Gemini 4 Argon API

Gemini 4 Argon is a multimodal deep reasoning and general intelligence large model developed by Google DeepMind. It was first released on September 30, 2026, for the Fairwind cybersecurity defense partners trial, and globally officially available on October 13, 2026. As a flagship reasoning model built on a large sparse MoE Transformer architecture, it is designed for high-complexity Agent long-chain planning, advanced mathematics and algorithmic logic, and omni-modal long-range correlation analysis.

On the iCreat platform, Gemini 4 Argon is accessible via the OpenAI-compatible Chat Completions endpoint. It supports multimodal inputs including text, high-resolution images, high-fidelity audio, long videos, and complex professional documents. The context window is 1 million tokens shared across input, thinking, and output. The platform pricing is aligned with Google's official overseas introductory pricing at $2 per million input tokens and $10 per million output tokens.

Model Positioning

Gemini 4 Argon is positioned as an enterprise-grade flagship reasoning model targeting core business and frontier research scenarios. It excels in handling high-complexity Agent long-chain planning, advanced mathematical and algorithmic logic, and omni-modal long-range correlation analysis. It is specifically optimized for coding and software engineering agents, achieving the highest scores in benchmarks like DeepSWE v1.1 and ERQA. It is the ideal choice for developers and researchers requiring ultra-long context, deep reasoning, and state-of-the-art agentic capabilities.

Core Capabilities

Deep Reasoning & Long-Chain Planning

The model supports low, medium, and high thinking modes controllable via the thinking parameter, allowing precise control over reasoning depth and token budget. It can handle complex multi-step planning and logical deduction, making it highly effective for agentic workflows.

Omni-Modal Understanding

Gemini 4 Argon natively understands text, high-resolution images, high-fidelity audio, long videos, and complex professional documents. This allows it to process and correlate information across different modalities seamlessly.

Ultra-Long Context & Output

It features a 1,048,576-token context window shared across input, thinking, and output. With the long decode continuation protocol, it can generate up to 1 million tokens in a single output, far exceeding the 128K limit of comparable flagship models.

Advanced Tool Use

The model supports a wide array of tools including code execution, computer use (preview), function calling, structured output, Google search grounding, URL context, and file search, enabling the construction of highly autonomous and capable AI agents.

Pricing

Note: The above reflects iCreat platform pricing, which is aligned with Google's official overseas introductory pricing in USD (effective until 2026-11-30). Starting 2026-12-01, the official price will adjust to $4 for input, $20 for output, and $0.2 for cache read per million tokens. Output pricing includes thinking tokens. Cache only supports implicit caching. Final billing is subject to the iCreat platform invoice and costUSD.

Application Scenarios

• High-complexity software engineering and autonomous coding agents
• Advanced mathematical proofs and algorithmic logic design
• Long-document analysis and complex professional document processing
• Long-range correlation analysis across text, video, and audio data
• Enterprise-level core business automation and deep research tasks
• Embodied reasoning and complex environment interaction

Model Comparison

Comparison Table 1: Gemini 4 Argon vs Gemini 3.8 Flash

Comparison Table 2: Cross-Vendor Flagship Comparison

Note: Argon's promotional pricing ends on 2026-11-30, after which it adjusts to $4/$20, matching Opus 5.5. Opus 5.5 leads in Terminal-Bench 4.0 and FrontierSWE v2 benchmarks.

Why Choose Gemini 4 Argon?

• Unmatched 1 million token output limit via long decode continuation protocol
• State-of-the-art coding and agent performance (DeepSWE v1.1 77.9%)
• Highest embodied reasoning score (ERQA 87%)
• Comprehensive omni-modal understanding including high-res images, audio, and long video
• Ultra-long 1 million token context window shared across input, thinking, and output
• Advanced tool use including Google search grounding and computer use (preview)
• Lowest average cost per task ($1.99) compared to other frontier flagships during promotional period

Specifications

Architecture

Gemini 4 Argon is built on a large sparse Mixture-of-Experts (MoE) Transformer architecture. It was trained using TPU, JAX, and ML Pathways. The architecture is optimized for deep reasoning and long-context processing, allowing it to efficiently handle a 1 million token window shared across input, thinking, and output. Its sparse MoE design enables high capacity and performance while maintaining computational efficiency during inference.

Notes

• The model parameter must use the iCreat model_code (gemini-4-argon), not the vendor's original model name.
• Promotional pricing ($2/$10/$0.1) is Google's official introductory price valid until 2026-11-30. Starting 2026-12-01, it will adjust to $4/$20/$0.2. Actual settlement is subject to the iCreat platform invoice and costUSD.
• Output pricing includes thinking tokens. The thinking mode has three levels (low/medium/high, default medium) and can be controlled via the thinking parameter.
• The context is a 1 million token window shared across input, thinking, and output. Default synchronous output is 65,536 tokens; long decode supports up to 1 million tokens.
• Context caching only supports implicit caching; explicit caching is not supported.
• Output is text-only; image generation, audio generation, and Live API are not supported.
• Multimodal content items must be passed by type: image_url, input_audio (data+format), and video_url (url).
• Supported features include Google search grounding, code execution, function calling, structured output, computer use (preview), URL context, and file search.

Frequently Asked Questions

How do I call Gemini 4 Argon?​

You can call it using the OpenAI-compatible /v1/chat/completions endpoint. Set the model field to gemini-4-argon, use Bearer authentication, and it supports streaming via the stream parameter.

How is the cost calculated?​

The cost is calculated per million tokens: $2 for input, $10 for output (including thinking tokens), and $0.1 for cache read. This promotional pricing is valid until 2026-11-30; starting December, it will be $4/$20/$0.2. The usage field in the response provides the token statistics.

How large is the context window?​

The context window is approximately 1 million tokens shared across input, thinking, and output. The default output is 64K tokens, and with long decode, it can generate up to 1 million tokens of output.

What input modalities are supported?​

It supports text, images, audio, video, and complex documents. The output is text-only.

What is the difference between Gemini 4 Argon and Gemini 3.8 Flash?​

Gemini 4 Argon is a flagship deep reasoning model with stronger coding and agent benchmarks, and a 1 million token output limit compared to 3.8 Flash's 64K. It is more expensive. Gemini 3.8 Flash is more cost-effective and suitable for daily multimodal tasks.

How does it compare to Claude Opus 5.5 and GPT-6 Astra?​

Gemini 4 Argon leads in DeepSWE and Vals Index, has the longest output limit (1 million tokens), and offers the lowest promotional price. However, it trails Opus 5.5 in Terminal-Bench 4.0 and FrontierSWE v2. Starting in December, its standard pricing will match Opus 5.5.