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Gemini 4 Argon vs GLM 5.3 Prime: API price, context & features · api14

Google: Gemini 4 Argon vs Z.ai: GLM 5.3 Prime

A side-by-side comparison of Google: Gemini 4 Argon ($7.00 / 1M tokens) and Z.ai: GLM 5.3 Prime ($9.80 / 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	Z.ai: GLM 5.3 Prime
Provider	Google	Z.ai
Input	$7.00 / 1M tokens	$9.80 / 1M tokens
Output	$35.00 / 1M tokens	$30.80 / 1M tokens
Cache read	$0.35 / 1M tokens	$1.96 / 1M tokens
Cache write	$0.66 / 1M tokens	-
Reasoning	$35.00 / 1M tokens	-
Audio input	$7.00 / 1M tokens	-
Image	$0.000007 / image	-
Web search	$49.00 / 1K searches	-
Context window	1,048,576	1,000,000
Max output	943,718	131,072
Input	audio, file, image, text, video	text
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Oct 13, 2026	Sep 23, 2026

Key differences

Google: Gemini 4 Argon is 29% cheaper on input tokens.
Z.ai: GLM 5.3 Prime is 12% cheaper on output tokens.
Only Google: Gemini 4 Argon supports structured JSON output.
A request with 1,000 input and 500 output tokens costs $0.02 with Google: Gemini 4 Argon and $0.03 with Z.ai: GLM 5.3 Prime.

In depth

Google: Gemini 4 Argon is a text model from Google, and Z.ai: GLM 5.3 Prime is a text model from Z.ai. Both are served through api14 as OpenAI-compatible endpoints, so from a client's point of view they are two model ids behind the same interface. Gemini 4 Argon was released by its provider on 2026-10-13, and GLM 5.3 Prime on 2026-09-23. Neither is a specialised model for a single narrow job; both are general purpose text generators that accept a prompt and return text.

The clearest difference is input modality. Gemini 4 Argon accepts audio, file, image, text and video as input, while GLM 5.3 Prime accepts text only. Both return text only. Their context windows are close enough that the difference is not worth planning around: Gemini 4 Argon lists 1048576 tokens and GLM 5.3 Prime lists 1000000, so treat them as roughly the same capacity for long documents. On parameters, Gemini 4 Argon supports structured outputs, which GLM 5.3 Prime does not list, while GLM 5.3 Prime exposes frequency_penalty, presence_penalty, logprobs, top_k and top_logprobs, which Gemini 4 Argon does not. Both support tools, tool_choice, reasoning, reasoning_effort, include_reasoning, response_format, seed, stop, temperature, max_tokens and top_p. On price, Gemini 4 Argon is the cheaper side for input and GLM 5.3 Prime is the cheaper side for output, so the better deal depends on whether your workload is input heavy or output heavy.

For practical selection: if your input is anything other than plain text, such as a screenshot, a PDF, an audio clip or a video, Gemini 4 Argon is the only one of the two that can take it, so the choice is made for you. If you need the response to conform to a schema, Gemini 4 Argon's structured outputs support is the safer route. If you are generating a lot of output tokens relative to input, GLM 5.3 Prime's cheaper output side will usually matter more, and its extra sampling controls (top_k, penalties, logprobs) give you more room to tune generation. If you need token level log probabilities for scoring or classification style work, GLM 5.3 Prime is the one that exposes them. For ordinary text in, text out chat or extraction with a large document, either will do, and the deciding factor is likely to be which side of the price table your token mix leans toward.

Calling either model is the same operation. Point your OpenAI-compatible client at api14's base URL, send your request to the chat completions endpoint with your api14 key, and set the model field to "google/gemini-4-argon" or "z-ai/glm-5.3-prime". Nothing else in the request body needs to change to switch between them, provided you stay within the parameters each model supports. If you rely on structured outputs, keep the model id on the Gemini side; if you rely on logprobs or top_k, keep it on the GLM side. A single string swap is enough to move a working integration from one to the other.

There are cases where the model that looks weaker on paper is still the better pick. If your task is pure text and you want fine grained control over sampling, GLM 5.3 Prime's top_k, frequency_penalty and presence_penalty give you knobs Gemini 4 Argon does not offer, and its logprobs and top_logprobs make it the choice when you need to inspect the distribution rather than just the sampled token. Conversely, if you want one model that can also read images, audio, files and video without a separate pipeline, Gemini 4 Argon covers those inputs and GLM 5.3 Prime cannot, so no amount of parameter tuning closes that gap.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "google/gemini-4-argon" to "z-ai/glm-5.3-prime" and prompts, streaming and tools keep working.

Google: Gemini 4 Argon details & playground →Z.ai: GLM 5.3 Prime 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": "z-ai/glm-5.3-prime",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

Google: Gemini 4 Argon vs Z.ai: GLM 5.3 Prime

Specifications

Key differences

In depth

Switch with one string

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