Gemini 4 Argon API

What is Gemini 4 Argon?

Gemini 4 Argon is Google's frontier model and the first model in the Gemini 4 family, announced September 30, 2026. It is a new sparse mixture-of-experts model, not a fine-tune of Gemini 3, built for deep work in coding, finance, legal, cybersecurity, and science. It reads text, images, video, audio, and PDFs inside a 1M-token window shared by input and output, and its knowledge cutoff is March 2026. It runs on UnificAlly as google/gemini-4-argon, with three reasoning effort levels (low, medium, high).

Gemini 4 Argon key features

What Gemini 4 Argon is best for

• Hard coding agents

  77.9% DeepSWE v1.1 and 68.2% on Vals Code Migration for long refactors and language ports.

• Finance research

  #1 on Vals Finance Agent v2 at 65.4% for multi-step financial analysis.

• Legal work

  19.6% on Harvey's Legal Agent Benchmark and 88.3% on LegalBench.

• Long video and charts

  91.7% LVBench and 71.6% Chartography for recordings and chart-heavy reports.

• Science and math

  88.8% LABBench 2 and 76.0% RiemannBench, the top scores in Google's table.

• Security review

  68.0% on CWE-bench v1 vulnerability remediation, tied for first.

Gemini 4 Argon use cases

Run coding agents that own a whole migration: C or C++ to Rust ports, framework upgrades, and repo-wide refactors where DeepSWE v1.1 and Code Migration scores mean fewer stalled runs. Build finance and legal pipelines that read filings, reconcile statements, and draft memos. Point it at hour-long recordings, scanned PDFs, and chart decks in one request. Use it for security review that finds a vulnerability, explains it, and writes the patch.

Gemini 4 Argon limitations

It is slow next to the Flash line. Long reasoning at high can run for a minute or more, so it is a poor fit for chat that needs instant replies. Thinking tokens bill as output, and Artificial Analysis measured it as more verbose than the median model.

Some agent boards still go to rivals. Terminal-Bench 4.0 lands at 57.4% against 66.4% for Claude Opus 5.5, FrontierSWE v2 at 55.0% against 65.5% for GPT-6 Astra, and OSWorld-2.0 at 69.2% against 72.6% for GPT-6 Astra.

Temperature, top_p, and top_k have no effect, and minimal effort is not supported. Google's 1M-token long-decode output mode is not available through chat completions, so one response tops out at 64K tokens.

Gemini 4 Argon vs Gemini 3.8 Flash

Argon wins every hard board. DeepSWE v1.1 goes from 73.7% to 77.9%, Terminal-Bench 4.0 from 19.1% to 57.4%, OSWorld-2.0 from 59.0% to 69.2%, and LVBench from 87.8% to 91.7%. Gemini 3.8 Flash keeps the speed lead and the max effort level. Route hard, long, high-value steps to Argon and keep 3.8 Flash for high-volume agent loops.

When to use Gemini 4 Argon

Use Gemini 4 Argon when the answer has to be right and the task is long: multi-hour coding, finance and legal analysis, long video, and science work. Pick Claude Opus 5.5 for terminal-heavy agents and GPT-6 Astra for desktop automation, where they still lead. Drop to Gemini 3.8 Flash when speed matters more than depth.

Gemini 4 Argon API examples

Call Gemini 4 Argon from any language by POSTing to /v1/chat/completions, the OpenAI-compatible endpoint shared by every language model in the UnificAlly API. Full parameter docs live at docs.unifically.com/models/llm/google/gemini-4-argon.

curl -X POST https://api.unifically.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "google/gemini-4-argon",
    "reasoning_effort": "high",
    "messages": [
      { "role": "user", "content": "Review this module for security bugs, rank them by severity, and write a patch for the worst one." }
    ]
  }'

The response comes back synchronously with the completion. Set "stream": true to receive tokens as they generate, and lower reasoning_effort to low for quick steps.