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Model comparison

GPT-5.4 Nano vs MiniMax M1

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

Provider
OpenAI / MiniMax
global / china
Context
400K / 1M
text+image+file->text / text->text
Input price
$0.20 / $0.40
per 1M tokens
Output price
$1.25 / $2.20
per 1M tokens
Left model
GPT-5.4 Nano
OpenAI
FamilyGPT-5.4
Modalitytext+image+file->text

面向低成本高并发任务的入门位,适合分类、抽取和轻量子代理。

Right model
MiniMax M1
MiniMax
FamilyMiniMax
Modalitytext->text

百万上下文级别,适合企业知识处理与复杂分析。

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, GPT-5.4 Nano is cheaper on combined input and output, but real routing, discounts, and limits still matter.

MiniMax M1 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.

  • GPT-5.4 Nano is worth checking first when the GPT-5.4 family, 400K context, and text+image+file->text capability match the job.
  • MiniMax M1 is worth checking first when the MiniMax family, 1M 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: GPT-5.4 Nano, GPT-5.4, text+image+file->text
  • MiniMax: MiniMax M1, MiniMax, text->text

Commercial fit

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

  • GPT-5.4 Nano: 面向低成本高并发任务的入门位,适合分类、抽取和轻量子代理。
  • MiniMax M1: 百万上下文级别,适合企业知识处理与复杂分析。