Ministral 8B

Ministral 8B is a state-of-the-art AI model developed by Mistral AI. It is designed for edge computing, making it efficient for local applications. With a maximum context length of 128,000 tokens, it excels in processing extensive information. This model is cost-effective, priced at $0.10 per million tokens. It supports function calling and features interleaved sliding-window attention for faster inference. Ideal for tasks like translation and smart assistants, it prioritizes privacy and speed.

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-8b",
    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-8b",
  messages=[
    {
      "role": "user",
      "content": "Write a blog about cat."
    }
  ]
)
print(completion.choices[0].message.content)

Ministral 8B

ContextMinistral

Input$0.1 / M

Output$0.1 / M

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Description

The Ministral 8B is an advanced AI model from Mistral AI. It was released in October 2024. This model is built for efficient edge computing. It has 8 billion parameters, which helps it excel in tasks like knowledge retrieval and reasoning. 

One key feature is its support for a maximum context length of 128,000 tokens. This makes it stand out among many other models. It can manage large amounts of information well, making it perfect for complex tasks. It has been compared to competitors, such as Google’s Gemma 2 2B and Meta’s Llama 3.1 8B. 

In benchmark tests, the Ministral 8B scored 65.0 in knowledge and commonsense reasoning. Llama 3.1 scored slightly lower at 64.7. The model also supports function calling, allowing it to work with external APIs easily. 

Moreover, it features an interleaved sliding-window attention pattern. This design helps with faster processing. It produces high-quality outputs while using less memory. It has outperformed the Mistral 7B in almost every category. 

In real-world use, the Ministral 8B can perform tasks like on-device translation and local analytics. It is designed for environments with strict privacy needs. Developers find it a valuable tool for their projects. 

For those wanting to add this powerful model to their systems, using our AIAPILAB services will give you discounted rates.

Model API Use Case

Ministral 8B is a small AI model that works well in edge computing. It can handle a large amount of data with a context length of 128,000 tokens. This model is perfect for tasks like on-device translation and smart assistants. For example, it helps robots understand commands quickly without needing the internet.

In tests, Ministral 8B performs better than other models. It received a score of 65.0 on MMLU, beating models like Llama 3.1 and Gemma 2.2B. The version tuned for instructions is free for personal use. For businesses, it costs $0.10 for every million tokens used.

Developers can use its ability to call functions. This allows them to get real-time data like weather updates. This feature makes apps more responsive for users.

Using Ministral 8B helps businesses keep data private and lower delays. Its small size means it works well on everyday devices. This opens up access to advanced AI for more people. For more details, check out [Unitalk](https://unitalk.ai/discover/model/ministral-8b-latest).

Model Review

Pros

1. Ministral 8B handles large contexts, processing 128,000 tokens effortlessly. 2. It surpasses competitors in benchmarks, showcasing superior knowledge and reasoning abilities. 3. The model integrates function calling, enabling smooth interaction with external APIs. 4. Its sliding-window attention boosts processing speed while saving memory efficiently. 5. Optimized for edge computing, it ensures privacy and security for sensitive applications.

Cons

1. The model requires 24 GB of GPU memory for single-device use, limiting accessibility. 2. It struggles with nuanced tasks, sometimes producing irrelevant or nonsensical outputs. 3. The need for a commercial license restricts local deployment options for developers.

Comparison

Feature/Aspect	Mistral 7B	Llama 3.1 8B	Ministral 8B
Model Parameters	7 billion	8 billion	8 billion
Special Features	Standard attention mechanism	Standard attention mechanism	Interleaved sliding-window attention
Maximum Context Length	32,000 tokens	32,000 tokens	128,000 tokens
Function Calling Support	Limited	Limited	Yes
Performance in Knowledge	Lower performance than 8B models	Comparable performance	Outperforms Llama 3.1 and Mistral 7B

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-8b",
    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-8b",
  messages=[
    {
      "role": "user",
      "content": "Write a blog about cat."
    }
  ]
)
print(completion.choices[0].message.content)

FAQ

Q1: What is the maximum context length for the Ministral 8B model?  
A1: The Ministral 8B model supports a maximum context length of 128,000 tokens.  

Q2: How can I implement function calling with the Ministral 8B model?  
A2: The model natively supports function calling for real-time API interactions.  

Q3: What types of tasks can the Ministral 8B model perform?  
A3: It excels in language understanding, reasoning, and multilingual tasks.  

Q4: How can I access the Ministral 8B model via API?  
A4: You can access it through Mistral’s APIs for various applications.  

Q5: What are the primary advantages of using Ministral 8B?  
A5: It offers efficiency, low latency, and privacy-focused on-device inference.