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Modèle:
Accédez à l'API Gemini 4 Argon de Google DeepMind sur PoYo via OpenAI Chat Completions ou le format natif Gemini. Contexte et sortie unifiés de 1M, n°1 sur Vals Index et DeepSWE v1.1 (77,9 %), à $1.60 en entrée et $8.00 en sortie par 1M de tokens (-20 % sur le tarif officiel).
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Détails des prix

Tarification transparente, sans frais cachés. Payez à l'usage.

Googlegemini-4-argon
Input
Prix PoYo
$1.60/1M tokens
320 crédits
Prix officiel
$2.00/1M tokens
Official
Économie
20%
Googlegemini-4-argon
Output
Prix PoYo
$8.00/1M tokens
1600 crédits
Prix officiel
$10.00/1M tokens
Official
Économie
20%
Googlegemini-4-argon
Cache Read
Prix PoYo
$0.160/1M tokens
32 crédits
Prix officiel
$0.200/1M tokens
Official
Économie
20%

* Les frais réels sont basés sur la sortie finale.

Modèles recommandés
GEMINI 4 ARGON · DISPONIBLE SUR POYO

Le modèle de raisonnement de pointe de Google DeepMind avec fenêtre unifiée d'entrée-sortie de 1M de tokens pour le code, l'entreprise et la cyberdéfense

API Gemini 4 Argon pour l'Ingénierie Logicielle, les Workflows d'Entreprise et la Cyberdéfense

Gemini 4 Argon est le modèle de raisonnement MoE (Mixture-of-Experts) de nouvelle génération de Google DeepMind. Conçu pour le raisonnement approfondi à long horizon, Argon porte sa capacité de sortie à 1 000 000 de tokens unifiés d'entrée-sortie (contre 64K auparavant), permettant la refactorisation de dépôts entiers, la synthèse juridique et financière et la correction autonome de vulnérabilités en une seule passe.

Appelez gemini-4-argon sur PoYo via POST /v1/chat/completions (compatible OpenAI) ou POST /v1beta/models/gemini-4-argon:generateContent (et :streamGenerateContent). PoYo facture $1.60 / 1M tokens d'entrée (320 crédits), $8.00 / 1M tokens de sortie (1 600 crédits) et $0.16 / 1M tokens de lecture cache (32 crédits), soit 20 % de moins que les tarifs officiels Google ($2.00 / $10.00 / $0.20).

Obtenir une clé API PoYo

Fonctionnalités Clés de l'API Gemini 4 Argon

Appuyé par les évaluations officielles de Google DeepMind en génie logiciel, analyse financière et juridique, compréhension multimodale et cybersécurité défensive.

01

State-of-the-Art Long-Horizon Software Engineering (77.9% DeepSWE v1.1)

Gemini 4 Argon sets a new state of the art on DeepSWE v1.1 (77.9%) and Vibe Code Bench (91.9%), outperforming Claude Opus 5.5 (74.2%) and GPT-6 Astra (74.1%). With a 1M token output limit, Argon can untangle multi-million-line legacy codebases, migrate C/C++ libraries to safe Rust (achieving a 2.7x speedup in Google's libgav1 video decoder), and optimize vLLM GPU kernels end-to-end without mid-response truncation.

  • 77.9% on DeepSWE v1.1 and 91.9% on Vibe Code Bench
  • Up to 1,000,000 unified input and output tokens per trajectory
  • Multi-file architecture refactoring and C++ to Rust migrations
Official Google DeepMind DeepSWE v1.1 benchmark chart showing Gemini 4 Argon at 77.9% vs Claude Opus 5.5 and GPT-6 Astra

02

#1 on Economic Knowledge Work & Enterprise Automation

Argon ranks #1 on the Vals Index (68.9%)—weighted across finance, legal, tax, and coding by U.S. GDP contribution—and leads Vals Finance Agent v2 (65.4%), Harvey's Legal Agent Benchmark (19.6% vs 5.4% for Astra), and Zapier's AutomationBench (51.3%). Its GraphWalks 256K–1M long-context recall reaches 84.2% F1, over 17 percentage points ahead of competing frontier models.

