Google
Gemini 4 Argonanteprima
Google deep reasoning frontier research model featuring native multimodal chain-of-thought, sub-second video analysis, and 2M token active context.
Intelligenza
93.6 #6
AAQI 94.0
Velocità Uscita
88 t/s
ITL 11.4 ms/tok
Prezzo combinato (3:1)
$5 /1M
Input $2.5/M · Output $10/M
Contesto
2M
≈ 5,000 pagine A4 · output max 65,536
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
Gemini 4 Argon
Claude Mythos Preview (#1)
Average (68)
Gli assi radar sono approssimati dai punteggi di categoria dove mancano misurazioni dirette — vedi Metodologia.
Punti di forza e compromessi
Dove vince
- Largest context window in production — 2M tokens
- All four modalities native in a single architecture
- Frontier coding, financial research and legal drafting
- Purpose-built for autonomous vulnerability research
A cosa fare attenzione
- Staged rollout — full API access still ramping
- Deep Think adds latency and token spend
- Proprietary; no open weights
Casi d'uso killer
Cyber defense triage
Autonomous vuln discovery and remediation — the Fairwind program's headline workload.
2M-token corpus analysis
Entire legal archives, monorepos or video libraries in one context.
Multimodal research
Cross-referencing text, audio calls and video feeds natively.
Quickstart per sviluppatori e API
Imposta prima
GEMINI_API_KEY — ottieni una chiave APIcurl "https://generativelanguage.googleapis.com/v1beta/models/gemini-4-argon:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H "content-type: application/json" \
-d '{
"contents": [{"parts": [{"text": "Refactor this function for O(n) time and explain the change."}]}]
}'from google import genai
client = genai.Client() # reads GEMINI_API_KEY
resp = client.models.generate_content(
model="gemini-4-argon",
contents="Refactor this function for O(n) time and explain the change.",
)
print(resp.text)const res = await fetch(
`https://generativelanguage.googleapis.com/v1beta/models/gemini-4-argon:generateContent`,
{
method: "POST",
headers: {
"x-goog-api-key": process.env.GEMINI_API_KEY,
"content-type": "application/json",
},
body: JSON.stringify({ contents: [{ parts: [{ text: "Refactor this function for O(n) time and explain the change." }] }] }),
}
);
console.log((await res.json()).candidates[0].content.parts[0].text);Risorse e link ufficiali
Deployment API
Confronta host e latenza
Throughput, TTFT e prezzi cache su Groq, Cerebras, Together, DeepInfra, Fireworks, Bedrock e Azure.
GroqCerebrasTogether AIDeepInfraFireworks AIAWS BedrockAzure AI
Punteggi per Fonte
Punteggi per Categoria
Risultati dei Benchmark
Scontri diretti live
Specifiche live e architettura
Sotto il cofano
Gemini 4 Argon — native multimodal transformer
- Native multimodal chain-of-thought — reasons over text, image, audio and video in one pass
- 2M-token context — the largest production window tracked
- Deep Think mode trades latency for accuracy on hard problems
- First Gemini 4-generation model; rolled out to trusted cyber defenders via the Fairwind program before GA
Data di Rilascio
5 ott 2026
Architettura
Gemini 4 Argon — native multimodal transformer
Parametri
Not disclosed
Licenza
Proprietary
Finestra di Contesto
2,000,000 tokens
Output max
65,536 tokens
Modalità
text + image + audio + video
Prezzi cache
lettura $0.25 · scrittura $2.5 /1M
Ragionamento
Livelli di sforzo
Prezzo Ingresso
$2.5/M
Prezzo Uscita
$10/M
Velocità Uscita
88 t/s
Tempo al Primo Token
—
Dati aggiornati
10 ott 2026
Stima bolletta mensile
Spesa mensile stimata
$0.00
Calcolo risparmio vs GPT-4o…
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Loading badge markdown...BibTeX
@misc{llmpodium2026gemini4argon,
title = {Benchmark Analysis and Intelligence Evaluation of Gemini 4 Argon},
author = {LLMPodium Research},
year = {2026},
url = {https://llmpodium.com/models/gemini-4-argon}
}Incorpora il badge del punteggio
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