Day 0 支援：MiniMax-M2.5

LiteLLM 現在在 Day 0 支援 MiniMax-M2.5。可透過 LiteLLM AI Gateway，在 OpenAI 相容與 Anthropic 相容的 API 中使用它。

支援的模型

LiteLLM 支援以下 MiniMax 模型：

模型	說明	輸入成本	輸出成本	上下文視窗
MiniMax-M2.5	進階推理、代理式能力	$0.3/M tokens	$1.2/M tokens	1M tokens
MiniMax-M2.5-lightning	更快且更敏捷（約 100 tps）	$0.3/M tokens	$2.4/M tokens	1M tokens

支援的功能

• Prompt 快取：使用快取的 prompts 降低成本（cache read 為 $0.03/M tokens，cache write 為 $0.375/M tokens）
• Function Calling：內建工具呼叫支援
• 推理：支援 thinking 的進階推理能力
• System Messages：完整支援 system message
• 成本追蹤：自動計算所有請求的成本

Docker 映像檔

docker pull litellm/litellm:v1.81.3-stable

使用方式 - OpenAI 相容 API (/v1/chat/completions)

LiteLLM Proxy

1. 設定 config.yaml

model_list:
  - model_name: minimax-m2-5
    litellm_params:
      model: minimax/MiniMax-M2.5
      api_key: os.environ/MINIMAX_API_KEY
      api_base: https://api.minimax.io/v1

2. 啟動 proxy

docker run -d \
  -p 4000:4000 \
  -e MINIMAX_API_KEY=$MINIMAX_API_KEY \
  -v $(pwd)/config.yaml:/app/config.yaml \
  ghcr.io/berriai/litellm:v1.81.3-stable \
  --config /app/config.yaml

3. 測試它！

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer $LITELLM_KEY' \
--data '{
  "model": "minimax-m2-5",
  "messages": [
    {
      "role": "user",
      "content": "what llm are you"
    }
  ]
}'

搭配 Reasoning Split

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer $LITELLM_KEY' \
--data '{
  "model": "minimax-m2-5",
  "messages": [
    {
      "role": "user",
      "content": "Solve: 2+2=?"
    }
  ],
  "extra_body": {
    "reasoning_split": true
  }
}'

使用方式 - Anthropic 相容 API (/v1/messages)

LiteLLM Proxy

1. 設定 config.yaml

model_list:
  - model_name: minimax-m2-5
    litellm_params:
      model: minimax/MiniMax-M2.5
      api_key: os.environ/MINIMAX_API_KEY
      api_base: https://api.minimax.io/anthropic/v1/messages

2. 啟動 proxy

docker run -d \
  -p 4000:4000 \
  -e MINIMAX_API_KEY=$MINIMAX_API_KEY \
  -v $(pwd)/config.yaml:/app/config.yaml \
  ghcr.io/berriai/litellm:v1.81.3-stable \
  --config /app/config.yaml

3. 測試它！

curl --location 'http://0.0.0.0:4000/v1/messages' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer $LITELLM_KEY' \
--data '{
  "model": "minimax-m2-5",
  "max_tokens": 1000,
  "messages": [
    {
      "role": "user",
      "content": "what llm are you"
    }
  ]
}'

搭配 Thinking

curl --location 'http://0.0.0.0:4000/v1/messages' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer $LITELLM_KEY' \
--data '{
  "model": "minimax-m2-5",
  "max_tokens": 1000,
  "thinking": {
    "type": "enabled",
    "budget_tokens": 1000
  },
  "messages": [
    {
      "role": "user",
      "content": "Solve: 2+2=?"
    }
  ]
}'

使用方式 - LiteLLM SDK

OpenAI 相容 API

import litellm

response = litellm.completion(
    model="minimax/MiniMax-M2.5",
    messages=[
        {"role": "user", "content": "Hello, how are you?"}
    ],
    api_key="your-minimax-api-key",
    api_base="https://api.minimax.io/v1"
)

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

Anthropic 相容 API

import litellm

response = litellm.anthropic.messages.acreate(
    model="minimax/MiniMax-M2.5",
    messages=[{"role": "user", "content": "Hello, how are you?"}],
    api_key="your-minimax-api-key",
    api_base="https://api.minimax.io/anthropic/v1/messages",
    max_tokens=1000
)

