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Perceptron Mk1 vs Llama 3.1 Euryale 70B v2.2: API price, context & features · api14

Perceptron: Perceptron Mk1 vs Sao10K: Llama 3.1 Euryale 70B v2.2

A side-by-side comparison of Perceptron: Perceptron Mk1 ($0.52 / 1M tokens) and Sao10K: Llama 3.1 Euryale 70B v2.2 ($2.98 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.

Specifications

	Perceptron: Perceptron Mk1	Sao10K: Llama 3.1 Euryale 70B v2.2
Provider	Perceptron	Sao10K
Input	$0.52 / 1M tokens	$2.98 / 1M tokens
Output	$5.25 / 1M tokens	$2.98 / 1M tokens
Context window	32,768	131,072
Max output	8,192	16,384
Input	text, image, video	text
Output	text	text
Tool calling
Structured output
Reasoning control
Added	May 12, 2026	Aug 28, 2024

Key differences

Perceptron: Perceptron Mk1 is 82% cheaper on input tokens.
Sao10K: Llama 3.1 Euryale 70B v2.2 is 43% cheaper on output tokens.
Sao10K: Llama 3.1 Euryale 70B v2.2 reads 4.0× more context (131,072 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 $0.00446 with Sao10K: Llama 3.1 Euryale 70B v2.2.

In depth

Perceptron: Perceptron Mk1 is a text model from Perceptron, released on 2026-05-12. Sao10K: Llama 3.1 Euryale 70B v2.2 comes from Sao10K and was released on 2024-08-28. Both are served as text category models on api14, and both return text as their only output modality, so anything you generate with either one comes back as text.

The two diverge most clearly on context, modalities and reasoning. Perceptron Mk1 accepts text, image and video as input, while the Euryale model accepts text only. That makes Perceptron Mk1 the choice when your prompt needs to include a screenshot, a diagram, a scanned page or a video clip alongside written instructions. The Euryale model has the larger context window, at 131072 tokens against 32768 for Perceptron Mk1, so it can hold far more text in a single call, which matters for long documents, extended transcripts or large volumes of reference material. Perceptron Mk1 is the only one of the two described as supporting reasoning, and its parameter set includes include_reasoning and reasoning, so it is the model to reach for when you want the model to work through a problem before answering. The Euryale model instead exposes a broader set of sampling and control parameters, including logit_bias, min_p, repetition_penalty, seed and stop, which gives you finer control over how its text is produced. On structured outputs the two are not meaningfully different, and neither is singled out as the only one supporting tools.

For picking between them, start with the shape of the input. If your task involves images or video, only Perceptron Mk1 can take it, so the decision is already made. If your task is text only but the material is very long, the Euryale model's larger context is a real advantage, and its extra knobs (seed for reproducibility, logit_bias for steering, repetition_penalty for looping text) suit generation work where you want to tune output. If your task is a text problem that benefits from explicit reasoning, Perceptron Mk1 is the one to call. On cost, input is cheaper on Perceptron Mk1, while output is cheaper on the Euryale model, so the cheaper option depends on whether your workload is input heavy or output heavy; the live price table shows the current numbers.

Calling either model works the same way through api14's OpenAI-compatible API. You point your client at the api14 base URL, send your request to the chat completions endpoint with your api14 key, and set the model field to the id you want. To use Perceptron Mk1, set the model id to perceptron/perceptron-mk1. To use the Euryale model, set it to sao10k/l3.1-euryale-70b. Everything else in the request body keeps the same shape, so switching models is a one line change to the model id. Keep in mind that parameters are not identical across the two: Perceptron Mk1 supports reasoning and include_reasoning, which the Euryale model does not, and the Euryale model supports logit_bias, min_p, repetition_penalty, seed and stop, which Perceptron Mk1 does not. Send only parameters the target model lists, and the same code path will serve both.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "perceptron/perceptron-mk1" to "sao10k/l3.1-euryale-70b" and prompts, streaming and tools keep working.

Perceptron: Perceptron Mk1 details & playground →Sao10K: Llama 3.1 Euryale 70B v2.2 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": "sao10k/l3.1-euryale-70b",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

Perceptron: Perceptron Mk1 vs Sao10K: Llama 3.1 Euryale 70B v2.2

Specifications

Key differences

In depth

Switch with one string

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