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Z.ai · Open weights

GLM 4.6 Original

Added Dec 11, 2025Open weights

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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.

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GLM 4.6 OriginalZ.aiThis modelApex Flash 1Z.aiRemove
ProviderZ.aiZ.ai
Discovery tierOpen weightsOpen weights
AddedDec 11, 2025Oct 5, 2026
Limits
Context window256K1.05MBest
Max output65.54KBest32.77K
Average output1.3K tokens672 tokens
Pricing, USD per 1M tokens
Input$0.50$0.15Best
Output$2.00$0.50Best
Cache read$0.10$0.075Best
Performance
Throughput—14.1 tok/s
Time to first token—6.9 s
Request success (24h)99.4%Best94.6%
Benchmark indexes
Intelligence Index14.9—
Math Index44.3—
Capabilities
ReasoningYesYes
ToolsYesYes
VisionNoYes
Reasoning effort—low, high, max

Public evaluations

Benchmarks

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

  • GLM 4.6 Original
  • Apex Flash 1

Intelligence Index

  • GLM 4.6 Original14.9
  • Apex Flash 1—

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

3.8Catalog models by score55.1

Indexes

  • Math Index

    Composite of the math evaluations

    • GLM 4.6 Original: 44.3
    • Apex Flash 1: —

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • GLM 4.6 Original: 26.3%
    • Apex Flash 1: —

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • GLM 4.6 Original: 5.5%
    • Apex Flash 1: —

  • GPQA Diamond

    Graduate-level science questions

    • GLM 4.6 Original: 63.2%
    • Apex Flash 1: —

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • GLM 4.6 Original: 78.4%
    • Apex Flash 1: —

  • IFBench

    Following precise output instructions

    • GLM 4.6 Original: 36.7%
    • Apex Flash 1: —

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • GLM 4.6 Original: 56.1%
    • Apex Flash 1: —

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • GLM 4.6 Original: 44.3%
    • Apex Flash 1: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • GLM 4.6 Original: 28.8%
    • Apex Flash 1: —

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • GLM 4.6 Original: 76.9%
    • Apex Flash 1: —

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • GLM 4.6 Original: 26.3%
    • Apex Flash 1: —

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
24.4%
About $0.40 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

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