About
MiniMax-M3 is a multimodal foundation model from MiniMax.
Pricing
| Input | $0.42 from $0.31 | per 1M prompt tokens |
|---|---|---|
| Output | $1.68 from $1.23 | per 1M generated tokens, reasoning included |
| Cached input | $0.08 | per 1M tokens read back from the prompt cache |
50% off right now. The provider is running a promotion, and the developer API bills what the provider actually charged for your call plus our flat markup, so it reaches your bill with nothing to claim. The “from” figure is the cheapest endpoint serving the model; which one answers is the provider’s routing decision, so treat it as a floor. Promotions can end at any time.
What a request costs
| Typical request | Each | Per 1,000 |
|---|---|---|
| Short chat reply1,500 in · 400 out | $0.0013 | $1.30 |
| Question over a long document40,000 in · 800 out | $0.018 | $18.14 |
| Agent tool step20,000 in · 1,200 out | $0.010 | $10.42 |
At the developer-API rate, before any provider promotion or prompt-cache saving. In the app, the model picker shows what a message costs on your plan before you send it.
Benchmarks
Head-to-head rankings
- #12SlidesAgent arenaElo 118047% wins
- #19Game devAgent arenaElo 115845% wins
- #19Full stackAgent arenaElo 119747% wins
- #21Web appsAgent arenaElo 119347% wins
- #23Android appsAgent arenaElo 117144% wins
- #23Mobile appsAgent arenaElo 116646% wins
- #35ASCII artModel arenaElo 116247% wins
- #37WebsitesModel arenaElo 126052% wins
- #39SVGModel arenaElo 116045% wins
- #40CodeModel arenaElo 125051% wins
Scores from Artificial Analysis; bars are scaled to the best model on our list. Rankings from Design Arena head-to-head votes. Published by OpenRouter alongside its catalogue.
Call it from code
OpenAI-compatible: point any OpenAI SDK at https://api.privateer.pro/v1 and set
model. Keys are created in the app under Settings.
curl https://api.privateer.pro/v1/chat/completions \
-H "Authorization: Bearer $PRIVATEER_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "minimax/minimax-m3",
"messages": [{ "role": "user", "content": "Hello" }]
}'
import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.privateer.pro/v1",
api_key=os.environ["PRIVATEER_API_KEY"],
)
reply = client.chat.completions.create(
model="minimax/minimax-m3",
messages=[{"role": "user", "content": "Hello"}],
)
print(reply.choices[0].message.content)
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://api.privateer.pro/v1",
apiKey: process.env.PRIVATEER_API_KEY,
});
const reply = await client.chat.completions.create({
model: "minimax/minimax-m3",
messages: [{ role: "user", content: "Hello" }],
});
console.log(reply.choices[0].message.content);
Parameters this model accepts
frequency_penaltyinclude_reasoninglogit_biaslogprobsmax_tokensmin_ppresence_penaltyreasoningrepetition_penaltyresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_ktop_logprobstop_p