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Gemini 4 Argon vs GPT-6 Sol Pro: API price, context & features · api14

Google: Gemini 4 Argon vs OpenAI: GPT-6 Sol Pro

A side-by-side comparison of Google: Gemini 4 Argon ($7.00 / 1M tokens) and OpenAI: GPT-6 Sol Pro ($7.00 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.

Specifications

	Google: Gemini 4 Argon	OpenAI: GPT-6 Sol Pro
Provider	Google	OpenAI
Input	$7.00 / 1M tokens	$7.00 / 1M tokens
Output	$35.00 / 1M tokens	$35.00 / 1M tokens
Cache read	$0.35 / 1M tokens	$0.70 / 1M tokens
Cache write	$0.66 / 1M tokens	$8.75 / 1M tokens
Reasoning	$35.00 / 1M tokens	-
Audio input	$7.00 / 1M tokens	-
Image	$0.000007 / image	-
Web search	$49.00 / 1K searches	$35.00 / 1K searches
Context window	1,048,576	1,050,000
Max output	943,718	128,000
Input	audio, file, image, text, video	file, image, text
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Oct 13, 2026	Sep 22, 2026

Key differences

Both have about the same input price.
A request with 1,000 input and 500 output tokens costs $0.02 with Google: Gemini 4 Argon and $0.02 with OpenAI: GPT-6 Sol Pro.

In depth

Google: Gemini 4 Argon is a text model from Google, released on 13 October 2026. OpenAI: GPT-6 Sol Pro is a text model from OpenAI, released on 22 September 2026. Both are served through api14 under a single OpenAI-compatible interface, so the choice between them is mostly about which input types and parameters your application needs rather than about wiring up a different client.

The clearest difference is in what each model accepts as input. Gemini 4 Argon takes audio, file, image, text and video, while GPT-6 Sol Pro takes file, image and text. If your pipeline feeds in recorded calls, screen captures or video clips, Gemini 4 Argon is the one that can read them directly. Both output text only. Their context windows are close enough that the difference is not worth planning around: Gemini 4 Argon lists 1048576 tokens and GPT-6 Sol Pro lists 1050000 tokens, which is effectively the same working room for long documents. On price the two are about the same for both input and output, so cost is not a deciding factor here. The parameter lists also overlap heavily, with tools, structured outputs, reasoning and reasoning effort available on both sides.

For day to day work, pick based on the shape of your input. If you are building something that has to reason over a mix of media, such as a support tool that ingests a screenshot plus a written ticket, or a pipeline that summarises recorded audio alongside a transcript, Gemini 4 Argon covers those modalities without a separate transcription or vision step. If your inputs are text, images and files, GPT-6 Sol Pro is a straightforward fit and exposes a couple of extra knobs, notably verbosity alongside max completion tokens, which can be useful when you want to steer how long the answers run. Both support tool calling and structured outputs, so agent style workflows and JSON shaped responses are available either way.

Calling either model through api14 means changing one string. Point your existing OpenAI client at the gateway, keep your messages, tools and response format as they are, and set the model field to google/gemini-4-argon or openai/gpt-6-sol-pro. Everything else in the request body stays the same, which makes it cheap to run the same prompt against both and compare the output on your own data rather than guessing from a spec sheet.

There is one case where GPT-6 Sol Pro is the better pick even though Gemini 4 Argon handles more input types: when you want the verbosity control, since that parameter is listed for GPT-6 Sol Pro and not for Gemini 4 Argon. Otherwise, if your inputs are text, images and files and you do not need that knob, either model will do the job at about the same price.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "google/gemini-4-argon" to "openai/gpt-6-sol-pro" and prompts, streaming and tools keep working.

Google: Gemini 4 Argon details & playground →OpenAI: GPT-6 Sol Pro details & playground →

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api14.com/api/v1",
  apiKey: process.env.API14_API_KEY,
});

const result = await client.chat.completions.create({
  "model": "openai/gpt-6-sol-pro",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

Google: Gemini 4 Argon vs OpenAI: GPT-6 Sol Pro

Specifications

Key differences

In depth

Switch with one string

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