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Access Google DeepMind's Gemini 4 Argon API on PoYo via OpenAI Chat Completions or Gemini Native format. 1M unified context & output, #1 on Vals Index & DeepSWE v1.1 (77.9%), at $1.60 input and $8.00 output per 1M tokens (20% off official).
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1.0
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Pricing details

Transparent pricing with no hidden fees. Pay as you go.

Googlegemini-4-argon
Input
PoYo price
$1.60/1M tokens
320 credits
Official price
$2.00/1M tokens
Official
You save
20%
Googlegemini-4-argon
Output
PoYo price
$8.00/1M tokens
1600 credits
Official price
$10.00/1M tokens
Official
You save
20%
Googlegemini-4-argon
Cache Read
PoYo price
$0.160/1M tokens
32 credits
Official price
$0.200/1M tokens
Official
You save
20%

* Actual fees are based on the final output.

Recommended models
GEMINI 4 ARGON · AVAILABLE NOW ON POYO

Google DeepMind's frontier reasoning model with a 1M unified input-output token window for coding, enterprise workflows, and cyber defense

Gemini 4 Argon API for Long-Horizon Coding, Enterprise Knowledge Work & Cyber Defense

Gemini 4 Argon is Google DeepMind's next-generation sparse Mixture-of-Experts (MoE) frontier reasoning model. Built to sustain deep multi-step reasoning across complex trajectories, Argon expands its output token limit to an industry-leading 1,000,000 unified input-output tokens (up from 64K), enabling whole-repository refactoring, exhaustive financial and legal synthesis, and autonomous vulnerability remediation in a single pass.

Call gemini-4-argon on PoYo via OpenAI-compatible POST /v1/chat/completions or Gemini-native POST /v1beta/models/gemini-4-argon:generateContent (and :streamGenerateContent). PoYo charges $1.60 / 1M input tokens (320 credits), $8.00 / 1M output tokens (1,600 credits), and $0.16 / 1M cached input tokens (32 credits)—20% below Google's $2.00 / $10.00 / $0.20 standard rates.

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Key Capabilities of Gemini 4 Argon API

Backed by Google DeepMind's official evaluations and production deployments across software engineering, enterprise analysis, multimodal spatial reasoning, and defensive cybersecurity.

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

What Can You Build with Gemini 4 Argon API?

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

Published October 2026 benchmarks from the Google DeepMind Gemini 4 Argon Model Card alongside official and PoYo API token pricing.

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).

How to Call Gemini 4 Argon API on PoYo

1. Generate an API Key: Sign in to your PoYo dashboard and create a unified bearer key.

2. Choose Your Endpoint Protocol: Use POST https://api.poyo.ai/v1/chat/completions for drop-in OpenAI SDK compatibility, or POST https://api.poyo.ai/v1beta/models/gemini-4-argon:generateContent (and :streamGenerateContent) for Google Gemini Native multimodal payloads.

3. Specify Model ID: Pass "model": "gemini-4-argon". Both streaming (SSE) and non-streaming responses are supported out of the box.

Frequently Asked Questions about Gemini 4 Argon API

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.

Why Integrate Gemini 4 Argon via PoYo API?

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.