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GLM 4.7 Flash Thinking

Added Jan 19, 2026Open weights

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Use in the app

GLM-4.7-Flash with extended thinking capabilities for complex reasoning. Lightweight 30B model optimized for coding and agentic tasks.

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

Pricing

USD per 1M tokens
Input
$0.07
Output
$0.40
Cache read
$0.01

Reference prices as published by the model provider. API example

Specifications

Context window
200K
Max output
128K
Average output (7 days)
2.3K tokens
Longer than 93% of 468 models

Side by side

GLM 4.7 Flash Thinking compared

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

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GLM 4.7 Flash ThinkingZ.aiThis modelApex Flash 1 AbliteratedZ.aiRemove
ProviderZ.aiZ.ai
Discovery tierOpen weightsOpen weights
AddedJan 19, 2026Oct 5, 2026
Limits
Context window200K1.05MBest
Max output128KBest32.77K
Average output2.3K tokens629 tokens
Pricing, USD per 1M tokens
Input$0.07Best$0.15
Output$0.40Best$0.50
Cache read$0.01Best$0.075
Performance
Throughput43.1 tok/sBest13.2 tok/s
Time to first token6.6 s5.2 sBest
Request success (24h)100.0%Best92.7%
Benchmark indexes
Intelligence Index14.9—
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 4.7 Flash Thinking
  • Apex Flash 1 Abliterated

Intelligence Index

  • GLM 4.7 Flash Thinking14.9
  • Apex Flash 1 Abliterated—

GLM 4.7 Flash Thinking: Better than 37% of 326 scored models

3.8Catalog models by score55.1

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • GLM 4.7 Flash Thinking: 41.7%
    • Apex Flash 1 Abliterated: —

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • GLM 4.7 Flash Thinking: 7.6%
    • Apex Flash 1 Abliterated: —

  • GPQA Diamond

    Graduate-level science questions

    • GLM 4.7 Flash Thinking: 58.1%
    • Apex Flash 1 Abliterated: —

  • IFBench

    Following precise output instructions

    • GLM 4.7 Flash Thinking: 60.8%
    • Apex Flash 1 Abliterated: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • GLM 4.7 Flash Thinking: 22.0%
    • Apex Flash 1 Abliterated: —

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • GLM 4.7 Flash Thinking: 98.8%
    • Apex Flash 1 Abliterated: —

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • GLM 4.7 Flash Thinking: 41.7%
    • Apex Flash 1 Abliterated: —

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
43.1 tok/s
Faster than 31% of 254 models
Time to first token
6.6 s
Quicker than 25% of 254 models
Prompt cache hits
0.0%
About $0.07 per 1M input tokens with caching
Request success (24h)
100.0%
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 100.0%
  • 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-flash:thinking",
    "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-flash:thinking",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Z.ai

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