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Model comparison

Kimi K2.5 vs GLM 4.5V

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

Provider
Moonshot AI / Zhipu AI (GLM)
china / china
Context
262.1K / 128K
text->text / text+image->text
Input price
$0.383 / $0.80
per 1M tokens
Output price
$1.72 / $2.80
per 1M tokens
Left model
Kimi K2.5
Moonshot AI
FamilyKimi
Modalitytext->text

Kimi 产品线里兼顾热度与能力的代表模型。

Right model
GLM 4.5V
Zhipu AI (GLM)
FamilyGLM Vision
Modalitytext+image->text

视觉理解场景和企业多模态流程的重要入口。

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, Kimi K2.5 is cheaper on combined input and output, but real routing, discounts, and limits still matter.

Kimi K2.5 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 K2.5 is worth checking first when the Kimi family, 262.1K context, and text->text capability match the job.
  • GLM 4.5V is worth checking first when the GLM Vision family, 128K context, and text+image->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 K2.5, Kimi, text->text
  • Zhipu AI (GLM): GLM 4.5V, GLM Vision, text+image->text

Commercial fit

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

  • Kimi K2.5: Kimi 产品线里兼顾热度与能力的代表模型。
  • GLM 4.5V: 视觉理解场景和企业多模态流程的重要入口。