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Z.ai · Efficient

GLM-4 Long

Added Jan 1, 2024

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Use in the app

Extended context model supporting up to 1M tokens

Input
Text
Output
Text
Discovery tier
Efficient

Pricing

USD per 1M tokens
Input
$0.20
Output
$0.20

Reference prices as published by the model provider. API example

Specifications

Context window
1M
Max output
4.1K
Average output (7 days)
252 tokens
Longer than 26% of 468 models

Side by side

GLM-4 Long compared

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

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GLM-4 LongZ.aiThis modelGLM 5.3 Flash TEEZ.aiRemove
ProviderZ.aiZ.ai
Discovery tierEfficientOpen weights
AddedJan 1, 2024Aug 28, 2026
Limits
Context window1M1.05MBest
Max output4.1K131.07KBest
Average output252 tokens845 tokens
Pricing, USD per 1M tokens
Input$0.20$0.15Best
Output$0.20Best$0.50
Cache read—$0.03
Performance
Throughput—345.2 tok/s
Time to first token—4.3 s
Request success (24h)—99.9%
Benchmark indexes
Intelligence Index—41.8
Coding Index—71.5
Capabilities
ReasoningNoYes
ToolsNoYes
VisionNoYes
Structured outputNoYes
Parallel toolsNoYes
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 Long
  • GLM 5.3 Flash TEE

Intelligence Index

  • GLM-4 Long—
  • GLM 5.3 Flash TEE41.8
3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • GLM-4 Long: —
    • GLM 5.3 Flash TEE: 71.5

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • GLM-4 Long: —
    • GLM 5.3 Flash TEE: 60.4%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • GLM-4 Long: —
    • GLM 5.3 Flash TEE: 25.4%

  • Harvey LAB-AAv1.1

    Legal tasks passed in full, with no material hallucination

    • GLM-4 Long: —
    • GLM 5.3 Flash TEE: 0.8%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • GLM-4 Long: —
    • GLM 5.3 Flash TEE: 1,452 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • GLM-4 Long: —
    • GLM 5.3 Flash TEE: 1,642 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • GLM-4 Long: —
    • GLM 5.3 Flash TEE: 15.4%

  • AA-LCRv1.1

    Reasoning across long documents

    • GLM-4 Long: —
    • GLM 5.3 Flash TEE: 80.0%

  • MLCR-AA

    Long-context reasoning over medical records

    • GLM-4 Long: —
    • GLM 5.3 Flash TEE: 11.7%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • GLM-4 Long: —
    • GLM 5.3 Flash TEE: 39.9%

  • GPQA Diamond

    Graduate-level science questions

    • GLM-4 Long: —
    • GLM 5.3 Flash TEE: 91.2%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • GLM-4 Long: —
    • GLM 5.3 Flash TEE: 32.8%

  • SciCode

    Scientific computing in Python

    • GLM-4 Long: —
    • GLM 5.3 Flash TEE: 51.6%

Legacy evaluations

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • GLM-4 Long: —
    • GLM 5.3 Flash TEE: 80.0%

Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis

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": "glm-4-long",
    "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="glm-4-long",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

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