Google DeepMind's frontier reasoning model — a sparse MoE transformer with a unified 1M-token context, native vision/audio/video input, and tunable thinking levels.
curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gemini-4-argon",
"messages": [{ "role": "user", "content": "Explain quantum tunneling simply." }]
}'Overview
Gemini 4 Argon is Google DeepMind's frontier reasoning model, announced September 30, 2026. It is a sparse mixture-of-experts transformer with native multimodal input (text, image, audio, video, PDF), a unified 1M-token context window, tunable thinking levels, and tool use — built for complex coding, agents, and knowledge work.
On Venice, Gemini 4 Argon runs under the anonymized privacy tier: your prompts are not stored, profiled, or used for training, and there is no personal history tied to your usage. Note that this tier is anonymized rather than end-to-end encrypted or TEE-isolated — if you need cryptographically enforced sovereignty, an open-weights model on Venice is the stronger fit. You also get it permissionless, per-token, with no Google account or subscription.
Agent quickstart
The API is OpenAI-compatible: change the base URL and the model id and existing client code works unchanged.
curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gemini-4-argon",
"stream": true,
"messages": [{ "role": "user", "content": "Draft the release note." }]
}'curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gemini-4-argon",
"messages": [{ "role": "user", "content": "Find the rate limits." }],
"tools": [{
"type": "function",
"function": {
"name": "search_docs",
"description": "Search the API documentation.",
"parameters": {
"type": "object",
"properties": { "query": { "type": "string" } },
"required": ["query"]
}
}
}],
"tool_choice": "auto"
}'import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["VENICE_API_KEY"],
base_url="https://api.venice.ai/api/v1",
)
resp = client.chat.completions.create(
model="gemini-4-argon",
messages=[{"role": "user", "content": "Build without permission."}],
)
print(resp.choices[0].message.content)Specifications
Assessment
Use cases
Prompting
Set the thinking level explicitly: use low for quick drafts, high for hard math, code, or legal reasoning — medium is the default.
Feed long PDFs, images, or video directly as context instead of summarizing them yourself; the 1M-token window absorbs them.
Ask for a JSON schema in the prompt and request structured output when feeding results into another system.
Reuse a stable prefix (system prompt + reference docs) to hit cached-input pricing at $0.13 per 1M tokens.
For agentic tasks, define your functions up front and let the model call them rather than describing tools in prose.
Split work across turns when you need more than 64K output tokens — Venice caps single responses there.
Alternatives
| Model | Best for | Context | Open weights | Price (Venice) |
|---|---|---|---|---|
| Gemini 4 Argon | Frontier reasoning & agentic coding | 1M tokens | No | $2.50 in · $12.50 out / 1M |
| Gemini 3.8 Flash | Fast everyday Gemini tasks | 1M tokens | No | $0.94 in · $4.69 out / 1M |
| Kimi K3 | Open-weight frontier alternative | 1M tokens | Yes | $3.75 in · $18.75 out / 1M |
| GLM 5.3 Flash | Budget reasoning at scale | 1M tokens | Yes | $0.15 in · $0.50 out / 1M |
| Grok 4.7 | Reasoning with a smaller window | 500K tokens | No | $2.27 in · $6.80 out / 1M |
Gemini 4 Argon is the right pick when a task genuinely needs frontier reasoning — hard agentic coding, legal or financial analysis, or multimodal long-document work — and the budget matches. For high-volume or cost-sensitive pipelines, GLM 5.3 Flash or Gemini 3.8 Flash cover the same 1M context at a fraction of the price.
Pricing
Billed per token on Venice: $2.50 per 1M input tokens and $12.50 per 1M output tokens.
New Venice accounts include a free daily allowance and 500 welcome credits — no credit card required.
Getting a key
Create a key in API settings. Nothing else is required to start.
Export it as VENICE_API_KEY so the snippets above run unedited.
Point an existing OpenAI client at https://api.venice.ai/api/v1. The scheme is part of the value: an OpenAI client given a bare host does not resolve it.
Pass gemini-4-argon as the model and send the request.
FAQ
Gemini 4 Argon is Google DeepMind's frontier reasoning model, announced on September 30, 2026. It is a sparse mixture-of-experts transformer with native multimodal input (text, images, audio, video, and PDFs), a unified 1M-token context window, tunable thinking levels, and support for function calling, structured output, and web search.
Venice bills Gemini 4 Argon per token: $2.50 per 1M input tokens and $12.50 per 1M output tokens, with cached input at $0.13 per 1M tokens. There is no subscription required — you pay only for what you use.
Neither. Gemini 4 Argon is a proprietary Google DeepMind model with closed weights, so it cannot be self-hosted or fine-tuned, and it is billed per token on Venice. If you need open weights with a similar 1M-token context, Kimi K3 or GLM 5.3 Flash on Venice are the closest alternatives.
Yes. Gemini 4 Argon supports function calling, structured JSON-schema output, and web search grounding, making it well suited to autonomous multi-step agents and data-extraction pipelines. All three capabilities are available through the Venice API.
Yes. Gemini 4 Argon is natively multimodal on input: it accepts text, images, audio, video, and PDF documents within its unified 1M-token context window. Output is text only — it does not generate images or audio.
Venice runs Gemini 4 Argon under its anonymized privacy tier: prompts are not stored, profiled, or used for training, and usage is not tied to a personal history. This tier is anonymized rather than end-to-end encrypted, so for cryptographically guaranteed privacy an open-weights model is the stronger choice.
Both offer a 1M-token context. Gemini 4 Argon is the stronger closed frontier model for the hardest reasoning and agentic tasks; Kimi K3 offers open weights you can self-host and audit, at a higher output price. Choose Argon for peak capability, Kimi K3 when weight sovereignty matters.
A unified context window of 1,000K tokens (1,048,576) shared across input and output, with up to 64K output tokens per response on Venice. Google's API additionally offers a long-decode mode up to 1M output tokens, which is not exposed on Venice.
Google DeepMind announced Gemini 4 Argon on September 30, 2026, initially rolling it out to trusted cyber defenders through its Fairwind Program before broader availability. It has been live on Venice since October 13, 2026.
One key, free to start, no credit card.