MiniMax: MiniMax M1

Public pricingIntelligence 76/100Huge memory深度思考工具调用

MiniMax: MiniMax M1 是一款文本模型，适合推理与问题求解。它结合了深度推理与规划、低延迟与高效推理、1M+ tokens上下文和均衡成本定位，可在reasoning, analysis, and hard problem solving中提供可靠表现。它适合重视延迟、成本与吞吐的团队，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。

Input

$0.40/1M

Output

$2.20/1M

Cached

$0.04/1M

Batch

$0.20/1M

Calculate your MiniMax M1 bill.

Set your workload — see cost at your exact volume.

What would MiniMax M1 cost you?

Adjust the workload to see your monthly bill.

1,00010,00050,000250,0001M10M

Technical specifications

MiniMax M1 at a glance.

Memory

1,000,000

tokens

Max reply

40,000

tokens

Memory tier

Huge

multiple books or whole repositories

Tokenizer

—

Released

Jun 2025

Training cutoff

Jun 2024

Availability

Public pricing

Status

active

Benchmarks

Quality benchmarks

Independent evaluations from public leaderboards. Higher is better.

  • swe_bench_verified

    56

  • livecodebench

    65

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 M1

  • Multi-step reasoning, research agents, or hard math.
  • Agentic workflows that call tools or APIs.
  • Long documents, full codebases, or extensive chat histories.
  • 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 M1 — 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 M1 costs roughly $118 per month. Input is $0.40 /1M tokens and output is $2.20 /1M tokens.
MiniMax M1 has a 1,000,000-token context window (huge memory — multiple books or whole repositories). That means you can fit about 187,500 words of input and history in a single call.
Beyond text generation, MiniMax M1 supports deep step-by-step reasoning, calling functions / tools, strict JSON output, fine-tuning on your own data. It streams replies by default.
MiniMax M1 was released in June 2025, with training data cut off around June 2024.
Models in a similar class include MiMo-V2-Omni, Qwen3.5 397B A17B, GLM 4.6. The "Similar models" section below this FAQ links into each.

Still unsure?

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