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Model comparison

o4-mini vs Kimi K2

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

Provider
OpenAI / Moonshot AI
global / china
Context
200K / 200K
text+image->text / text->text
Input price
$1.10 / $0.35
per 1M tokens
Output price
$4.40 / $1.60
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
Kimi K2
Moonshot AI
FamilyKimi
Modalitytext->text

长上下文办公、搜索和知识场景里很常见。

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, Kimi K2 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.
  • Kimi K2 is worth checking first when the Kimi 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
  • Moonshot AI: Kimi K2, Kimi, 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。
  • Kimi K2: 长上下文办公、搜索和知识场景里很常见。