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Model comparison

o4-mini vs Sonar Reasoning Pro

Not a benchmark table. This puts pricing, context, interface fit, and key visibility into one decision card.

Provider
OpenAI / Perplexity
global / global
Context
200K / 200K
text+image->text / text->text
Input price
$1.10 / $2.00
per 1M tokens
Output price
$4.40 / $8.00
per 1M tokens
Left model
o4-mini
OpenAI
Familyo-series
Modalitytext+image->text

适合推理型产品化调用和代理场景。

Right model
Sonar Reasoning Pro
Perplexity
FamilySonar
Modalitytext->text

研究和检索融合场景里的高意图品牌词入口。

Comparison summary

How to choose first

This is a cross-provider comparison. Start with the job boundary, then verify what your key can actually see.

On the listed price snapshot, o4-mini is cheaper on combined input and output, but real routing, discounts, and limits still matter.

When context is similar, compare output quality, API stability, limits, and actually callable models first.

Decision boundary

Do not start with which model is absolutely stronger. Start with the boundary: cost, context, speed, quality, ecosystem, or supply stability.

  • o4-mini is worth checking first when the o-series family, 200K context, and text+image->text capability match the job.
  • Sonar Reasoning Pro is worth checking first when the Sonar family, 200K context, and text->text capability match the job.

Key checking route

If you already hold a key, the valuable check is provider identity, callable models, and whether balance, limits, or subscription status are visible.

  • OpenAI: o4-mini, o-series, text+image->text
  • Perplexity: Sonar Reasoning Pro, Sonar, text->text

Commercial fit

Commercially, do not look at model names alone. Combine price, limits, region, upstream stability, and ongoing monitoring.

  • o4-mini: 适合推理型产品化调用和代理场景。
  • Sonar Reasoning Pro: 研究和检索融合场景里的高意图品牌词入口。