MiniMax M1
About this model
MiniMax M1 is an AI model from MiniMax, made available through the API on June 17, 2025. It can read up to 1,000,000 tokens in one request, roughly 1,875 pages of text, and write up to 40,000 tokens in a single answer.
Through the API it costs $0.40 per million input tokens and $2.20 per million output tokens. Its pricing sits in the middle of the market.
It supports extended reasoning: it can "think" step by step before answering, which helps on maths, code and complex questions. It can call tools and functions, so developers can connect it to search, databases or other software.
Quality scores
Rankings →Not rated yet. Independent test results usually appear a few weeks after a model is released.
Where to use it
Companies that serve this model through an API: 2. Prices per million tokens, cheapest first.
| Provider | Inputi | Outputi | Contexti | Uptime (24 h)i |
|---|---|---|---|---|
MinimaxOfficialCheapest | $0.40 | $2.20 | 1M | 100.0% |
Novitabf16 | $0.55 | $2.20 | 1M | 99.8% |
Availability and prices come from OpenRouter’s public API and are refreshed daily. Uptime is the share of successful requests over the last 24 hours. Some providers run a compressed version of open models (fp8, int4), which is cheaper but can be slightly less accurate.
Use it in your code
Call MiniMax M1 through OpenRouter’s OpenAI-compatible API: create a key on openrouter.ai, then run one of these examples.
curl https://openrouter.ai/api/v1/chat/completions \
-H "Authorization: Bearer $OPENROUTER_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model": "minimax/minimax-m1", "messages": [{"role": "user", "content": "Hello! What can you do?"}]}'import os
from openai import OpenAI
client = OpenAI(base_url="https://openrouter.ai/api/v1", api_key=os.environ["OPENROUTER_API_KEY"])
reply = client.chat.completions.create(
model="minimax/minimax-m1",
messages=[{"role": "user", "content": "Hello! What can you do?"}],
)
print(reply.choices[0].message.content)import OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://openrouter.ai/api/v1", apiKey: process.env.OPENROUTER_API_KEY });
const reply = await client.chat.completions.create({
model: "minimax/minimax-m1",
messages: [{ role: "user", content: "Hello! What can you do?" }],
});
console.log(reply.choices[0].message.content);The same code works with every model on this site: only the model ID changes. Many labs also offer their own API and SDK.
What it costs in practice
- 1,000 chat replies$0.86
- Summarise a 100-page document$0.022
- An app with 10,000 requests a day for a month$384
Similar-priced alternatives
| Model | Inputi | Outputi | Contexti | |
|---|---|---|---|---|
Devstral 2 2512Mistral AI | $0.40 | $2 | 256K | Compare with ⇄ |
Qwen3 VL 235B A22B ThinkingAlibaba Qwen | $0.40 | $4 | 128K | Compare with ⇄ |
ERNIE 4.5 VL 424B A47B Baidu | $0.42 | $1.25 | 123K | Compare with ⇄ |
GLM 4.6Z.ai (Zhipu) | $0.43 | $1.75 | 200K | Compare with ⇄ |
MiMo-V2.6-ProXiaomi | $0.44 | $0.87 | 1.1M | Compare with ⇄ |
Inkling SmallThinking Machines Lab | $0.45 | $1.20 | 512K | Compare with ⇄ |
Frequently asked questions
- How much does MiniMax M1 cost?
- MiniMax M1 costs $0.40 per million input tokens and $2.20 per million output tokens through the API. For example, 1,000 chat replies of about 500 tokens in and 300 out cost around $0.86.
- What is the context window of MiniMax M1?
- MiniMax M1 can read up to 1,000,000 tokens in one request, about 1,875 pages of text. It can write up to 40,000 tokens in a single answer.
- Is MiniMax M1 open source?
- No. MiniMax M1 is a closed model: its weights are not published, so you use it through MiniMax’s own apps and API, or through cloud providers.
- Who makes MiniMax M1?
- MiniMax M1 is made by MiniMax. It became available through the API on June 17, 2025.
- Can MiniMax M1 read images?
- No. MiniMax M1 only accepts text as input.
- What are the alternatives to MiniMax M1?
- Models with a similar price from other companies include Devstral 2 2512, Qwen3 VL 235B A22B Thinking and ERNIE 4.5 VL 424B A47B . You can compare them side by side on this page.