LLMs with largest context windows

[Last updated: Oct. 2024]

One of the biggest advancements in LLMs is the expansion of large context windows – the amount of information an LLM can process at one time. Large context windows allow users to tackle increasingly complex and high-volume tasks across various industries. 

Whether it's processing vast legal documents, managing customer interactions, or analyzing entire video transcripts, the ability to maintain context over thousands – or even millions – of tokens opens up new possibilities for AI-driven applications.

LLMs with largest context windows:

• Google’s Gemini 1.5 sets the bar high with its 2 million token context window, ideal for intricate multimodal tasks like combining text, images, and video in complex workflows.
• Claude 3.5 Sonnet offers a robust 200,000 token window, making it well-suited for extended, long-form content processing and sophisticated workflows.
• OpenAI o1-preview, o1-mini, and GPT-4o provide 128,000 token windows, making them highly competitive for large-scale document analysis, code generation, and multilingual processing.
• Mistral Large 2 and Llama 3.2 deliver flexible solutions for handling complex datasets with their 128,000 token windows – balancing efficiency and performance across diverse tasks.
• Magic.dev’s LTM-2-Mini pushes the boundaries with a staggering 100 million token window – enabling processing of enormous datasets like entire code repositories or large-scale document collections.

Let’s get into the details.

LLMs with large context windows

Magic.dev LTM-2-Mini

Context Window – Up to 100 million tokens

Magic.dev's LTM-2-Mini boasts an extraordinary 100 million token (10 million lines of code or 750 novels) context window, making it the largest context window available. This model is built for handling massive datasets, like entire codebases or vast collections of documents.

• Primary use cases – Large-scale code analysis, AI agents with long-term memory, and extensive document synthesis.
• Efficiency – Optimized for processing huge amounts of context while maintaining performance, making it ideal for developers working with complex projects.

Google Gemini 1.5

Context Window – Up to 2 million tokens

Google’s Gemini 1.5 model leads the market with its 2 million token context window, perfect for use cases that require managing massive volumes of data in a single interaction. It excels in multimodal tasks, making it ideal for processing text, images, and potentially video in the future.

• Primary use cases – Complex research papers, multimodal data interpretation, and long video transcriptions.
• Multimodal capabilities – Can process both text and images, opening up possibilities for more diverse applications.

Anthropic Claude 3.5 Sonnet

Context Window – Up to 200,000 tokens

Building on the foundation of Claude 3, Claude 3.5 Sonnet retains its 200,000 token context window, making it ideal for applications that require understanding of long-form text over extended conversations or tasks. It features faster processing speeds than previous models and handles multimodal tasks efficiently.

• Primary use cases – Customer support, multi-step workflows, and in-depth document processing.
• Performance – Twice the speed of Claude 3 Opus, making it highly suitable for real-time applications

OpenAI o1-preview and o1-mini

Context Window – Up to 128,000 tokens

OpenAI’s o1-preview and o1-mini models offer a robust 128,000 token context window, ideal for handling large inputs across a variety of applications. While both models share the same context window, they differ in performance and use cases.

• o1-preview – Optimized for high-performance tasks, offering a balance of speed and reasoning capabilities over large datasets.
• o1-mini – A lighter, more efficient version for faster processing, suitable for edge devices or smaller applications with limited resources.
• Primary use cases – Large-scale document analysis, customer interaction management, and complex coding tasks, with o1-mini excelling in more resource-constrained environments.

OpenAI GPT-4o 

Context Window – Up to 128,000 tokens

OpenAI’s GPT-4o is a major upgrade, boasting a 128,000 token context window that makes it highly effective for handling long and complex documents, generating code, and performing document-based retrieval tasks. The model is designed to maintain coherence and relevance across longer inputs, though challenges with reasoning in extended contexts can sometimes arise.

• Primary use cases – Document summarization, long-form content generation, and code analysis.
• Efficiency – Improved speed and cost-efficiency, making it accessible for a wide range of use cases​
  

Mistral Large 2

Context Window – Up to 128,000 tokens

Mistral Large 2 is a highly capable model with a 128,000 token context window, similar to GPT-4o. It excels in multilingual processing and advanced reasoning tasks, positioning it as a competitor in handling complex, large-scale datasets across different languages.

• Primary use cases – Multilingual data processing, code generation, and large-scale document analysis.
• Performance – Offers strong multilingual and reasoning capabilities, comparable to GPT-4o.

Meta Llama 3.2

Context Window – Up to 128,000 tokens

Meta’s Llama 3.2 models come with a 128,000 token context window. This makes it a strong competitor for handling large datasets and supporting diverse tasks including multimodal and text-based projects.

• Primary use cases – Document analysis, creative AI tasks, and multimodal applications.
• Versions – Offers both larger models for enterprise use and lighter versions optimized for edge devices​

What business cases are large context windows best for? 

1. Comprehensive document analysis – Analyzing entire books, research papers, or large legal documents.

Benefit – LLMs with large context windows can read and analyze long documents without needing to split them into smaller sections. This makes summarization, question-answering, and extracting insights more accurate since the model retains the full context throughout.

Examples

• Analyzing financial reports for insights.
• Reviewing lengthy legal contracts for potential issues or clauses.
• Summarizing large research papers or technical manuals.