  • 68.9% on Vals Index and 65.4% on Vals Finance Agent v2
  • 19.6% on Harvey's Legal Agent Benchmark (3x higher than peers)
  • 84.2% F1 on GraphWalks 256K–1M long-context reasoning
Official Google DeepMind Vals Index benchmark comparison showing Gemini 4 Argon leading at 68.9%

03

Multimodal Video, 3D Spatial & Scientific Synthesis

Natively trained across text, images, audio, video, and PDFs, Gemini 4 Argon achieves 91.7% on LVBench (long video understanding), 88.8% on LABBench 2, and 87% on Embodied Reasoning (ERQA). In official DeepMind demonstrations, Argon synthesizes 50 GB of Airborne LiDAR, GIS vectors, and archival CAD drawings into interactive 3D reconstructions, and drives multi-agent visual critique loops in Blender and Three.js.

  • 91.7% on LVBench long-video comprehension and 71.6% on Chartography
  • 88.8% on LABBench 2 and 76.0% on RiemannBench mathematical reasoning
  • Autonomous 3D scene generation and multimodal scientific synthesis

04

Defensive Cybersecurity & Prompt Injection Resilience

Built for cyber defenders in Google's Fairwind Program and Wiz's Scan for Good initiative, Argon ties for #1 on CWE-bench v1 (68.0%) for autonomous vulnerability remediation and discovers complex multi-language exposures across 20 programming languages. Adversarial training reduces indirect prompt injection success to just 0.7% across 15 adaptive attempts on the Gray Swan IPI benchmark.

  • 68.0% on CWE-bench v1 for automated security patching
  • 0.7% attack success rate on Gray Swan Indirect Prompt Injection (IPI)
  • Autonomous source-code auditing and proof-of-concept validation
Official Google DeepMind CWE-bench v1 cybersecurity vulnerability remediation benchmark chart for Gemini 4 Argon

Que construire avec l'API Gemini 4 Argon ?

01

Full-Repository Refactoring & Language Migration

Leverage the 1M unified input-output token window to refactor monolithic modules, migrate C/C++ codebases to memory-safe Rust, and output complete multi-file patches in one trajectory.

02

Autonomous Enterprise & Operations Agents

Orchestrate end-to-end business workflows across APIs, databases, and document repositories with #1 AutomationBench (51.3%) reliability and transparent chain-of-thought monitoring.

03

Financial Modeling & Legal Due Diligence

Connect evidence across hundreds of SEC filings, contracts, and case precedents with leading accuracy on Vals Finance Agent v2 (65.4%) and Harvey's Legal Agent Benchmark (19.6%).

04

Automated Security Auditing & Patching

Scan multi-language repositories to surface subtle logic flaws, validate exploitability in sandboxed environments, and generate verified CWE remediation patches.

05

Long-Video & Multimodal Document Intelligence

Analyze hours-long video recordings, technical charts, audio streams, and dense PDFs with 91.7% LVBench accuracy and 84.2% recall across 256K–1M token contexts.

06

Scientific Computing & 3D Simulation

Accelerate quantum circuit optimization, molecular conformer search, formal proof verification in Lean, and browser-ready Three.js / WebGL scientific visualizations.

Gemini 4 Argon vs GPT-6 Astra vs Claude Opus 5.5 vs Claude Fable 5.1

Benchmarks publiés en octobre 2026 dans la Model Card officielle de Gemini 4 Argon et comparaison des tarifs API.

Benchmark / MetricGemini 4 ArgonGPT-6 AstraClaude Opus 5.5Claude Fable 5.1
Vals Index (Knowledge Work)68.9% (#1)63.1%67.0%65.8%
AutomationBench (End-to-End Workflows)51.3% (#1)41.4%42.5%31.4%
Vals Finance Agent v265.4% (#1)53.5%58.6%58.9%
Harvey's Legal Agent Benchmark19.6% (#1)5.4%3.8%6.7%
DeepSWE v1.1 (Agentic Coding)77.9% (#1)74.1%74.2%67.4%
Vibe Code Bench91.9% (#1)89.6%90.3%90.3%
Terminal-Bench 4.057.4%58.2%66.4%57.9%
LABBench 2 / RiemannBench (Science & Math)88.8% / 76.0%85.4% / 72.0%73.1% / 69.6%68.6% / 65.6%
GraphWalks 256K–1M (Long Context F1)84.2% (#1)71.8%66.8%65.0%
LVBench (Long Video) / Chartography91.7% / 71.6%87.5% / 71.0%83.7% / 66.3%79.7% / 46.2%
CWE-bench v1 (Cybersecurity)68.0% (Tied #1)68.0%67.0%58.0%
Context Window / Max Output1M / 1M unified1.05M / 128K1M / 128K1M / 128K
Official Standard Price (Input / Output per 1M)$2.00 / $10.00$10.00 / $50.00$4.00 / $20.00$10.00 / $50.00
PoYo API Price (Input / Output / Cache Read per 1M)$1.60 / $8.00 / $0.16$8.00 / $40.00 / $0.80$3.20 / $16.00 / $0.16$8.00 / $40.00 / $0.20

Source: Google DeepMind Gemini 4 Argon Model Card (October 2026). PoYo pricing reflects a 20% discount off official standard rates (200 credits = $1.00 USD).

