Mistral Large 2411

Mistral-Large-Instruct-2411 is a powerful language model developed by Mistral AI. With 123 billion parameters, it excels in reasoning, coding, and knowledge tasks. This model offers improved long context and function calling capabilities. It is ideal for complex workflows and applications needing accurate instruction following. Users can access it through Vertex AI for various AI tasks.

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/mistral-large-2411",
    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/mistral-large-2411",
  messages=[
    {
      "role": "user",
      "content": "Write a blog about cat."
    }
  ]
)
print(completion.choices[0].message.content)

Mistral Large 2411

ContextIt

Input$2 / M

Output$6 / M

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Description

Mistral AI is the provider of the Large-Instruct-2411 API. It launched this model in October 2023. The model has 123 billion parameters. This makes it a very advanced large language model. It is strong in reasoning, knowledge, and coding.

The Large-Instruct-2411 improves on the previous version. It has better long context understanding. This allows it to handle complex tasks more effectively. It can follow precise instructions and manage JSON outputs well. It is especially useful for tasks that need large context, like retrieval-augmented generation and code generation.

The API is designed for many different uses. It can efficiently manage complex workflows. It also supports multiple languages, including English, French, and German. Additionally, it can generate code in over 80 programming languages, such as Python, Java, and JavaScript.

Mistral Large-Instruct-2411 performs well in benchmarks. It scored 81.2% in common reasoning tests. This makes it one of the top models available. The model can also produce structured responses that meet high standards for compliance and security.

Using the Mistral Large-Instruct-2411 API lets developers create smart agents. The model can be customized for specific needs. This makes it useful for a wide range of applications. For the best results, consider integrating this powerful model into your projects. Use our AIAPILAB services to get better prices and maximize your benefits with Mistral Large-Instruct-2411.

Model API Use Case

Mistral Large 2411 is a strong API with 123 billion parameters. It is built for tough tasks like code generation and reasoning. Many businesses can use this model to work better and boost productivity.

One way to use it is in customer support. By using Mistral Large 2411, companies can manage up to 200,000 tokens each minute. This means they can quickly reply to customer questions. For example, a retail store could use this API to answer common questions, handle orders, and give tailored suggestions.

Another use is in software development. Mistral Large 2411 can create code snippets in more than 80 programming languages. This includes popular ones like Python and Java. Such ability can greatly speed up development. Teams can then focus on important tasks instead of doing the same coding over and over.

Additionally, the API can produce JSON outputs. This makes it easy to connect with other systems. With its good pricing, Mistral Large 2411 is a cost-effective choice. It is cheaper than options like GPT-4, which costs more for similar work. This makes it a great option for businesses wanting to improve their AI use.

Learn more about its features and uses at [Mistral AI](https://cloud.google.com/vertex-ai/generative-ai/docs/partner-models/mistral).

Model Review

Pros

1. Enhances reasoning: The model excels in complex reasoning tasks, providing accurate insights. 2. Supports multiple languages: It efficiently manages tasks in various languages, boosting accessibility. 3. Improves context handling: The model effectively understands long contexts, enhancing information retrieval. 4. Facilitates code generation: It generates and reviews code in over 80 programming languages, aiding developers. 5. Creates intelligent agents: The API empowers developers to craft smart agents for diverse applications.

Cons

1. The model struggles with prompt format changes. Users face issues adapting to the new structure. 2. Some users report inconsistent output quality. This inconsistency can hinder workflow efficiency. 3. The API lacks comprehensive documentation. Developers may find it challenging to troubleshoot issues.

Comparison

Feature/Aspect	Qwen 32B Preview	Mistral Large 2407	Mistral Large 2411
Model Size	32 billion parameters	123 billion parameters	123 billion parameters
Code Generation	Excellent performance in coding tasks	Good at code generation tasks	Proficient in generating and understanding code in 80+ languages
Prompt Processing	Standard prompt processing	Standard prompt processing	Enhanced prompt processing architecture
Multilingual Support	Strong multilingual capabilities	Similar multilingual capabilities	Supports multiple languages including English, French, Spanish, German, Italian
Reasoning Capabilities	Good reasoning but less advanced than 2411	Top-tier reasoning capabilities	Advanced reasoning and mathematical skills

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/mistral-large-2411",
    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/mistral-large-2411",
  messages=[
    {
      "role": "user",
      "content": "Write a blog about cat."
    }
  ]
)
print(completion.choices[0].message.content)

FAQ

Q1: What tasks can Mistral Large 2411 handle?  
A1: Mistral Large 2411 excels in reasoning, knowledge, and coding tasks.

Q2: How do I access the Mistral Large 2411 API?  
A2: Utilize the API endpoint to send requests for model interactions.

Q3: Can I fine-tune Mistral Large 2411 for specific needs?  
A3: Yes, fine-tuning is possible with your unique data and domain knowledge.

Q4: What languages does Mistral Large 2411 support?  
A4: It supports multiple languages, including English, French, and Spanish.

Q5: How does Mistral Large 2411 perform with long context?  
A5: It efficiently manages long context, enhancing retrieval-augmented generation.