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Gemini 4 Argon vs GPT-6.1 Sol: API price, context & features · api14

Google: Gemini 4 Argon vs OpenAI: GPT-6.1 Sol

A side-by-side comparison of Google: Gemini 4 Argon ($7.00 / 1M tokens) and OpenAI: GPT-6.1 Sol ($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-6.1 Sol
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	$0.35 / 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 29, 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-6.1 Sol.

In depth

Google: Gemini 4 Argon is a text model from Google, released on 13 October 2026. OpenAI: GPT-6.1 Sol is a text model from OpenAI, released on 29 September 2026. Both are served through api14 as text generation models with a single text output, so whichever you call, you get text back. The choice between them comes down to what you feed in and how you want to steer the generation.

The clearest difference is input modality. Gemini 4 Argon accepts audio, file, image, text and video, so it is the one to reach for when your request includes media beyond plain text and still images. GPT-6.1 Sol accepts file, image and text, which covers documents and pictures but not audio or video. On context, the two are close: Gemini 4 Argon lists 1,048,576 tokens and GPT-6.1 Sol lists 1,050,000, a difference small enough that it should not drive your decision. Neither model is flagged as uniquely supporting tools, structured outputs or reasoning, so treat those as available on both rather than as a tiebreaker. The parameter lists do differ in naming: Gemini 4 Argon exposes max_tokens, stop, temperature and top_p, while GPT-6.1 Sol exposes max_completion_tokens, max_tokens and verbosity, and omits stop, temperature and top_p. If your code depends on temperature, top_p or stop sequences, Gemini 4 Argon is the more direct fit; if you want a verbosity control, GPT-6.1 Sol has one.

For practical work, pick Gemini 4 Argon when the input is a video, an audio clip, or a mix of media and text, and when you want to tune sampling with temperature, top_p or stop. Pick GPT-6.1 Sol when your input is text, images or files, and when you would rather control output length through verbosity or max_completion_tokens. Both handle long documents comfortably given the context sizes, so long-context summarisation or retrieval-style prompting is viable on either. Cost is not a differentiator here: the qualitative comparison puts input and output pricing at about the same level for the two models, so let capability and parameter fit decide.

Calling either model through api14 works the same way, because the API is OpenAI-compatible. You point your client at the api14 base URL, send a chat completions request, and set the model field. To use Gemini 4 Argon, set the model id to google/gemini-4-argon. To use GPT-6.1 Sol, set it to openai/gpt-6.1-sol. Everything else in the request body stays as your existing OpenAI-style code, so switching is a one-line change to the model id. If you keep both ids in configuration, you can route individual requests to whichever model suits the input without rewriting your integration.

There are cases where GPT-6.1 Sol is still the better pick even though Gemini 4 Argon takes more input types. If your pipeline is already built around OpenAI-style parameters such as max_completion_tokens and verbosity, or if you want to keep behaviour consistent with other OpenAI models in your stack, staying on openai/gpt-6.1-sol avoids parameter translation. Its context is marginally larger and it covers the text, image and file inputs most applications actually send, so for text-and-image workloads with no audio or video, it is a sound default.

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-6.1-sol" and prompts, streaming and tools keep working.

Google: Gemini 4 Argon details & playground →OpenAI: GPT-6.1 Sol 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-6.1-sol",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

Google: Gemini 4 Argon vs OpenAI: GPT-6.1 Sol

Specifications

Key differences

In depth

Switch with one string

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