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Documentation Index

Fetch the complete documentation index at: https://docs.kymaapi.com/llms.txt

Use this file to discover all available pages before exploring further.

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Overview

sonar-pro is Perplexity’s pro web-search model. Like sonar, it answers from a live search of the web with citations — but it searches deeper, reasons over more sources, and writes longer, more complete answers. Use it for complex current-events questions and research tasks where a one-line lookup is not enough. Sonar Pro does its own searching internally. You send a question; it returns a grounded, cited report. It does not support tool calling.

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Specs

FieldValue
Model IDsonar-pro
Best forDeep web research, complex current-events questions, longer cited reports
Context window200K
Max output tokens8K
Input modalitiesText, image
Output modalitiesText
Tool callingNo
Structured outputsNo
Prompt cachingNo
SpeedMedium
Cost bandPremium
Release stageStable

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Pricing

Sonar Pro bills two ways on the same request:
  • Tokens — 4.05/1Minput,4.05 / 1M input, 20.25 / 1M output.
  • Web-search fee — a flat $0.00675 per request, on top of tokens, for the live search.
Because the search fee is flat, it matters less here than on sonar — Sonar Pro’s longer, multi-source answers make the output tokens the larger line item. Every response’s usage.cost already includes the search fee. See Pricing → Web search.

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Use this when

  • The question is complex and current — multi-part research, comparisons across recent sources, “what changed and why.”
  • You want a longer, well-cited answer rather than a quick fact.
  • You need more context room (200K) for a large prompt plus a long grounded response.

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Pick something else when

  • A quick cited lookup is all you need: sonar is cheaper per request.
  • You need function calling / tools: use kimi-k2.6.
  • The task does not need fresh web data: a standard model avoids the per-request search fee entirely.

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Example

from openai import OpenAI

client = OpenAI(base_url="https://kymaapi.com/v1", api_key="ky-...")

response = client.chat.completions.create(
    model="sonar-pro",
    messages=[{"role": "user", "content": "Compare the pricing and context windows of the three newest open-weight coding models released this quarter, with sources."}]
)

print(response.choices[0].message.content)