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GLM 4.7

Added Dec 22, 2025Open weights

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Use in the app

GLM-4.7 is a next-gen GLM series text model with stronger reasoning, long-context chat, and reliable tool use.

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

Pricing

USD per 1M tokens
Input
$0.40
Output
$1.93
Cache read
$0.0801

Reference prices as published by the model provider. API example

Specifications

Context window
200K
Max output
65.5K
Average output (7 days)
539 tokens
Longer than 51% of 465 models

Side by side

GLM 4.7 compared

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

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GLM 4.7Z.aiThis modelGLM 5.2Z.aiRemove
ProviderZ.aiZ.ai
Discovery tierOpen weightsOpen weights
AddedDec 22, 2025Jun 15, 2026
Limits
Context window200K1.05MBest
Max output65.54K131.07KBest
Average output539 tokens839 tokens
Pricing, USD per 1M tokens
Input$0.40Best$0.42
Output$1.93$1.32Best
Cache read$0.0801$0.078Best
Performance
Throughput74.0 tok/s106.8 tok/sBest
Time to first token2.4 s1.5 sBest
Request success (24h)99.8%Best99.6%
Benchmark indexes
Intelligence Index17.422.4Best
Coding Index—46.5
Math Index48.0—
Capabilities
ReasoningYesYes
ToolsYesYes
Structured outputYesYes
Reasoning effort—none, high, max

Public evaluations

Benchmarks

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

  • GLM 4.7
  • GLM 5.2

Intelligence Index

  • GLM 4.717.4
  • GLM 5.222.4

GLM 4.7: Better than 46% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • GLM 4.7: —
    • GLM 5.2: 46.5

  • Math Index

    Composite of the math evaluations

    • GLM 4.7: 48.0
    • GLM 5.2: —

Agentic work

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • GLM 4.7: —
    • GLM 5.2: 1,244 Elo

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • GLM 4.7: 40.7%
    • GLM 5.2: 42.3%Top

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • GLM 4.7: 6.4%
    • GLM 5.2: 9.8%Top

  • GPQA Diamond

    Graduate-level science questions

    • GLM 4.7: 66.4%
    • GLM 5.2: 68.6%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • GLM 4.7: 79.4%
    • GLM 5.2: —

  • IFBench

    Following precise output instructions

    • GLM 4.7: 54.6%
    • GLM 5.2: —

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • GLM 4.7: 56.2%
    • GLM 5.2: —

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • GLM 4.7: 48.0%
    • GLM 5.2: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • GLM 4.7: 30.3%
    • GLM 5.2: —

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • GLM 4.7: 94.2%
    • GLM 5.2: —

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • GLM 4.7: 40.7%
    • GLM 5.2: 42.3%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.

Throughput
74.0 tok/s
Faster than 50% of 250 models
Time to first token
2.4 s
Quicker than 68% of 250 models
Prompt cache hits
56.7%
About $0.22 per 1M input tokens with caching
Request success (24h)
99.8%
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 99.0%
  • 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-4.7",
    "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-4.7",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Z.ai

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