diff --git a/docs/my-website/blog/minimax_m2_5/index.md b/docs/my-website/blog/minimax_m2_5/index.md
new file mode 100644
index 00000000000..9259d1caad2
--- /dev/null
+++ b/docs/my-website/blog/minimax_m2_5/index.md
@@ -0,0 +1,393 @@
+---
+slug: minimax_m2_5
+title: "Day 0 Support: MiniMax-M2.5"
+date: 2026-02-12T10:00:00
+authors:
+  - name: Sameer Kankute
+    title: SWE @ LiteLLM (LLM Translation)
+    url: https://www.linkedin.com/in/sameer-kankute/
+    image_url: https://pbs.twimg.com/profile_images/2001352686994907136/ONgNuSk5_400x400.jpg
+  - name: Krrish Dholakia
+    title: "CEO, LiteLLM"
+    url: https://www.linkedin.com/in/krish-d/
+    image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg
+  - name: Ishaan Jaff
+    title: "CTO, LiteLLM"
+    url: https://www.linkedin.com/in/reffajnaahsi/
+    image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg
+description: "Day 0 support for MiniMax-M2.5 on LiteLLM"
+tags: [minimax, M2.5, llm]
+hide_table_of_contents: false
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+LiteLLM now supports MiniMax-M2.5 on Day 0. Use it across OpenAI-compatible and Anthropic-compatible APIs through the LiteLLM AI Gateway.
+
+## Supported Models
+
+LiteLLM supports the following MiniMax models:
+
+| Model | Description | Input Cost | Output Cost | Context Window |
+|-------|-------------|------------|-------------|----------------|
+| **MiniMax-M2.5** | Advanced reasoning, Agentic capabilities | $0.3/M tokens | $1.2/M tokens | 1M tokens |
+| **MiniMax-M2.5-lightning** | Faster and More Agile (~100 tps) | $0.3/M tokens | $2.4/M tokens | 1M tokens |
+
+## Features Supported
+
+- **Prompt Caching**: Reduce costs with cached prompts ($0.03/M tokens for cache read, $0.375/M tokens for cache write)
+- **Function Calling**: Built-in tool calling support
+- **Reasoning**: Advanced reasoning capabilities with thinking support
+- **System Messages**: Full system message support
+- **Cost Tracking**: Automatic cost calculation for all requests
+
+## Docker Image
+
+```bash
+docker pull litellm/litellm:v1.81.3-stable
+
+## Usage - OpenAI Compatible API (/v1/chat/completions)
+
+<Tabs>
+<TabItem value="proxy" label="LiteLLM Proxy">
+
+**1. Setup config.yaml**
+
+```yaml
+model_list:
+  - model_name: minimax-m2-5
+    litellm_params:
+      model: minimax/MiniMax-M2.5
+      api_key: os.environ/MINIMAX_API_KEY
+      api_base: https://api.minimax.io/v1
+```
+
+**2. Start the proxy**
+
+```bash
+docker run -d \
+  -p 4000:4000 \
+  -e MINIMAX_API_KEY=$MINIMAX_API_KEY \
+  -v $(pwd)/config.yaml:/app/config.yaml \
+  ghcr.io/berriai/litellm:v1.81.3-stable \
+  --config /app/config.yaml
+```
+
+**3. Test it!**
+
+```bash
+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"
+    }
+  ]
+}'
+```
+
+</TabItem>
+</Tabs>
+
+### With Reasoning Split
+
+```bash
+curl --location 'http://0.0.0.0:4000/chat/completions' \
+--header 'Content-Type: application/json' \
+--header 'Authorization: Bearer $LITELLM_KEY' \
+--data '{
+  "model": "minimax-m2-5",
+  "messages": [
+    {
+      "role": "user",
+      "content": "Solve: 2+2=?"
+    }
+  ],
+  "extra_body": {
+    "reasoning_split": true
+  }
+}'
+```
+
+## Usage - Anthropic Compatible API (/v1/messages)
+
+<Tabs>
+<TabItem value="proxy" label="LiteLLM Proxy">
+
+**1. Setup config.yaml**
+
+```yaml
+model_list:
+  - model_name: minimax-m2-5
+    litellm_params:
+      model: minimax/MiniMax-M2.5
+      api_key: os.environ/MINIMAX_API_KEY
+      api_base: https://api.minimax.io/anthropic/v1/messages
+```
+
+**2. Start the proxy**
+
+```bash
+docker run -d \
+  -p 4000:4000 \
+  -e MINIMAX_API_KEY=$MINIMAX_API_KEY \
+  -v $(pwd)/config.yaml:/app/config.yaml \
+  ghcr.io/berriai/litellm:v1.81.3-stable \
+  --config /app/config.yaml
+```
+
+**3. Test it!**
+
+```bash
+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"
+    }
+  ]
+}'
+```
+
+</TabItem>
+</Tabs>
+
+### With Thinking
+
+```bash
+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=?"
