MiniMax: MiniMax M2.1

Public pricingIntelligence 82/100Medium memory深度思考工具调用

MiniMax: MiniMax M2.1 是一款文本模型，适合编码、软件工程和智能体工作流。它结合了强大的编码能力、可靠的工具调用与智能体表现、197K tokens上下文和低成本定位，可在编码、软件工程和智能体工作流中提供可靠表现。它适合重视质量、速度与成本的团队，能带来稳定输出、灵活部署与扩展空间。 它适合需要稳定回答、较长上下文、清晰结构和可扩展部署的团队。 它适合需要稳定回答、较长上下文、清晰结构和可扩展部署的团队。 它适合需要稳定回答、较长上下文、清晰结构和可扩展部署的团队。 它适合需要稳定回答、较长上下文、清晰结构和可扩展部署的团队。 它适合需要稳定回答、较长上下文、清晰结构和可扩展部署的团队。 它适合需要稳定回答、较长上下文、清晰结构和可扩展部署的团队。 它适合需要稳定回答、较长上下文、清晰结构和可扩展部署的团队。

Input

$0.29/1M

Output

$0.95/1M

Cached

$0.03/1M

Batch

$0.15/1M

Calculate your MiniMax M2.1 bill.

Set your workload — see cost at your exact volume.

What would MiniMax M2.1 cost you?

Adjust the workload to see your monthly bill.

1,00010,00050,000250,0001M10M

Technical specifications

MiniMax M2.1 at a glance.

Memory

196,608

tokens

Max reply

196,608

tokens

Memory tier

Medium

a long report or a codebase file

Tokenizer

—

Released

Dec 2025

Training cutoff

Sep 2025

Availability

Public pricing

Status

active

Benchmarks

Quality benchmarks

Independent evaluations from public leaderboards. Higher is better.

  • aime_2025

    83

  • gpqa_diamond

    83

  • ifeval

    70

  • mmlu_pro

    88

What it can do

Capabilities & limits.

  • Understands images
  • Deep step-by-step thinking
  • Uses tools / calls functions
  • Strict JSON output
  • Streams replies
  • Fine-tunable on your data

When to pick MiniMax M2.1

  • Multi-step reasoning, research agents, or hard math.
  • Agentic workflows that call tools or APIs.
  • High-volume workloads where unit cost matters.

When to look elsewhere

  • Your workload involves images — pick a vision-capable model instead.

Alternatives

Similar models to compare.

FAQ

MiniMax M2.1 — the questions we see most.

Pricing, capabilities, alternatives — generated from the same data that powers the calculator above.

Get instant answers from our AI agent

At a typical workload of 50,000 conversations a month with 1,500-token prompts and 800-token replies, MiniMax M2.1 costs roughly $60 per month. Input is $0.29 /1M tokens and output is $0.95 /1M tokens.
MiniMax M2.1 has a 196,608-token context window (medium memory — a long report or a codebase file). That means you can fit about 36,864 words of input and history in a single call.
Beyond text generation, MiniMax M2.1 supports deep step-by-step reasoning, calling functions / tools, strict JSON output, fine-tuning on your own data. It streams replies by default.
MiniMax M2.1 was released in December 2025, with training data cut off around September 2025.
Models in a similar class include MiniMax M2-her, MiniMax M2.7, MiniMax M2. The "Similar models" section below this FAQ links into each.

Still unsure?

Compare MiniMax M2.1 against 100+ other models.

Open the full wizard — pick a use case, set your usage, and see side-by-side monthly costs in under a minute.

Open the comparison wizard