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Google · Frontier

Gemini 4 Argon

Added Oct 13, 2026

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Use in the app

Google's Gemini 4 Argon is a flagship reasoning model for complex software engineering, mathematical and scientific problem solving, and multi-step agents. It accepts text, images, audio, video, and PDFs, with tool calling and structured outputs. Its 1,048,576-token context is shared across input, thinking, and output; synchronous output is capped at 65,536 tokens.

  • Reasoning
  • Tools
  • Vision
  • Structured output
  • PDF input
  • Audio input
  • Video input
Input
Text, Images, Video, Audio
Output
Text
Reasoning effort
low · medium · high
Discovery tier
Frontier

Pricing

USD per 1M tokens
Input
$2.00
Output
$10.00
Cache read
$0.10
Cache write
$0.19

Reference prices as published by the model provider. API example

Specifications

Context window
1M
Max output
65.5K
Average output (7 days)
1.6K tokens
Longer than 87% of 473 models

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TryMuse WonderClaude Fable 5.1Kimi K3.1 ProAll 3

Public evaluations

Benchmarks

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

Intelligence Index

52.6

Tested at high reasoning effort

Better than 97% of 326 scored models

3.8Catalog models by score55.1

Agentic work

  • AutomationBench-AA

    77.5%

    Workflow automation, penalized for guardrail violations

    Highest of 173 scored models

  • AutomationBench-AA tasks completed

    53.1%

    Workflows finished end to end without a guardrail violation

    Highest of 173 scored models

  • Harvey LAB-AAv1.1

    3.9%

    Legal tasks passed in full, with no material hallucination

    Better than 79% of 29 scored models

  • AA-Briefcasev1.1

    1,486 Elo

    Agentic knowledge work, rated head to head

    Better than 82% of 166 scored models

  • GDPval-AAv2.1

    1,624 Elo

    Economically valuable occupational tasks, rated head to head

    Better than 89% of 187 scored models

Document reasoning

  • GDP.pdf

    21.8%

    Professional reasoning over PDF documents, all-pass rate

    Better than 79% of 171 scored models

  • AA-LCRv1.1

    79.7%

    Reasoning across long documents

    Better than 75% of 309 scored models

  • MLCR-AA

    13.9%

    Long-context reasoning over medical records

    Better than 62% of 121 scored models

Reasoning and knowledge

  • Humanity’s Last Exam

    57.1%

    Expert-level questions across academic fields

    Better than 99% of 323 scored models

Coding

  • Terminal-Benchv4.0

    57.1%

    Practical software engineering in a terminal

    Better than 98% of 171 scored models

  • SciCode

    61.8%

    Scientific computing in Python

    Better than 98% of 174 scored models

Legacy evaluations

  • AA-LCR (unversioned)

    79.7%

    Long-context reasoning, earlier grading

    Better than 75% of 309 scored models

Scores from Artificial Analysis, updated Oct 26, 2026. Gemini 4 Argon was tested at high 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
113.7 tok/s
Faster than 67% of 259 models
Time to first token
10.0 s
Quicker than 9% of 259 models
Prompt cache hits
44.3%
About $1.16 per 1M input tokens with caching
Request success (24h)
98.4%
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 94.1%
  • 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": "google/gemini-4-argon",
    "reasoning_effort": "medium",
    "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="google/gemini-4-argon",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Google

Other Google models, starting with the same family.