+    }
+  ]
+}'
+```
+
+## Usage - LiteLLM SDK
+
+### OpenAI-compatible API
+
+```python
+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-compatible API
+
+```python
+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)
+```
+
+### With Thinking
+
+```python
+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}")
+```
+
+### With Reasoning Split (OpenAI API)
+
+```python
+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}")
+```
+
+## Cost Tracking
+
+LiteLLM automatically tracks costs for MiniMax-M2.5 requests. The pricing is:
+
+- **Input**: $0.3 per 1M tokens
+- **Output**: $1.2 per 1M tokens
+- **Cache Read**: $0.03 per 1M tokens
+- **Cache Write**: $0.375 per 1M tokens
+
+### Accessing Cost Information
+
+```python
+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)}")
+```
+
+## Streaming Support
+
+### OpenAI API
+
+```python
+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="")
+```
+
+### Streaming with Reasoning Split
+
+```python
+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
+```
+
+## Using with Native SDKs
+
+### Anthropic SDK via LiteLLM Proxy
+
+```python
+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")
+```
+
+### OpenAI SDK via LiteLLM Proxy
+
+```python
+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")
+```
diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json
index f6edcf7efd0..7d0225af599 100644
--- a/litellm/model_prices_and_context_window_backup.json
+++ b/litellm/model_prices_and_context_window_backup.json
@@ -21432,6 +21432,36 @@
         "max_input_tokens": 1000000,
         "max_output_tokens": 8192
     },
+    "minimax/MiniMax-M2.5": {
+        "input_cost_per_token": 3e-07,
+        "output_cost_per_token": 1.2e-06,
+        "cache_read_input_token_cost": 3e-08,
+        "cache_creation_input_token_cost": 3.75e-07,
+        "litellm_provider": "minimax",
+        "mode": "chat",
+        "supports_function_calling": true,
+        "supports_tool_choice": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_system_messages": true,
+        "max_input_tokens": 1000000,
+        "max_output_tokens": 8192
+    },
+    "minimax/MiniMax-M2.5-lightning": {
+        "input_cost_per_token": 3e-07,
+        "output_cost_per_token": 2.4e-06,
+        "cache_read_input_token_cost": 3e-08,
+        "cache_creation_input_token_cost": 3.75e-07,
+        "litellm_provider": "minimax",
+        "mode": "chat",
+        "supports_function_calling": true,
+        "supports_tool_choice": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_system_messages": true,
+        "max_input_tokens": 1000000,
+        "max_output_tokens": 8192
+    },
     "minimax/MiniMax-M2": {
         "input_cost_per_token": 3e-07,
         "output_cost_per_token": 1.2e-06,
diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json
index f6edcf7efd0..7d0225af599 100644
--- a/model_prices_and_context_window.json
+++ b/model_prices_and_context_window.json
@@ -21432,6 +21432,36 @@
         "max_input_tokens": 1000000,
         "max_output_tokens": 8192
     },
+    "minimax/MiniMax-M2.5": {
+        "input_cost_per_token": 3e-07,
+        "output_cost_per_token": 1.2e-06,
+        "cache_read_input_token_cost": 3e-08,
+        "cache_creation_input_token_cost": 3.75e-07,
+        "litellm_provider": "minimax",
+        "mode": "chat",
+        "supports_function_calling": true,
+        "supports_tool_choice": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_system_messages": true,
+        "max_input_tokens": 1000000,
+        "max_output_tokens": 8192
+    },
+    "minimax/MiniMax-M2.5-lightning": {
+        "input_cost_per_token": 3e-07,
+        "output_cost_per_token": 2.4e-06,
+        "cache_read_input_token_cost": 3e-08,
+        "cache_creation_input_token_cost": 3.75e-07,
+        "litellm_provider": "minimax",
+        "mode": "chat",
+        "supports_function_calling": true,
+        "supports_tool_choice": true,
+        "supports_prompt_caching": true,
+        "supports_reasoning": true,
+        "supports_system_messages": true,
+        "max_input_tokens": 1000000,
+        "max_output_tokens": 8192
+    },
     "minimax/MiniMax-M2": {
         "input_cost_per_token": 3e-07,
         "output_cost_per_token": 1.2e-06,