MiniMax

MiniMax M3

About

MiniMax M3 is a 428B open-weight model from MiniMax. MiniMax M3. Text, multi-image, and native video. Reasoning model. It accepts text, images, and video. Context window is 1M. Released June 2, 2026.

Parameters
428B
Published parameter count.
Context window
1M
Tokens of context on a request.
License
MiniMax Community
License on the weights.

Pricing

Token rates are US dollars per 1M tokens. Image rates are per 1K images. Audio rates are per 1M audio seconds.

Rate	Price
Input / 1M	$0.30
Cached input / 1M	$0.06
Output / 1M	$1.20

Compare

MiniMax M3 is one of the catalog's chat models. The table is each model's published price, size, and context.

Model	What it does	Price	Parameters	Context
MiniMax M3	MiniMax M3. Text, multi-image, and native video. Reasoning model.	$0.30 / 1M input	428B	1M
Qwen3.5 0.8B	Multimodal chat model with up to 64 images or one video per request.	$0.08 / 1M input	0.8B	256K
Gemini 3.5 Flash Lite	Fastest and cheapest Gemini tier. Multimodal chat; emits no reasoning tokens.	$0.30 / 1M input	—	1M
Gemini 3.7 Flash	Newest Gemini Flash tier with extended reasoning. Multimodal chat + native video.	$0.75 / 1M input	—	1M

Benchmarks

Published scores for MiniMax M3, from the MiniMax-M3 FlagOS card.

Evals

Reading	Example
Qualitative	Clear structure, grounded in the input
Quantitative	SWE-Bench Pro: 59.
Cost and performance	Lower listed rate, mid-pack latency

Methods

Methods this model serves. Payload shapes are in the docs.

Method	Returns
detection	Boxes around objects
chat	The model's reply

Estimate cost

Estimate. An hour of video is 15 frames a minute at 256 tokens a frame, plus the audio in that request, and 500 output tokens a minute.

Hours of video

$139.68

Quick start

Call this model on the OpenAI-compatible gateway. The model id is already filled in.

from openai import OpenAI

client = OpenAI(
    base_url="https://gateway.vlm.run/v1/openai",
    api_key="<VLMRUN_API_KEY>",
)

response = client.chat.completions.create(
    model="minimax/minimax-m3",
    messages=[{"role": "user", "content": "What is in this image?"}],
)

print(response.choices[0].message.content)