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Perceptron Mk1 vs Qwen2.5 VL 72B Instruct: API price, context & features · api14

Perceptron: Perceptron Mk1 vs Qwen: Qwen2.5 VL 72B Instruct

A side-by-side comparison of Perceptron: Perceptron Mk1 ($0.52 / 1M tokens) and Qwen: Qwen2.5 VL 72B Instruct ($2.80 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.

Specifications

	Perceptron: Perceptron Mk1	Qwen: Qwen2.5 VL 72B Instruct
Provider	Perceptron	Qwen
Input	$0.52 / 1M tokens	$2.80 / 1M tokens
Output	$5.25 / 1M tokens	$3.50 / 1M tokens
Cache read	-	$1.40 / 1M tokens
Context window	32,768	128,000
Max output	8,192	115,200
Input	text, image, video	text, image
Output	text	text
Tool calling
Structured output
Reasoning control
Added	May 12, 2026	Feb 1, 2025

Key differences

Perceptron: Perceptron Mk1 is 81% cheaper on input tokens.
Qwen: Qwen2.5 VL 72B Instruct is 33% cheaper on output tokens.
Qwen: Qwen2.5 VL 72B Instruct reads 3.9× more context (128,000 tokens).
Only Perceptron: Perceptron Mk1 supports reasoning effort control.
A request with 1,000 input and 500 output tokens costs $0.00315 with Perceptron: Perceptron Mk1 and $0.00455 with Qwen: Qwen2.5 VL 72B Instruct.

In depth

Perceptron: Perceptron Mk1 is made by Perceptron, and Qwen: Qwen2.5 VL 72B Instruct is made by Qwen. Both are listed on api14 as text models, meaning text is what you get back from either of them. The difference is on the input side and in how much room they give you to work with.

Perceptron Mk1 accepts text, images and video as input, while Qwen2.5 VL 72B Instruct accepts text and images but not video. That makes Perceptron Mk1 the only one of the two that can look at moving footage. The context windows point the other way: Qwen2.5 VL 72B Instruct supports 128000 tokens, which is substantially larger than the 32768 tokens Perceptron Mk1 supports. On reasoning, Perceptron Mk1 is the one that exposes reasoning related parameters, so if you want the model to show or control its reasoning, that is the side to look at. On price, Perceptron Mk1 is the cheaper option for input, while Qwen2.5 VL 72B Instruct is cheaper for output, so the total depends on how much text you send versus how much you generate.

For a task that involves video, Perceptron Mk1 is the straightforward choice, since Qwen2.5 VL 72B Instruct cannot take video input at all. For a task that involves a very large amount of text, such as a long document, a big codebase dump or a long conversation history, Qwen2.5 VL 72B Instruct gives you far more context to fit it in. For image plus text work at moderate length, both can handle it, and the decision comes down to whether you want reasoning controls (Perceptron Mk1) or the larger window and the wider set of sampling and output parameters that Qwen2.5 VL 72B Instruct supports, including logit_bias, logprobs, seed and stop. If your workload is mostly short prompts with long generated answers, the cheaper output on Qwen2.5 VL 72B Instruct may matter more to you.

Calling either model through api14 works the same way, because the API is OpenAI compatible. You point your existing OpenAI client at the api14 base URL, send your key, and set the model field to the id you want. To use Perceptron Mk1, set the model to perceptron/perceptron-mk1. To use Qwen2.5 VL 72B Instruct, set the model to qwen/qwen2.5-vl-72b-instruct. Nothing else in the request shape has to change, so switching between them is a one line edit in your config or your request body. You can keep two constants in your code and swap them per call, or route by task type at runtime.

There are still cases where the model you did not pick first is the better one. If you started with Perceptron Mk1 for its video input but your actual workload turns out to be long text with no video, Qwen2.5 VL 72B Instruct is the better fit because of the larger context window. If you started with Qwen2.5 VL 72B Instruct for its context size but you need reasoning controls, Perceptron Mk1 is the one that offers them. Check the live price table on the page before you commit, since the cheaper side differs between input and output.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "perceptron/perceptron-mk1" to "qwen/qwen2.5-vl-72b-instruct" and prompts, streaming and tools keep working.

Perceptron: Perceptron Mk1 details & playground →Qwen: Qwen2.5 VL 72B Instruct 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": "qwen/qwen2.5-vl-72b-instruct",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

Perceptron: Perceptron Mk1 vs Qwen: Qwen2.5 VL 72B Instruct

Specifications

Key differences

In depth

Switch with one string

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