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Model comparison

o4-mini vs GLM 5.1

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

Provider
OpenAI / Zhipu AI (GLM)
global / china
Context
200K / 202.8K
text+image->text / text->text
Input price
$1.10 / $1.40
per 1M tokens
Output price
$4.40 / $4.40
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
GLM 5.1
Zhipu AI (GLM)
FamilyGLM
Modalitytext->text

智谱高阶通用路线代表,适合中国企业复杂推理场景。

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.

GLM 5.1 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.

  • o4-mini is worth checking first when the o-series family, 200K context, and text+image->text capability match the job.
  • GLM 5.1 is worth checking first when the GLM family, 202.8K 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
  • Zhipu AI (GLM): GLM 5.1, GLM, 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。
  • GLM 5.1: 智谱高阶通用路线代表,适合中国企业复杂推理场景。