Explore Mistral Models & APIs with APIpie Overview

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For detailed information about using models with APIpie, check out our Models Overview and Completions Guide.

Description

Mistral AI, a leading European AI company founded by former DeepMind and Meta AI researchers, develops powerful large language models known for their efficiency and performance. Their models, including the groundbreaking Mixtral-8x7B (which outperforms GPT-3.5)(one of the best 7B models), consistently achieve top rankings in open-source model benchmarks. The Mixtral architecture uses a Mixture of Experts approach, allowing it to achieve remarkable performance while maintaining computational efficiency. These models are available through various providers integrated with APIpie's routing system.

Key Features

• Extended Token Capacity: Models support context lengths from 4K to 256K tokens, with specialized variants like Codestral-Mamba (256K tokens) and Mistral-Nemo (128K tokens).
• Multi-Provider Availability: Accessible through OpenRouter, Together, Deepinfra, Bedrock, EdenAI.
• Diverse Applications: Optimized for chat, instruction-following, code generation, and embeddings, with specialized models for different tasks.
• Efficiency: High performance with optimized parameter counts and innovative architectures like Mixture of Experts and Mamba.

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Model List in the Mistral Series

Model List updates dynamically please see the Models Route for the up to date list of models

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For information about model performance and benchmarks, see the Mistral AI Documentation and Model Cards.

Model Name	Max Tokens	Response Tokens	Providers	Subtype
Mixtral Models
Mixtral-8x22B-Instruct-v0.1	65,536	65,536	Deepinfra, Together, OpenRouter	Chat
Mixtral-8x7B-Instruct-v0.1	32,768	32,768	Deepinfra, Together	Chat
Mixtral-8x7B-v0.1	32,768	32,768	Together	Language
mixtral-8x7b-instruct	32,768	32,768	OpenRouter	Chat
mixtral-8x7b	32,768	32,768	OpenRouter	Chat
mixtral-8x7b-instruct-v0	4,096	4,096	Bedrock	Chat
Mistral Models
Mistral-7B-Instruct-v0.1	4,096	4,096	Deepinfra, Together, OpenRouter	Chat
Mistral-7B-Instruct-v0.2	32,768	32,768	Deepinfra, Together, Monster	Chat
Mistral-7B-Instruct-v0.3	32,768	4,096	Together, OpenRouter	Chat
Mistral-7B-v0.1	4,096	4,096	Together	Language
mistral-7b-instruct	32,768	32,768	OpenRouter	Chat
mistral-7b-instruct-v0	8,192	8,192	Bedrock	Chat
mistral-large	128,000	128,000	OpenRouter	Chat
mistral-large	8,192	8,192	Bedrock	Chat
mistral-medium	32,000	32,000	OpenRouter	Chat
mistral-small	32,000	32,000	OpenRouter	Chat
mistral-tiny	32,000	32,000	OpenRouter	Chat
mistral-small-2402-v1	8,192	8,192	Bedrock	Chat
mistral-nemo	128,000	4,096	OpenRouter	Chat
Specialized Models
codestral-mamba	256,000	256,000	OpenRouter	Code
openhermes-2.5-mistral-7b	4,096	4,096	OpenRouter	Chat
pixtral-12b	4,096	4,096	OpenRouter	Chat
pixtral-large-2411	128,000	128,000	OpenRouter	Chat
ministral-8b	128,000	128,000	OpenRouter	Chat
ministral-3b	128,000	128,000	OpenRouter	Chat
Other Variants
1024__mistral-embed	-	-	EdenAI	Embedding
large-latest	-	-	EdenAI	Chat
tiny	-	-	EdenAI	Chat
small	-	-	EdenAI	Chat
ministral-3b-latest	-	-	EdenAI	Chat
ministral-8b-latest	-	-	EdenAI	Chat

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Example API Call

Below is an example of how to use the Chat Completions API to interact with a Mistral model:

curl -L -X POST 'https://apipie.ai/v1/chat/completions' \
-H 'Content-Type: application/json' \
-H 'Accept: application/json' \
-H 'Authorization: Bearer <YOUR_API_KEY>' \
--data-raw '{
  "provider": "openrouter",
  "model": "mixtral-8x7b-instruct",
  "max_tokens": 150,
  "messages": [
    {
      "role": "user",
      "content": "What are the key differences between traditional and quantum computing?"
    }
  ]
}'

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Response Example

{
  "id": "chatcmpl-12345example12345",
  "object": "chat.completion",
  "created": 1729535643,
  "provider": "openrouter",
  "model": "mixtral-8x7b-instruct",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Here are the key differences between traditional and quantum computing:\n\n1. **Information Processing**\n- Traditional: Uses bits (0 or 1)\n- Quantum: Uses qubits (can be 0, 1, or both simultaneously)\n\n2. **Computation Method**\n- Traditional: Sequential processing\n- Quantum: Parallel processing through superposition\n\n3. **Processing Power**\n- Traditional: Linear scaling\n- Quantum: Exponential scaling for certain tasks\n\n4. **Applications**\n- Traditional: General-purpose computing\n- Quantum: Specialized tasks like cryptography and complex simulations"
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 18,
    "completion_tokens": 132,
    "total_tokens": 150
  }
}

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API Highlights

• Provider Selection: Choose from multiple providers or use automatic routing
• Model Options: Select from various Mistral models based on your needs
• Token Management: Set appropriate token limits based on model capabilities
• Message Formatting: Structure your prompts effectively using message formatting

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Applications and Integrations

• Conversational AI: Build chatbots and virtual assistants.
• Code Generation: Leverage specialized models like Codestral-Mamba for programming tasks
• Enterprise Solutions: Deploy models for business applications
• Extended Context Processing: Handle long documents with models supporting up to 256K tokens
• Research Applications: Utilize open-weights models for academic research and development

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Ethical Considerations

Mistral models should be used responsibly with appropriate safeguards. For guidance on responsible AI usage, see Mistral's terms.

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Licensing

Mistral models are available under various licensing terms, including the Apache 2.0 License for open-source models. For detailed information, consult Mistral's documentation.

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Try out Mistral models in APIpie's supported integrations.