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Perceptron Mk1 vs GLM 5.3 FlashX: API price, context & features · api14

Perceptron: Perceptron Mk1 vs Z.ai: GLM 5.3 FlashX

A side-by-side comparison of Perceptron: Perceptron Mk1 ($0.52 / 1M tokens) and Z.ai: GLM 5.3 FlashX ($1.30 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.

Specifications

	Perceptron: Perceptron Mk1	Z.ai: GLM 5.3 FlashX
Provider	Perceptron	Z.ai
Input	$0.52 / 1M tokens	$1.30 / 1M tokens
Output	$5.25 / 1M tokens	$4.38 / 1M tokens
Cache read	-	$0.26 / 1M tokens
Context window	32,768	1,048,576
Max output	8,192	131,072
Input	text, image, video	text, image, video
Output	text	text
Tool calling
Structured output
Reasoning control
Added	May 12, 2026	Sep 18, 2026

Key differences

Perceptron: Perceptron Mk1 is 59% cheaper on input tokens.
Z.ai: GLM 5.3 FlashX is 17% cheaper on output tokens.
Z.ai: GLM 5.3 FlashX reads 32.0× more context (1,048,576 tokens).
Only Z.ai: GLM 5.3 FlashX supports tool calling.
Only Perceptron: Perceptron Mk1 supports structured JSON output.
A request with 1,000 input and 500 output tokens costs $0.00315 with Perceptron: Perceptron Mk1 and $0.00348 with Z.ai: GLM 5.3 FlashX.

In depth

Perceptron: Perceptron Mk1 is a text model from Perceptron, released on 12 May 2026. Z.ai: GLM 5.3 FlashX is a text model from Z.ai, released on 18 September 2026. Both are served through api14 as chat models, and both accept text, image and video as input while producing text output only. Neither is an embedding or image generation model, so you are choosing between two general purpose text generators with vision input.

The clearest split is context length. GLM 5.3 FlashX has a context window of 1,048,576 tokens, which is far larger than the 32,768 tokens offered by Perceptron Mk1. If your prompt includes long documents, large codebases, extended transcripts or many images and video frames in one request, GLM 5.3 FlashX is the only one of the two that will hold it without chunking. On tooling, GLM 5.3 FlashX is the only one that supports tools and tool_choice, so function calling and agent loops belong to it. Perceptron Mk1 is the only one that supports structured_outputs, which matters when you need the response constrained to a schema. Both expose reasoning and include_reasoning, so reasoning support is not a differentiator here. Perceptron Mk1 also accepts frequency_penalty and presence_penalty, which GLM 5.3 FlashX does not list, while GLM 5.3 FlashX adds reasoning_effort and response_format.

For day to day work, pick Perceptron Mk1 when you want schema constrained output, when you are tuning repetition with frequency or presence penalties, or when your inputs comfortably fit in 32k tokens and you want the cheaper input side. Pick GLM 5.3 FlashX when the job involves tool calls, when you need to pass very large contexts in a single request, or when output volume dominates your bill, since it is the cheaper side for output. A reasonable pattern is to prototype on Perceptron Mk1 for structured extraction and switch to GLM 5.3 FlashX once the same workload needs tools or grows past the smaller window.

Calling either model through api14 works the same way. Both are OpenAI compatible, so you point your existing client at the api14 base URL, send your key, and use the chat completions endpoint. The only change between the two is the model id: use perceptron/perceptron-mk1 for Perceptron Mk1 and z-ai/glm-5.3-flashx for GLM 5.3 FlashX. Your message array, image and video parts, and streaming setup stay as they are. Parameters are the one place you need care, since tools and tool_choice only apply to GLM 5.3 FlashX and structured_outputs only applies to Perceptron Mk1, so keep per model parameter sets rather than one shared config.

There are still cases where Perceptron Mk1 is the better pick even though GLM 5.3 FlashX is larger and tool capable. If your task is a bounded extraction or classification job where the answer must conform to a schema, and the input fits inside 32k tokens, Perceptron Mk1 gives you structured outputs plus penalty controls that GLM 5.3 FlashX does not list, and it is the cheaper side on input. For those narrow, high volume calls, the smaller window is not a limitation and the schema guarantee is worth more than the extra context.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "perceptron/perceptron-mk1" to "z-ai/glm-5.3-flashx" and prompts, streaming and tools keep working.

Perceptron: Perceptron Mk1 details & playground →Z.ai: GLM 5.3 FlashX 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-flashx",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

Perceptron: Perceptron Mk1 vs Z.ai: GLM 5.3 FlashX

Specifications

Key differences

In depth

Switch with one string

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