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Sonar Reasoning Pro vs Pareto 26.9: API price, context & features · api14

Perplexity: Sonar Reasoning Pro vs Pareto 26.9

A side-by-side comparison of Perplexity: Sonar Reasoning Pro ($7.00 / 1M tokens) and Pareto 26.9 ($8.75 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.

Specifications

	Perplexity: Sonar Reasoning Pro	Pareto 26.9
Provider	Perplexity	Unbiased
Input	$7.00 / 1M tokens	$8.75 / 1M tokens
Output	$28.00 / 1M tokens	$26.25 / 1M tokens
Web search	$17.50 / 1K searches	-
Cache read	-	$0.88 / 1M tokens
Context window	128,000	262,144
Max output	115,200	131,072
Input	text, image	text, image
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Mar 7, 2025	Oct 13, 2026

Key differences

Perplexity: Sonar Reasoning Pro is 20% cheaper on input tokens.
Pareto 26.9 is 6% cheaper on output tokens.
Pareto 26.9 reads 2.0× more context (262,144 tokens).
Only Pareto 26.9 supports tool calling.
Only Perplexity: Sonar Reasoning Pro supports reasoning effort control.
A request with 1,000 input and 500 output tokens costs $0.02 with Perplexity: Sonar Reasoning Pro and $0.02 with Pareto 26.9.

In depth

Perplexity: Sonar Reasoning Pro is made by Perplexity and is a text model that accepts text and image input and returns text. Pareto 26.9 is made by Unbiased and is also a text model that accepts text and image input and returns text. Both are served through api14, so the choice between them comes down to behaviour and limits rather than access.

The clearest split is context and tooling. Pareto 26.9 has the larger context window, at 262144 tokens against 128000 for Sonar Reasoning Pro, so it can hold substantially more material in a single request. Pareto 26.9 is also the only one of the two that supports tools and tool_choice, which matters if your application needs the model to call functions or external services. Sonar Reasoning Pro is the only one that exposes reasoning controls, including the reasoning and include_reasoning parameters, and it also supports web_search_options. On price, Sonar Reasoning Pro is the cheaper of the two for input, while Pareto 26.9 is the cheaper for output, so the cost balance depends on how much you send versus how much you generate. Both accept images as input, and neither is meaningfully distinguished by structured output support in the information here.

For practical selection, reach for Sonar Reasoning Pro when the task benefits from explicit reasoning and from search grounding, for example multi step analysis, questions that need current information from the web, or work where you want to inspect or control the reasoning step. Its parameter set, which includes temperature, top_p, top_k, frequency_penalty and presence_penalty, gives you more knobs for shaping generation. Reach for Pareto 26.9 when the job is long context or agentic. Large document sets, long conversation histories, and anything that needs tool calls fit it better, and its lower output price helps when responses are long. If your workload is mostly short prompts with long answers, Pareto 26.9 is likely the cheaper option; if it is mostly long inputs with short answers, Sonar Reasoning Pro is likely cheaper.

Calling either model through api14 works the same way because the API is OpenAI compatible. 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. Switching between the two is a one line change: use "perplexity/sonar-reasoning-pro" for Sonar Reasoning Pro and "unbiased/pareto-26.9" for Pareto 26.9. The request and response shapes stay the same, so existing OpenAI SDK code needs no other edits. Do keep in mind that the two models accept different parameters, so a request that sets reasoning or web_search_options will not carry over cleanly to Pareto 26.9, and a request that sets tools or tool_choice will not carry over cleanly to Sonar Reasoning Pro.

There are still cases where the other model wins. If you need tool calling but your prompts are short and your answers are short, Pareto 26.9 remains the right pick because Sonar Reasoning Pro does not support tools at all. Conversely, if you need reasoning output or web search and your inputs are modest in size, Sonar Reasoning Pro is the better fit even though Pareto 26.9 has the larger window. Neither model is strictly better; match the parameter set and context limit to the shape of your task.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "perplexity/sonar-reasoning-pro" to "unbiased/pareto-26.9" and prompts, streaming and tools keep working.

Perplexity: Sonar Reasoning Pro details & playground →Pareto 26.9 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": "unbiased/pareto-26.9",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

Perplexity: Sonar Reasoning Pro vs Pareto 26.9

Specifications

Key differences

In depth

Switch with one string

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