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Model comparison

DeepSeek V3.1 vs Kimi K2

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

Provider
DeepSeek / Moonshot AI
china / china
Context
128K / 200K
text->text / text->text
Input price
$0.20 / $0.35
per 1M tokens
Output price
$0.30 / $1.60
per 1M tokens
Left model
DeepSeek V3.1
DeepSeek
FamilyDeepSeek
Modalitytext->text

适合做价格带和版本代际对比。

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, DeepSeek V3.1 is cheaper on combined input and output, but real routing, discounts, and limits still matter.

Kimi K2 has the larger context window, which helps with long documents, knowledge bases, logs, and multi-turn workflows.

Decision boundary

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

  • DeepSeek V3.1 is worth checking first when the DeepSeek family, 128K context, and text->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.

  • DeepSeek: DeepSeek V3.1, DeepSeek, text->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.

  • DeepSeek V3.1: 适合做价格带和版本代际对比。
  • Kimi K2: 长上下文办公、搜索和知识场景里很常见。