MiniMax M3 API: Complete Integration Guide

Choose the protocol before the SDK

MiniMax supports both Anthropic- and OpenAI-compatible clients. The vendor recommends its Anthropic-compatible endpoint when you need the full thinking and interleaved-thinking behavior:

• global Anthropic base URL: https://api.minimax.io/anthropic
• global OpenAI base URL: https://api.minimax.io/v1
• mainland China equivalents: api.minimax.cn

Pick one protocol and test your complete tool schema on it. Compatibility reduces migration work, but it does not mean every provider-specific reasoning field behaves identically.

Current pay-as-you-go price

The global Standard tier showed the following active rates on September 11, 2026, in USD per 1M tokens:

Priority service is 1.5 times those active rates. MiniMax labels the current numbers as a permanent 50% discount, but a product label is not an immutable contract. Save the rate-card date with any budget.

Prompt caching begins with eligible inputs of at least 512 tokens. Cached tokens still count when MiniMax decides whether the request crosses the 512K price boundary. A stable prefix can reduce cost; a timestamp or reordered file near the start can turn the next request into a miss.

### When Priority is worth testing

Priority service is useful only when lower queueing delay changes the value of the result. An interactive coding assistant may justify it; a nightly index or evaluation batch often will not. Run representative prompts on both tiers and compare tail latency, not just the median. Include retries and failed requests in the calculation. Paying 1.5 times the token rate for a job that still misses its deadline is not a performance strategy.

Curl example

curl https://api.minimax.io/v1/chat/completions \
  -H "Authorization: Bearer $MINIMAX_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"MiniMax-M3","messages":[{"role":"user","content":"List the risky parts of this migration."}]}'

Python example

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["MINIMAX_API_KEY"],
    base_url="https://api.minimax.io/v1",
)

result = client.chat.completions.create(
    model="MiniMax-M3",
    messages=[{"role": "user", "content": "Return three test cases."}],
)
print(result.choices[0].message.content)

For AIWave, the client shape stays the same while the base URL and credential change:

client = OpenAI(
    api_key=os.environ["AIWAVE_API_KEY"],
    base_url="https://aiwave.live/v1",
)

The current AIWave catalog lists MiniMax-M3. Check the dated AIWave rate card before a run; a gateway price includes its own route and commercial terms, so it should not be presented as MiniMax's direct price.

Long-context discipline

A million-token ceiling is useful when the evidence genuinely belongs in one request. It can also hide poor retrieval. Start by separating stable context from changing context:

• stable: architecture maps, interfaces, policy files, long reference sets;
• changing: the current diff, user instruction, error log, and tool results.

Keep the stable prefix deterministic if you expect caching. Reserve answer and tool-call headroom instead of filling the window. MiniMax's public general API documentation does not give one independent maximum-output number for every case, so test the output length your application requires rather than copying a benchmark setting into production.

Before launch, verify image/video payload handling, tool calls, streaming, timeouts, cache-hit accounting, and the >512K tier with your own account. Log the returned model ID and token usage for every request. If you route through a gateway, reconcile that response against the gateway's per-request ledger.

Primary references

• MiniMax text generation guide
• API overview
• Pay-as-you-go pricing
• Prompt caching
• MiniMax M3 release