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Model comparison

Kimi K2 vs ERNIE 4.5 300B A47B

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

Provider
Moonshot AI / Baidu Wenxin
china / china
Context
200K / 123K
text->text / text->text
Input price
$0.35 / $0.28
per 1M tokens
Output price
$1.60 / $1.10
per 1M tokens
Left 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。

Right model
ERNIE 4.5 300B A47B
Baidu Wenxin
FamilyERNIE
Modalitytext->text

文心代表型号之一,适合中国站品牌词与政企流量承接。

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, ERNIE 4.5 300B A47B 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.

  • Kimi K2 is worth checking first when the Kimi family, 200K context, and text->text capability match the job.
  • ERNIE 4.5 300B A47B is worth checking first when the ERNIE family, 123K 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.

  • Moonshot AI: Kimi K2, Kimi, text->text
  • Baidu Wenxin: ERNIE 4.5 300B A47B, ERNIE, text->text

Commercial fit

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

  • Kimi K2: 长上下文办公、搜索和知识场景里很常见。
  • ERNIE 4.5 300B A47B: 文心代表型号之一,适合中国站品牌词与政企流量承接。