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GLM 5.2 Thinking

Added Jun 15, 2026Open weights

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Use in the app

GLM-5.2 with thinking enabled for harder long-horizon coding, autonomous agent workflows, complex engineering optimization, and real-world development tasks.

  • Reasoning
  • Tools
  • Structured output
Input
Text
Output
Text
Reasoning effort
high · max
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.42
Output
$1.32
Cache read
$0.078

Reference prices as published by the model provider. API example

Specifications

Context window
1M
Max output
131.1K
Average output (7 days)
1.8K tokens
Longer than 89% of 468 models

Side by side

GLM 5.2 Thinking compared

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

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GLM 5.2 ThinkingZ.aiThis modelMuse Spark 1.1MetaRemove
ProviderZ.aiMeta
Discovery tierOpen weightsBalanced
AddedJun 15, 2026Jul 9, 2026
Limits
Context window1.05MBest1M
Max output131.07KBest65.54K
Average output1.8K tokens460 tokens
Pricing, USD per 1M tokens
Input$0.42Best$1.25
Output$1.32Best$4.25
Cache read$0.078Best$0.15
Performance
Throughput108.0 tok/s—
Time to first token2.0 s—
Request success (24h)99.7%—
Benchmark indexes
Intelligence Index33.733.7
Coding Index68.871.3Best
Capabilities
ReasoningYesYes
ToolsYesNo
VisionNoYes
Structured outputYesNo
Audio inputNoYes
Video inputNoYes
Reasoning efforthigh, maxminimal, low, medium, high, xhigh

Public evaluations

Benchmarks

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

  • GLM 5.2 Thinking
  • Muse Spark 1.1

Intelligence Index

  • GLM 5.2 Thinking33.7
  • Muse Spark 1.133.7

GLM 5.2 Thinking tested at max reasoning effort

GLM 5.2 Thinking: Better than 76% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • GLM 5.2 Thinking: 68.8
    • Muse Spark 1.1: 71.3Top

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • GLM 5.2 Thinking: 28.4%
    • Muse Spark 1.1: 38.8%Top

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • GLM 5.2 Thinking: 6.2%
    • Muse Spark 1.1: 13.7%Top

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • GLM 5.2 Thinking: 1,227 EloTop
    • Muse Spark 1.1: 850 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • GLM 5.2 Thinking: 1,373 EloTop
    • Muse Spark 1.1: 1,211 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • GLM 5.2 Thinking: 10.4%
    • Muse Spark 1.1: 14.4%Top

  • AA-LCRv1.1

    Reasoning across long documents

    • GLM 5.2 Thinking: 78.3%Top
    • Muse Spark 1.1: 77.7%

  • MLCR-AA

    Long-context reasoning over medical records

    • GLM 5.2 Thinking: 12.8%Top
    • Muse Spark 1.1: 7.2%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • GLM 5.2 Thinking: 41.1%
    • Muse Spark 1.1: 46.2%Top

  • GPQA Diamond

    Graduate-level science questions

    • GLM 5.2 Thinking: 89.5%
    • Muse Spark 1.1: 89.8%Top

  • IFBench

    Following precise output instructions

    • GLM 5.2 Thinking: 73.3%
    • Muse Spark 1.1: —

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • GLM 5.2 Thinking: 1.0%
    • Muse Spark 1.1: 6.1%Top

  • SciCode

    Scientific computing in Python

    • GLM 5.2 Thinking: 51.2%
    • Muse Spark 1.1: 58.8%Top

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • GLM 5.2 Thinking: 50.8%
    • Muse Spark 1.1: —

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • GLM 5.2 Thinking: 99.1%
    • Muse Spark 1.1: —

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • GLM 5.2 Thinking: 78.3%Top
    • Muse Spark 1.1: 77.7%

Scores from Artificial Analysis, updated Oct 26, 2026. GLM 5.2 Thinking was tested at max reasoning effort; compare scores measured at the same effort.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
108.0 tok/s
Faster than 65% of 254 models
Time to first token
2.0 s
Quicker than 77% of 254 models
Prompt cache hits
59.1%
About $0.22 per 1M input tokens with caching
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 98.2%
  • 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-5.2:thinking",
    "reasoning_effort": "high",
    "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-5.2:thinking",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Z.ai

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