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

搭配 Thinking

response = litellm.anthropic.messages.acreate(
    model="minimax/MiniMax-M2.5",
    messages=[{"role": "user", "content": "Solve: 2+2=?"}],
    thinking={"type": "enabled", "budget_tokens": 1000},
    api_key="your-minimax-api-key"
)

# Access thinking content
for block in response.choices[0].message.content:
    if hasattr(block, 'type') and block.type == 'thinking':
        print(f"Thinking: {block.thinking}")

搭配 Reasoning Split（OpenAI API）

response = litellm.completion(
    model="minimax/MiniMax-M2.5",
    messages=[
        {"role": "user", "content": "Solve: 2+2=?"}
    ],
    extra_body={"reasoning_split": True},
    api_key="your-minimax-api-key",
    api_base="https://api.minimax.io/v1"
)

# Access thinking and response
if hasattr(response.choices[0].message, 'reasoning_details'):
    print(f"Thinking: {response.choices[0].message.reasoning_details}")
print(f"Response: {response.choices[0].message.content}")

成本追蹤

LiteLLM 會自動追蹤 MiniMax-M2.5 請求的成本。定價如下：

• 輸入：$0.3 / 1M tokens
• 輸出：$1.2 / 1M tokens
• Cache Read：$0.03 / 1M tokens
• Cache Write：$0.375 / 1M tokens

存取成本資訊

response = litellm.completion(
    model="minimax/MiniMax-M2.5",
    messages=[{"role": "user", "content": "Hello!"}],
    api_key="your-minimax-api-key"
)

# Access cost information
print(f"Cost: ${response._hidden_params.get('response_cost', 0)}")

串流支援

OpenAI API

response = litellm.completion(
    model="minimax/MiniMax-M2.5",
    messages=[{"role": "user", "content": "Tell me a story"}],
    stream=True,
    api_key="your-minimax-api-key",
    api_base="https://api.minimax.io/v1"
)

for chunk in response:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

搭配 Reasoning Split 的串流

stream = litellm.completion(
    model="minimax/MiniMax-M2.5",
    messages=[
        {"role": "user", "content": "Tell me a story"},
    ],
    extra_body={"reasoning_split": True},
    stream=True,
    api_key="your-minimax-api-key",
    api_base="https://api.minimax.io/v1"
)

reasoning_buffer = ""
text_buffer = ""

for chunk in stream:
    if hasattr(chunk.choices[0].delta, "reasoning_details") and chunk.choices[0].delta.reasoning_details:
        for detail in chunk.choices[0].delta.reasoning_details:
            if "text" in detail:
                reasoning_text = detail["text"]
                new_reasoning = reasoning_text[len(reasoning_buffer):]
                if new_reasoning:
                    print(new_reasoning, end="", flush=True)
                    reasoning_buffer = reasoning_text

    if chunk.choices[0].delta.content:
        content_text = chunk.choices[0].delta.content
        new_text = content_text[len(text_buffer):] if text_buffer else content_text
        if new_text:
            print(new_text, end="", flush=True)
            text_buffer = content_text

搭配原生 SDK 使用

透過 LiteLLM Proxy 使用 Anthropic SDK

import os
os.environ["ANTHROPIC_BASE_URL"] = "http://localhost:4000"
os.environ["ANTHROPIC_API_KEY"] = "sk-1234"  # Your LiteLLM proxy key

import anthropic

client = anthropic.Anthropic()

message = client.messages.create(
    model="minimax-m2-5",
    max_tokens=1000,
    system="You are a helpful assistant.",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "Hi, how are you?"
                }
            ]
        }
    ]
)

for block in message.content:
    if block.type == "thinking":
        print(f"Thinking:\n{block.thinking}\n")
    elif block.type == "text":
        print(f"Text:\n{block.text}\n")

透過 LiteLLM Proxy 使用 OpenAI SDK

import os
os.environ["OPENAI_BASE_URL"] = "http://localhost:4000"
os.environ["OPENAI_API_KEY"] = "sk-1234"  # Your LiteLLM proxy key

from openai import OpenAI

client = OpenAI()

response = client.chat.completions.create(
    model="minimax-m2-5",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hi, how are you?"},
    ],
    extra_body={"reasoning_split": True},
)

# Access thinking and response
if hasattr(response.choices[0].message, 'reasoning_details'):
    print(f"Thinking:\n{response.choices[0].message.reasoning_details[0]['text']}\n")
print(f"Text:\n{response.choices[0].message.content}\n")