Comment appeler l'API Gemini 4 Argon sur PoYo

1. Créer une clé API : Connectez-vous au tableau de bord PoYo et générez votre clé.

2. Choisir le protocole : Utilisez POST https://api.poyo.ai/v1/chat/completions pour les SDK OpenAI ou POST https://api.poyo.ai/v1beta/models/gemini-4-argon:generateContent (et :streamGenerateContent) pour le format natif Gemini.

3. Définir l'ID du modèle : Passez "model": "gemini-4-argon" en mode standard ou streaming.

Questions Fréquentes sur l'API Gemini 4 Argon

What is Gemini 4 Argon?

Gemini 4 Argon is Google DeepMind's flagship sparse Mixture-of-Experts (MoE) frontier reasoning model announced in late September 2026 and released globally in October 2026. It is engineered for long-horizon software engineering, high-stakes enterprise knowledge work (finance, legal, tax), scientific discovery, and defensive cybersecurity.

How much does the Gemini 4 Argon API cost on PoYo vs Google Official?

Google's official standard introductory pricing is $2.00 per 1M input tokens, $10.00 per 1M output tokens, and $0.20 per 1M cached input tokens. On PoYo, you get a flat 20% discount: $1.60 / 1M input tokens (320 credits), $8.00 / 1M output tokens (1,600 credits), and $0.16 / 1M cache read tokens (32 credits), where 200 credits = $1.00 USD.

Which API endpoints does PoYo support for Gemini 4 Argon?

PoYo supports both the OpenAI-compatible Chat Completions endpoint (POST https://api.poyo.ai/v1/chat/completions) and the Google Gemini Native endpoints (POST https://api.poyo.ai/v1beta/models/gemini-4-argon:generateContent and :streamGenerateContent) using the model ID gemini-4-argon.

What is the context window and maximum output token limit of Gemini 4 Argon?

Gemini 4 Argon features a 1,000,000-token unified input-output context window and expands maximum output capacity to 1,000,000 tokens (up from 64K in previous Gemini models). This allows the model to sustain 100K+ token reasoning chains and generate full-repository code or exhaustive reports in a single trajectory.

How does Gemini 4 Argon compare to Claude Opus 5.5 and GPT-6 Astra?

On Google DeepMind's published evaluations, Gemini 4 Argon leads on DeepSWE v1.1 (77.9% vs 74.2% for Opus 5.5 and 74.1% for Astra), Vals Index (68.9% vs 67.0% and 63.1%), AutomationBench (51.3%), GraphWalks 256K–1M (84.2%), and LVBench (91.7%), while tying Astra on CWE-bench v1 (68.0%). Claude Opus 5.5 leads on Terminal-Bench 4.0 (66.4% vs 57.4%), and GPT-6 Astra leads on FrontierSWE v2 (65.5%) and Terminal-Bench Science 0.1 (68.1%).

What input modalities and knowledge cutoff does Gemini 4 Argon have?

According to the Gemini 4 Argon Model Card, the model natively accepts text, images, audio, video, and PDF documents as input and produces text output. Its knowledge cutoff is March 2026.

Pourquoi choisir PoYo pour l'API Gemini 4 Argon ?

01

20% Lower Token Costs

Pay $1.60 input, $8.00 output, and $0.16 cache read per 1M tokens—saving 20% compared to official rates with flexible pay-as-you-go credits.

02

Dual OpenAI & Gemini Native Formats

Switch seamlessly between /v1/chat/completions for existing OpenAI SDKs and /v1beta/models/gemini-4-argon:generateContent for native Gemini features.

03

Benchmark Side-by-Side with Opus & Astra

Use one PoYo API key to route workloads across Gemini 4 Argon, Claude Opus 5.5, Claude Sonnet 5.5, GPT-6 Astra, and GPT-6.1 Sol.

04

Instant Global Access, No Tier Gating

Start calling gemini-4-argon immediately in production or testing in the web Playground without enterprise waitlists or regional quota bottlenecks.