MiniMax M3 pricing for faq bot

Quick start

Call MiniMax M3 with the OpenAI Python SDK pointed at api.minimax.chat:

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.minimax.chat/v1",
    api_key=os.environ["MINIMAX_API_KEY"],
)
response = client.chat.completions.create(
    model="minimax-m3",
    max_tokens=800,
    messages=[{"role": "user", "content": "Your prompt here"}]
)
# Cost: ~$0.001410 per call at standard rates
# Input:  $0.30/MTok
# Output: $1.20/MTok

How MiniMax M3 compares on cost

Per-call cost for all models scoring 50+ on faq bot benchmarks. Hover for details.

Cheapest viable option: GPT 5.6 Luna at $0.001260/call (11% less, 85/100 quality).

What drives faq bot cost

The most bounded question-answering shape: a short question against a fixed knowledge base, with a short answer. Because the knowledge base never changes between calls, cache reads apply to nearly the entire input, and the effective cost per answer falls well below the list rate. A fast-tier model is usually sufficient when the answers are already written.

For MiniMax M3 specifically, that means output tokens cost 4.0x more than input, at $1.20 vs $0.30 per MTok. At this use case’s 1,500/800 token split, output accounts for 68% of the $0.001410 per-call cost.

MiniMax M3 quality benchmarks for faq bot

Benchmark data for faq bot is pending for MiniMax M3. Test against your own evaluation suite before moving to production.

Benchmark data is not yet available for MiniMax M3. We recommend testing against your own evaluation criteria.

About MiniMax M3

Prices from platform.minimax.io/docs/pricing/overview (read 2026-10-11). Long context (>512K tokens) is billed above the listed input and output rates. Cache hit: $0.06/MTok input. No batch discount published. Not yet benchmarked, so never recommended.

Other models from MiniMax

You can compare MiniMax M3 head-to-head against any other model on our model comparison pages, or track how pricing has changed over time on the pricing history tracker.

Frequently asked questions