    • GLM 5.3 Thinking: —

  • IFBench

    Following precise output instructions

    • GLM 4.6V: 27.9%
    • GLM 5.3 Thinking: —

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • GLM 4.6V: —
    • GLM 5.3 Thinking: 41.9%

  • SciCode

    Scientific computing in Python

    • GLM 4.6V: —
    • GLM 5.3 Thinking: 59.0%

  • LiveCodeBench

    Recent competitive programming problems

    • GLM 4.6V: 41.1%
    • GLM 5.3 Thinking: —

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • GLM 4.6V: 26.3%
    • GLM 5.3 Thinking: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • GLM 4.6V: 3.0%
    • GLM 5.3 Thinking: —

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • GLM 4.6V: 30.7%
    • GLM 5.3 Thinking: —

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • GLM 4.6V: 17.0%
    • GLM 5.3 Thinking: 79.7%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

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