LightweightMistral

Mistral Nemo

Mistral Nemo is Mistral's lightweight model optimized for efficient text processing with a 128K token context window.

Context 131K
Tier Lightweight
Input from
$0.020 / 1M tokens
across 3 providers

API Pricing

Cheapest on Deep Infra 78% below avg
ProviderInput / 1MOutput / 1MUpdated
$0.020$0.0404/4/2026
$0.020$0.0404/14/2026
$0.231$0.2314/7/2026

Prices updated daily. Last check: 4/14/2026

Model Details

General

Creator
Mistral
Family
Mistral
Tier
Lightweight
Context Window
131K
Modalities
Text

Capabilities

Tool Calling
No
Open Source
No

Strengths & Limitations

  • 128K token context window for processing lengthy documents
  • Lightweight architecture optimized for efficiency
  • Text generation and comprehension capabilities
  • Lower computational requirements than flagship models
  • Streaming response support for real-time applications
  • JSON mode for structured output generation
  • Multilingual text processing support
  • No tool calling or function execution capabilities
  • Text-only model with no image or multimodal support
  • Proprietary model with weights not publicly available
  • Smaller parameter count limits complex reasoning compared to flagship models
  • No fine-tuning availability through standard APIs

Key Features

128K token context window
Text generation and completion
JSON mode for structured output
Streaming responses
Multilingual text processing
Document analysis and summarization
Conversational chat interface
Batch processing support

About Mistral Nemo

Mistral Nemo is a lightweight model from Mistral, positioned as an efficient option within the company's model lineup. As a text-only model, it focuses on delivering capable language understanding and generation while maintaining computational efficiency compared to Mistral's larger flagship offerings. The model features a 128K token context window, enabling it to process lengthy documents and maintain context across extended conversations. Mistral Nemo handles standard text processing tasks including content generation, analysis, and question answering, though it does not include tool calling capabilities or multimodal support. Mistral Nemo serves applications where efficiency and cost-effectiveness are priorities over maximum capability. It provides a balance between performance and resource requirements, making it suitable for organizations that need reliable language model capabilities without the computational overhead of larger models.

Common Use Cases

Mistral Nemo is well-suited for applications requiring efficient text processing at scale, including content generation, document summarization, customer support automation, and text analysis workflows. Its lightweight architecture makes it appropriate for high-volume use cases where cost efficiency is important, such as content moderation, basic chatbots, text classification, and routine document processing. Organizations looking to implement language AI capabilities without the computational expense of larger models will find Mistral Nemo effective for standard text-based tasks that don't require advanced reasoning, tool use, or multimodal capabilities.

Frequently Asked Questions

How much does Mistral Nemo cost per million tokens?

Mistral Nemo pricing varies by provider and pricing type (standard vs batch). Check the pricing table above for current rates across all providers.

What is Mistral Nemo best used for?

Mistral Nemo excels at efficient text processing tasks including content generation, document analysis, summarization, and conversational applications where cost-effectiveness is important. Its 128K context window makes it suitable for processing lengthy documents while maintaining lower computational requirements than flagship models.

Does Mistral Nemo support tool calling and function execution?

No, Mistral Nemo does not include tool calling capabilities. It's a text-only model focused on language processing tasks. For applications requiring function calling or tool integration, consider Mistral's larger models that include these features.