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Perceptron: Perceptron Mk1 vs Qwen2.5 Coder 32B Instruct

A side-by-side comparison of Perceptron: Perceptron Mk1 ($0.52 / 1M tokens) and Qwen2.5 Coder 32B Instruct ($2.31 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.

Specifications

	Perceptron: Perceptron Mk1	Qwen2.5 Coder 32B Instruct
Provider	Perceptron	Qwen
Input	$0.52 / 1M tokens	$2.31 / 1M tokens
Output	$5.25 / 1M tokens	$3.50 / 1M tokens
Context window	32,768	32,768
Max output	8,192	29,491
Input	text, image, video	text
Output	text	text
Tool calling
Structured output
Reasoning control
Added	May 12, 2026	Nov 11, 2024

Key differences

Perceptron: Perceptron Mk1 is 77% cheaper on input tokens.
Qwen2.5 Coder 32B Instruct is 33% cheaper on output tokens.
Only Perceptron: Perceptron Mk1 supports structured JSON output.
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 $0.00406 with Qwen2.5 Coder 32B Instruct.

In depth

Perceptron Mk1 is a text model from Perceptron, released on 2026-05-12. Qwen2.5 Coder 32B Instruct is a text model from Qwen, released on 2024-11-11. Both are served through api14 under the text category, and both accept text prompts and return text output, so at the level of basic request and response shape they are interchangeable.

The two differ in what they accept and what they expose. Perceptron Mk1 takes image and video input alongside text, while Qwen2.5 Coder 32B Instruct takes text only. Both list a context length of 32768 tokens, so there is no meaningful difference in how much you can send in one call. Perceptron Mk1 is the only one of the two that supports structured outputs and the only one that supports reasoning, and its parameter list includes include_reasoning and reasoning. Qwen2.5 Coder 32B Instruct instead exposes a wider set of sampling and formatting controls, including logit_bias, min_p, repetition_penalty, response_format, seed and stop. Neither model is listed as supporting tools.

For a developer choosing between them, the deciding factor is usually the input. If your task involves screenshots, diagrams, frames from a video, or any other visual material that the model needs to read, Perceptron Mk1 is the one that can accept it, and it can also return structured output and expose reasoning, which suits extraction, classification and multi step analysis where you want the intermediate thinking surfaced. If your task is purely textual and especially if it is code, Qwen2.5 Coder 32B Instruct gives you finer control over generation through seed, stop, logit_bias and repetition_penalty, which matters when you need reproducible or tightly constrained output. On cost, Perceptron Mk1 is cheaper on input and Qwen2.5 Coder 32B Instruct is cheaper on output, so the better value depends on whether your workload is input heavy or output heavy. Check the live price table for the current numbers.

Calling either model through api14 works the same way, because the API is OpenAI compatible. You send a chat completions request to the same endpoint with the same authentication, and the only thing that changes is the model id. Use perceptron/perceptron-mk1 for Perceptron Mk1 and qwen/qwen-2.5-coder-32b-instruct for Qwen2.5 Coder 32B Instruct. Everything else in your request body, such as messages, temperature and max_tokens, stays where it is, though parameters that one model does not list will not apply to it. This makes switching between the two a one line change, so you can route different requests to different models without rewriting your client.

There are still cases where the other model is the better pick. If you need structured outputs or reasoning, Qwen2.5 Coder 32B Instruct does not offer them, so Perceptron Mk1 is the only option. If you need seed, stop, logit_bias or repetition_penalty, Perceptron Mk1 does not list them, so Qwen2.5 Coder 32B Instruct is the one to call. And if your output is long and you are paying for it, the output side pricing favors Qwen2.5 Coder 32B Instruct, while input heavy work favors Perceptron Mk1.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "perceptron/perceptron-mk1" to "qwen/qwen-2.5-coder-32b-instruct" and prompts, streaming and tools keep working.

Perceptron: Perceptron Mk1 details & playground →Qwen2.5 Coder 32B 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/qwen-2.5-coder-32b-instruct",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

More comparisons

Perceptron Mk1 vs Qwen2.5 Coder 32B Instruct: API price, context & features · api14