> For clean Markdown of any page, append `.md` to the page URL.
> For a complete documentation index, see https://docs.sarvam.ai/llms.txt.
> For full documentation content in one file, see https://docs.sarvam.ai/llms-full.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.sarvam.ai/_mcp/server.

# Sarvam-M (Deprecated)

> Sarvam-M - deprecated 24B parameter multilingual, hybrid-reasoning language model with strong Indian language benchmark performance.

> **Warning**
>
> **Deprecated Model:** Sarvam-M has been deprecated and is no longer available through the Chat Completions API. Please migrate to [**Sarvam-105B**](/api/getting-started/models/sarvam-105b) for improved performance across all tasks. The information below is retained for reference only.

**Sarvam-M (Reasoning LLM)**

Multilingual, hybrid-reasoning, text-only model built on Mistral-Small.

Post-trained for superior reasoning and Indic language support.

**Performance Improvements:**

* **+20%** on Indian language benchmarks
* **+21.6%** on math benchmarks
* **+17.6%** on programming benchmarks
* **+86%** on romanized Indian language GSM-8K benchmarks

**Key Features:**

* **Hybrid Thinking Mode:** Switch between "think" (reasoning, coding, math) and "non-think" (fast conversations).
* **Advanced Indic Skills:** Authentically trained on Indian languages & cultural contexts.
* **Superior Reasoning:** Outperforms similar-sized models on coding & math.
* **Seamless Chat:** Works across Indic scripts & romanized text.

## Key Features

#### Strong Indian Language Support

Trained in 11 major Indic languages with support for native script, Romanised, and code-mixed inputs, tailored for everyday and formal Indian use cases.

#### Hybrid Reasoning Model

Supports both "think" and "non-think" modes, excelling in math, logic, and code-related tasks with special training for improved reasoning and direct answers.

#### Efficient and Fast Inference

Uses compression to make responses faster, works well even on lower-cost hardware setups, and can handle many users at once without slowing down.

#### Superior Performance

Outperforms leading models including Mistral 3 Small, Gemma 3, and Llama models across Indian language benchmarks.

#### Context-Aware Processing

Maintains context across long conversations with 8192 token context length and intelligent reasoning capabilities.

## Learn More

For detailed information on performance benchmarks and capabilities, visit [our blog](https://www.sarvam.ai/blogs/sarvam-m).

## Model Specifications

#### Key Considerations

* Maximum context length: 8192 tokens
* Temperature range: 0 to 2
  * Non-thinking mode: 0.2 (recommended)
  * Thinking mode: 0.5 (recommended)
* Top-p range: 0 to 1
* Reasoning effort options: low, medium, high
  * Setting any value enables thinking mode
  * Higher values increase reasoning depth

## Key Capabilities

> **Warning**
>
> **These examples no longer run.** The API rejects `model: "sarvam-m"` with:
>
> ```json
> {
>   "error": {
>     "message": "Model 'sarvam-m' has been deprecated. Please use one of the available models instead: sarvam-105b.",
>     "code": "invalid_request_error"
>   }
> }
> ```
>
> They are retained for historical reference only. For working equivalents of every example below, see [**Sarvam-105B**](/api/getting-started/models/sarvam-105b).

#### Basic Chat Completion

Simple, one-turn interaction where the user asks a question and the model replies with a single, direct response.

#### Python

```python
from sarvamai import SarvamAI

client = SarvamAI(
    api_subscription_key="YOUR_SARVAM_API_KEY",
)

response = client.chat.completions(
    model="sarvam-m",
    messages=[
{"role": "user", "content": "Why is India called a land of diverse landscapes?"}
    ],
    temperature=0.5,
    top_p=1,
    max_tokens=1000,
)

print(response)
```

#### JavaScript

```javascript
import { SarvamAIClient } from "sarvamai";

// Initialize the SarvamAI client with your API key
const client = new SarvamAIClient({
    apiSubscriptionKey: "YOUR_SARVAM_API_KEY",
});

async function main() {
    const response = await client.chat.completions({
        model: "sarvam-m",
        messages: [
            {
                role: "user",
                content: "Why is India called a land of diverse landscapes?",
            },
        ],
        temperature: 0.5,
        top_p: 1,
        max_tokens: 1000,
    });

    // Log the assistant's reply
    console.log(response.choices[0].message.content);
}

main();
```

#### cURL

```bash
curl -X POST https://api.sarvam.ai/v1/chat/completions \
  -H "api-subscription-key: $SARVAM_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      {"role": "user", "content": "Why is India called a land of diverse landscapes?"}
    ],
    "model": "sarvam-m",
    "temperature": 0.5,
    "top_p": 1,           
    "max_tokens": 1000
  }'
```

#### Multi-turn Conversation

Involves multiple exchanges between the system, user, and assistant, where context is maintained across all turns for coherent and relevant responses.

#### Python

```python
from sarvamai import SarvamAI

client = SarvamAI(
    api_subscription_key="YOUR_SARVAM_API_KEY",
)

response = client.chat.completions(
    model="sarvam-m",
    messages=[
{"role": "system", "content": "You are a travel expert specializing in Indian destinations."},
{"role": "user", "content": "Suggest a good place to visit in South India."},
{"role": "assistant", "content": "You can visit Munnar in Kerala. It's known for its tea plantations and cool climate."},
{"role": "user", "content": "What is the best time to visit Munnar?"}
    ],
    temperature=0.7,
    top_p=1,
    max_tokens=1000
)

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

#### JavaScript

```javascript
import { SarvamAIClient } from "sarvamai";

const client = new SarvamAIClient({
    apiSubscriptionKey: "YOUR_SARVAM_API_KEY"
});

async function main() {
    const response = await client.chat.completions({
        model: "sarvam-m",
        messages: [
            { role: "system", content: "You are a travel expert specializing in Indian destinations." },
            { role: "user", content: "Suggest a good place to visit in South India." },
            { role: "assistant", content: "You can visit Munnar in Kerala. It's known for its tea plantations and cool climate." },
            { role: "user", content: "What is the best time to visit Munnar?" }
        ],
        temperature: 0.7,
        top_p: 1,
        max_tokens: 1000
    });
    
    console.log(response.choices[0].message.content);
}

main();
```

#### cURL

```bash
curl -X POST https://api.sarvam.ai/v1/chat/completions \
  -H "api-subscription-key: $SARVAM_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      {"role": "system", "content": "You are a travel expert specializing in Indian destinations."},
      {"role": "user", "content": "Suggest a good place to visit in South India."},
      {"role": "assistant", "content": "You can visit Munnar in Kerala. It is known for its tea plantations and cool climate."},
      {"role": "user", "content": "What is the best time to visit Munnar?"}
    ],
    "model": "sarvam-m",
    "temperature": 0.7,
    "top_p": 1,           
    "max_tokens": 1000
  }'
```

## Next Steps

#### [Developer quickstart](/api/api-guides-tutorials/chat-completion/overview)

Learn how to integrate chat completion into your application.

#### [API Reference](/api-reference/chat/chat-completions-v1)

Complete API documentation for chat completion endpoints.

#### [Cookbook](/api/api-guides-tutorials/chat-completion/overview)

Step-by-step tutorial for chat completion implementation.

> Sarvam-M - deprecated 24B parameter multilingual, hybrid-reasoning language model with strong Indian language benchmark performance.