Gemini 4 Argon | Google LLM API Pricing & Performance

Name: Gemini 4 Argon API
Brand: google
Price: 4 USD
Availability: InStock

google/gemini-4-argon

Data di rilascio: 2026-10-13

1,048,576 context · $4.00/M input tokens · $20.00/M output tokens

Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across...

Prezzi

Pay-per-use

Nessun costo iniziale, paga solo per ciò che usi

Input$4.00 / M Tokens

Output$20.00 / M Tokens

Cache Read$0.20 / M Tokens

Cache Write$0.38 / M Tokens

Prova il modello

google/gemini-4-argon

Online

Ciao! Sono un assistente IA utile. Come posso aiutarti?

Pronto a usare questo modello in un coding agent locale?Setup agent

Utilizzo API

Usa i seguenti esempi di codice per integrare la nostra API:

import OpenAI from 'openai';

if (!process.env.WAVESPEED_API_KEY) throw new Error('Set WAVESPEED_API_KEY');
const client = new OpenAI({
  apiKey: process.env.WAVESPEED_API_KEY,
  baseURL: 'https://llm.wavespeed.ai/v1',
  timeout: 120_000,
  maxRetries: 0,
});

try {
  const response = await client.chat.completions.create({
    model: 'google/gemini-4-argon',
    messages: [{ role: 'user', content: 'Hello!' }],
  });
  const body = response as typeof response & { error?: { message?: string } };
  if (body.error) throw new Error(body.error.message || 'LLM generation failed');
  console.log(response.choices[0]?.message?.content ?? '');
} catch (error) {
  console.error('LLM request failed:', error);
  process.exitCode = 1;
}

import OpenAI from 'openai';

if (!process.env.WAVESPEED_API_KEY) throw new Error('Set WAVESPEED_API_KEY');
const client = new OpenAI({
  apiKey: process.env.WAVESPEED_API_KEY,
  baseURL: 'https://llm.wavespeed.ai/v1',
  timeout: 120_000,
  maxRetries: 0,
});

try {
  const response = await client.chat.completions.create({
    model: 'google/gemini-4-argon',
    messages: [{ role: 'user', content: 'Hello!' }],
  });
  const body = response as typeof response & { error?: { message?: string } };
  if (body.error) throw new Error(body.error.message || 'LLM generation failed');
  console.log(response.choices[0]?.message?.content ?? '');
} catch (error) {
  console.error('LLM request failed:', error);
  process.exitCode = 1;
}

Introduzione al modello

Google: Gemini 4 Argon

This model is available through the WaveSpeed AI OpenAI-compatible API for chat completions and compatible application workflows.

Why It Looks Great

text+image+file+audio+video->text architecture for Audio, file, Image, Text, Video to Text workloads
1048576 context window for long prompts, document analysis, and multi-turn workflows
Vision input support for image understanding and multimodal tasks
Function calling and tool-use support for agentic application workflows
Structured output support for JSON responses and schema-constrained generation
Reasoning controls available through supported parameters

Key Features

Context Window: 1048576 tokens
Max Input: 104858 tokens
Max Output: 943718 tokens
Vision: Supported
Function Calling: Supported
Structured Outputs: Supported
Image Generation: Not listed
Audio Input: Supported
Supported Parameters: include_reasoning, max_tokens, reasoning, reasoning_effort, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_p

Specifications

Specification	Value
Provider	google
Model Type	Chat Completions model
Architecture	text+image+file+audio+video->text
Context Window	1048576 tokens
Max Input	104858 tokens
Max Output	943718 tokens
Input	Audio, file, Image, Text, Video
Output	Text
Vision	Supported
Function Calling	Supported
Structured Outputs	Supported
Release Date	October 14, 2026

How to Use

Write your prompt - describe the task, provide context, and specify the desired output format.
Submit - the model processes your request and returns the response.

API Integration

Base URL: https://llm.wavespeed.ai/v1 API Endpoint: chat/completions Model ID: google/gemini-4-argon

API Usage

Python SDK

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://llm.wavespeed.ai/v1"
)

response = client.chat.completions.create(
    model="google/gemini-4-argon",
    messages=[{"role": "user", "content": "Hello!"}]
)

print(response.choices[0].message.content)

cURL

curl https://llm.wavespeed.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "google/gemini-4-argon",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Notes

Model: google/gemini-4-argon
Provider: google

Info

Providergoogle

Tipollm

Funzionalità supportate

Input

TestoImmagineAudio

Output

Testo

Contesto1,048,576

Output massimo32,768

Vision✓ Supportato

Function Calling✓ Supportato

Guida all'accesso API

Base URLhttps://llm.wavespeed.ai/v1

API Endpointchat/completions

ID modellogoogle/gemini-4-argon

Domande frequenti su Gemini 4 Argon

Quanto costa Gemini 4 Argon via API?+

Prezzi su WaveSpeedAI: $4.00 per milione di token in input e $20.00 per milione di token in output. Prompt caching e batch processing sono fatturati separatamente e riducono il costo effettivo su carichi lunghi e ripetitivi.

Qual è la context window di Gemini 4 Argon?+

Gemini 4 Argon supporta fino a 1049K token di contesto e fino a 33K token di output per richiesta.

Gemini 4 Argon è compatibile con OpenAI?+

WaveSpeedAI espone Gemini 4 Argon su https://llm.wavespeed.ai/v1 tramite l’interfaccia Chat Completions compatibile con OpenAI. Per la maggior parte dei client OpenAI SDK basta cambiare base URL e API key; i campi opzionali dipendono dal modello.

Come si inizia con Gemini 4 Argon?+

Accedi a WaveSpeedAI, crea una API key in Access Keys e invia una richiesta a https://llm.wavespeed.ai/v1/chat/completions con il model id mostrato sopra. Consulta il catalogo attuale per disponibilità, funzionalità e prezzi.