NOUMENOpen app
NOUMEN

Z.ai · Open weights

GLM 4.6 Original

Added Dec 11, 2025Open weights

Compare models

Choose up to 3 models to compare.1 of 3 chosen

Loading models…

Use in the app

GLM-4.6, Zhipu's flagship text model with 256K context window and advanced reasoning capabilities. Direct via Z-AI (Zhipu).

  • Reasoning
  • Tools
Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.50
Output
$2.00
Cache read
$0.10

Reference prices as published by the model provider. API example

Specifications

Context window
256K
Max output
65.5K
Average output (7 days)
1.3K tokens
Longer than 81% of 468 models

Side by side

GLM 4.6 Original compared

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

Compare models

Choose up to 3 models to compare.1 of 3 chosen

Loading models…

GLM 4.6 OriginalZ.aiThis modelGemma 4 31B IT TEEGoogleRemove
ProviderZ.aiGoogle
Discovery tierOpen weightsOpen weights
AddedDec 11, 2025May 26, 2026
Limits
Context window256K262.14KBest
Max output65.54K262.14KBest
Average output1.3K tokens269 tokens
Pricing, USD per 1M tokens
Input$0.50$0.15Best
Output$2.00$0.46Best
Cache read$0.10—
Performance
Throughput—56.1 tok/s
Time to first token—6.0 s
Request success (24h)99.4%99.8%Best
Benchmark indexes
Intelligence Index14.9Best14.7
Coding Index—43.4
Math Index44.3—
Capabilities
ReasoningYesYes
ToolsYesYes
Reasoning effort——

Public evaluations

Benchmarks

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

  • GLM 4.6 Original
  • Gemma 4 31B IT TEE

Intelligence Index

  • GLM 4.6 Original14.9
  • Gemma 4 31B IT TEE14.7

GLM 4.6 Original: Better than 37% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • GLM 4.6 Original: —
    • Gemma 4 31B IT TEE: 43.4

  • Math Index

    Composite of the math evaluations

    • GLM 4.6 Original: 44.3
    • Gemma 4 31B IT TEE: —

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • GLM 4.6 Original: —
    • Gemma 4 31B IT TEE: 4.9%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • GLM 4.6 Original: —
    • Gemma 4 31B IT TEE: 0.5%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • GLM 4.6 Original: —
    • Gemma 4 31B IT TEE: 368 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • GLM 4.6 Original: —
    • Gemma 4 31B IT TEE: 619 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • GLM 4.6 Original: —
    • Gemma 4 31B IT TEE: 6.0%

  • AA-LCRv1.1

    Reasoning across long documents

    • GLM 4.6 Original: 26.3%
    • Gemma 4 31B IT TEE: 69.7%Top

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • GLM 4.6 Original: 5.5%
    • Gemma 4 31B IT TEE: 23.6%Top

  • GPQA Diamond

    Graduate-level science questions

    • GLM 4.6 Original: 63.2%
    • Gemma 4 31B IT TEE: 85.7%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • GLM 4.6 Original: 78.4%
    • Gemma 4 31B IT TEE: —

  • IFBench

    Following precise output instructions

    • GLM 4.6 Original: 36.7%
    • Gemma 4 31B IT TEE: 75.6%Top

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • GLM 4.6 Original: —
    • Gemma 4 31B IT TEE: 0.0%

  • SciCode

    Scientific computing in Python

    • GLM 4.6 Original: —
    • Gemma 4 31B IT TEE: 45.5%

  • LiveCodeBench

    Recent competitive programming problems

    • GLM 4.6 Original: 56.1%
    • Gemma 4 31B IT TEE: —

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • GLM 4.6 Original: 44.3%
    • Gemma 4 31B IT TEE: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • GLM 4.6 Original: 28.8%
    • Gemma 4 31B IT TEE: 36.4%Top

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • GLM 4.6 Original: 76.9%Top
    • Gemma 4 31B IT TEE: 59.9%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • GLM 4.6 Original: 26.3%
    • Gemma 4 31B IT TEE: 69.7%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
29.1%
About $0.38 per 1M input tokens with caching
Request success (24h)
99.4%
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 97.3%
  • 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-4.6-original",
    "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-4.6-original",
    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.