Ministral 3B

Ministral 3B is a cutting-edge AI model developed by Mistral AI. It features 3 billion parameters and is optimized for edge computing. With a maximum context length of 128,000 tokens, it excels in knowledge and reasoning tasks. This model is designed for on-device applications, ensuring low latency and high efficiency. It outperforms many larger models in various benchmarks.

import OpenAI from "openai"

const openai = new OpenAI({
  baseURL: "https://api.aiapilab.com/v1",
  apiKey: $AIAPILAB_API_KEY
})

async function main() {
  const completion = await openai.chat.completions.create({
    model: "mistralai/ministral-3b",
    messages: [
      {
        "role": "user",
        "content": "Write a blog about cat."
      }
    ]
  })

  console.log(completion.choices[0].message)
}
main()

from openai import OpenAI

client = OpenAI(
  base_url="https://api.aiapilab.com/v1",
  api_key="$AIAPILAB_API_KEY",
)

completion = client.chat.completions.create(
  model="mistralai/ministral-3b",
  messages=[
    {
      "role": "user",
      "content": "Write a blog about cat."
    }
  ]
)
print(completion.choices[0].message.content)

Ministral 3B

ContextMinistral

Input$0.04 / M

Output$0.04 / M

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Description

Ministral 3B is an advanced AI model. It was released by Mistral AI in late 2024. This model is known for its efficiency on edge devices. It has 3 billion parameters and supports a context length of up to 128,000 tokens. This means it can handle large amounts of information quickly. For example, it can process a 50-page document in no time.

Ministral 3B excels in several areas. It performs well in knowledge retrieval, common sense reasoning, and function calling. In various benchmarks, it has outperformed competitors like Google’s Gemma 2 and Meta's Llama 3.2. For instance, in multi-task language understanding, it scored 60.9 while its rivals scored 52.4 and 56.2.

This model is designed for low-latency applications. It is perfect for tasks like translation and analytics on devices. The model allows real-time interactions, making it ideal for smart assistants and robotics. Additionally, it has strong coding capabilities and can generate code snippets quickly.

In internal tests conducted by Mistral, this model outperformed its predecessor, Mistral 7B. The ability to call functions allows it to connect with external APIs. This feature enhances its usefulness in many areas, from customer support to data processing.

Overall, Ministral 3B marks a big step forward for small-scale AI models. Its blend of performance and efficiency is great for developers. To get the best experience and competitive pricing, consider integrating this model through AIAPILAB services.

Model API Use Case

Ministral 3B API is made for quick on-device and edge computing. It works well in situations that need fast responses. This makes it perfect for real-time tasks. The API can manage a context length of up to 128,000 tokens. It is capable of handling large amounts of data, which helps with complex processes.

One way to use it is in smart home devices. The API quickly processes commands for lights, thermostats, and security systems. For example, a smart assistant can answer voice commands like "Set the living room light to 70% brightness" in less than a second.

Another use is in mobile translation apps. It provides instant translations for people traveling. A user can type "How do I get to the nearest restaurant?" in English, and it will give the Spanish translation almost right away.

Ministral 3B also allows function calling. This means it can connect with other APIs to fetch real-time data, like weather updates. This feature makes the user experience better by providing timely details.

In summary, the Ministral 3B API is a strong choice for developers who want to use AI in privacy-focused and resource-saving applications. For more details, check out [Mistral AI](https://mistral.ai).

Model Review

Pros

1. Efficient Processing: Handles large contexts quickly, processing up to 128,000 tokens seamlessly. 2. Superior Performance: Outshines competitors like Gemma 2 and Llama 3.2 in multiple benchmarks. 3. Real-Time Interactions: Supports low-latency applications, perfect for smart assistants and robotics. 4. Versatile Function Calling: Integrates smoothly with external APIs, enhancing its usability. 5. Advanced Reasoning: Excels in knowledge retrieval and common sense reasoning tasks effectively.

Cons

1. The model may generate repetitive responses, causing frustration for users. 2. It struggles with nuanced conversations, limiting its effectiveness in complex dialogues. 3. The reliance on external APIs can create latency issues, affecting real-time performance.

Comparison

Feature/Aspect	Gemma 2 2B	Llama 3.2 3B	Ministral 3B
Parameters	2 billion	3 billion	3 billion
Ideal Use Cases	Suitable for simpler tasks but less efficient than Ministral 3B	General natural language tasks	Optimized for edge computing and on-device applications
Maximum Context Length	128,000 tokens	128,000 tokens	128,000 tokens
Performance Benchmarks	Lower scores in multi-task evaluations compared to Ministral 3B	Strong in multilingual tasks, but slightly behind in some benchmarks	Outperforms Llama 3.2 3B and Gemma 2 2B on various tasks
Function Calling Support	No native function calling support	Limited function calling capabilities	Yes, supports native function calling

API

import OpenAI from "openai"

const openai = new OpenAI({
  baseURL: "https://api.aiapilab.com/v1",
  apiKey: $AIAPILAB_API_KEY
})

async function main() {
  const completion = await openai.chat.completions.create({
    model: "mistralai/ministral-3b",
    messages: [
      {
        "role": "user",
        "content": "Write a blog about cat."
      }
    ]
  })

  console.log(completion.choices[0].message)
}
main()

from openai import OpenAI

client = OpenAI(
  base_url="https://api.aiapilab.com/v1",
  api_key="$AIAPILAB_API_KEY",
)

completion = client.chat.completions.create(
  model="mistralai/ministral-3b",
  messages=[
    {
      "role": "user",
      "content": "Write a blog about cat."
    }
  ]
)
print(completion.choices[0].message.content)

FAQ

Q1：What is the context length for Ministral 3B?  
A1：Ministral 3B supports a context length of 128,000 tokens.

Q2：How does Ministral 3B handle function calling?  
A2：Ministral 3B natively supports function calling for API interactions.

Q3：What tasks is Ministral 3B optimized for?  
A3：Ministral 3B excels in knowledge retrieval and common-sense reasoning.

Q4：Can I use Ministral 3B for on-device applications?  
A4：Yes, Ministral 3B is designed for edge computing and on-device tasks.

Q5：What languages does Ministral 3B support?  
A5：Ministral 3B primarily supports English and several other languages.