MiniMax-M2.7 Guide

This page documents how MiniMax-M2.7 is implemented in this deployment. It focuses on API compatibility, model resolution behavior, pricing controls, and safe rollout.

If you need the official model narrative and benchmark report, see:

• https://www.minimax.io/models/text/m27
• https://www.minimax.io/news/minimax-m27-en

What You Get by Enabling M2.7

MiniMax-M2.7 is wired as a first-class registered model in src/lib/minimax/models.ts.

• Model ID: MiniMax-M2.7
• Upstream model ID: MiniMax-M2.7
• Billing model ID: MiniMax-M2.7 (independent attribution)
• Aliases: codex-MiniMax-M2.7, MiniMax M2.7
• Channels: supported in web chat, OpenAI-compatible API, and Anthropic-compatible API

This means you can switch existing integrations to M2.7 by changing only the model value.

API Compatibility

M2.7 uses the exact same request/response contracts as other MiniMax models in this project.

• POST /api/v1/chat/completions (OpenAI-compatible)
• POST /api/v1/messages (Anthropic-compatible)
• POST /api/minimax/chat (hosted web chat backend)

No endpoint path changes or schema changes are required.

Pricing and Context Controls (Environment Variables)

Unlike M2, M2.1, and M2.5 (fixed in code), M2.7 pricing and context are configurable via environment variables.

Validation behavior:

• Invalid or negative price values fall back to defaults.
• Invalid or non-positive MAX_TOKENS falls back to default.
• Fallbacks are logged with warnings.

Defaults and Fallback Logic

Channel defaults remain unchanged after adding M2.7:

• web_chat default: MiniMax-M2.5
• api_openai default: MiniMax-M2
• api_anthropic default: MiniMax-M2

Model resolution behavior:

1. If model is omitted, channel default is used.
2. If model is an alias (for example codex-MiniMax-M2.7), it resolves to MiniMax-M2.7.
3. If model is unknown:
   • default behavior: fallback to channel default
   • strict behavior (MINIMAX_MODEL_STRICT_API=true on API routes): return model error

This design keeps backward compatibility for old clients while allowing controlled adoption of M2.7.

Discover Live Effective Values

Always inspect live model metadata before cost planning or rollout.

GET /api/v1/models

The response includes model-level pricing, capabilities, and per-channel defaults.

{
  "object": "list",
  "data": [
    {
      "id": "MiniMax-M2.7",
      "name": "minimax-m2.7",
      "displayName": "MiniMax M2.7",
      "description": "Latest-generation MiniMax model for advanced coding and agentic workflows",
      "pricing": {
        "inputRateUsd": 0.5,
        "outputRateUsd": 1.5
      },
      "capabilities": {
        "maxTokens": 200000,
        "supportsStreaming": true,
        "supportsThinking": true
      }
    }
  ],
  "defaults": {
    "web_chat": "MiniMax-M2.5",
    "api_openai": "MiniMax-M2",
    "api_anthropic": "MiniMax-M2"
  }
}

Treat this endpoint as the source of truth for your running environment.

Request Examples

OpenAI-compatible

{
  "model": "MiniMax-M2.7",
  "messages": [
    { "role": "system", "content": "You are a senior engineer." },
    { "role": "user", "content": "Refactor this module for reliability." }
  ],
  "stream": true
}

Anthropic-compatible

{
  "model": "MiniMax-M2.7",
  "system": "Give concise and practical answers.",
  "messages": [
    { "role": "user", "content": "Design a staged migration plan." }
  ],
  "max_tokens": 1024
}

Alias-based model selection

These are equivalent and resolve to MiniMax-M2.7:

• MiniMax-M2.7
• codex-MiniMax-M2.7
• MiniMax M2.7

Billing Semantics

Usage recording and charging bind to billingModelId, not the incoming alias string. For M2.7, billing attribution is independent:

• Usage records persist as model MiniMax-M2.7
• Charge calculation uses resolved M2.7 input/output rates
• Dashboard breakdown can compare M2, M2.1, M2.5, and M2.7 consumption separately

Safe Rollout Checklist

1. Keep current defaults unchanged to avoid regressions.
2. Route a small percentage of traffic with model: "MiniMax-M2.7".
3. Compare output quality, latency, and token spend versus baseline.
4. Validate /api/v1/models values in production before budget forecasting.
5. Expand traffic gradually and keep a quick rollback path to default models.

Related Docs

• Available Models
• API Overview
• Chat Completions API
• Messages API