2. Codebase and software analysis – Understand and analyze entire code repositories or software documentation.

Benefit – Models like Magic.dev’s LTM-2-Mini (up to 10 million lines of code at once) allow developers to query vast codebases, identify bugs, and even generate code that interacts seamlessly with existing systems.

Examples

• Code completion or generation tasks with full repository context.
• Documenting or refactoring entire software systems across multiple files.
• Conducting security audits by reviewing the entire codebase.

3. Multimodal data processing – Handle large datasets across different formats, like text, images, and videos.

Benefit – With massive context windows, LLMs can process datasets that combine text and visuals, making them suitable for multimedia content generation and large-scale analysis.

Examples

• Analyzing medical images with patient history for diagnosis.
• Cross-referencing visual data with text descriptions for environmental studies.
• Handling complex video transcripts for summarization or tagging.

4. Long-term memory for AI agents – Create AI agents with memory capabilities that persist across interactions and even sessions.

Benefit – With extended context windows, AI agents can maintain memory over vast sequences of interactions, making them more effective in tasks requiring continuity and historical reference.

Examples

• Customer support systems that recall previous interactions for personalized responses.
• Virtual assistants that retain context over long periods for improved task management.
• AI-driven project managers who track tasks, deadlines, and decisions.

5. Scientific and technical research – Process and synthesize extensive research datasets – including academic papers and experimental data.

Benefit – With large context windows, LLMs can process multiple research papers or datasets simultaneously, providing better synthesis, hypothesis generation, and predictive modeling.

Examples

• Reviewing academic papers for meta-analyses.
• Generating insights from large-scale scientific experiments or datasets.
• Developing predictive models by analyzing historical data.

6. Large-scale conversational AI – Engage in extended multi-turn conversations that span a significant amount of context.

Benefit – Customer service bots, personal assistants, or teaching tools can maintain the full conversation context across hundreds of messages or interactions.

Examples

• Personal AI tutors that remember previous questions and responses.
• Customer service chatbots handling long, detailed conversations without restarting.
• AI-driven project managers keeping track of tasks and previous decisions.

7. Large-scale information retrieval and knowledge management – Creating systems that access and retrieve vast amounts of information from large document sets.

Benefit: Long context windows enable advanced knowledge management systems, where the model can pull relevant information from large corpora, like corporate databases or legal repositories.

Examples

• Corporate knowledge assistants that answer questions by referencing multiple internal documents.
• Healthcare AI systems consulting vast medical literature to assist in diagnosis or treatment.
• Legal research assistants scanning thousands of cases for relevant insights or precedents.

8. Video analysis – Process, understand, and summarize long video content.

Benefit – Large context windows allow models to analyze entire video transcripts, tracking themes, extracting key moments, and cross-referencing content across various parts of a video. This is useful for media companies, education platforms, and legal entities dealing with video content.

Examples

• Summarizing video interviews, podcasts, or documentaries.
• Automatically generating insights from online course videos or lectures.
• Reviewing long video testimonies or security footage for key events.
• Tagging video content with metadata for easier search and retrieval.

Do long context windows cost more? 

In short, yes. Long context windows enable advanced capabilities but do come at a higher computational and financial cost due to increased memory, slower processing, and more resource-heavy inference. 

But, they don’t have to mean wasted money. When you have the right use case figured out and optimize your LLMs in production, you can control costs. 

Here are the cost challenges you need to consider:

• Increased memory usage
  Longer sequences mean more memory consumption. As the number of tokens in the context window increases, the model must store and process more information, which results in greater memory requirements.
  
  This leads to higher GPU/TPU memory usage during inference and training.
  
  
• Slower processing times
  Processing longer inputs takes more time. Large context windows require the model to attend to more tokens, increasing the computational complexity. 
  
  Transformer models, like those used in GPT and similar architectures, use an attention mechanism where the complexity grows quadratically with the number of tokens. As the number of tokens increases, it significantly slows down the processing speed.
  
  
• More expensive inference
  Inference costs scale with input length. Models with larger context windows require more operations per token to maintain context over long inputs, resulting in higher compute costs for running predictions or generating outputs. 
  
  Cloud services, like OpenAI or Anthropic, usually charge based on the number of tokens processed, so longer contexts increase costs directly.
  
  
• Higher energy and resource usage
  More compute resources are needed for extended context handling. Handling longer sequences requires more powerful hardware to avoid bottlenecks.
  
  Training and inference over large contexts might require higher-end GPUs, leading to higher operational costs, especially in large-scale deployments.
  
  
• Optimization challenges
  Models with larger context windows require more sophisticated optimization. Managing long sequences without performance degradation is a challenge. 
  
  Techniques like LongRoPE and other position encoding methods are used to improve efficiency, but these often come at an extra computational cost.

How to manage costs for large context LLMs?

• Use adaptive context windows: Instead of always using the maximum context window, some systems adapt the window size to the input length, reducing costs when smaller contexts suffice.
• Pruning or focusing attention: Techniques like sparse attention can help reduce the computational load by limiting attention to the most relevant tokens.
• Batching inputs: Combining shorter inputs in batches can help minimize resource use when long context windows aren’t required.

How do I hire senior AI engineers for large context window LLMs?

You could spend the next 6-18 months planning to recruit and build an AI team, but you won’t be building any AI capabilities. That’s why Codingscape exists. 

We can assemble a senior AI development team for you in 4-6 weeks and start building your AI apps with large context LLMs. It’ll be faster to get started, more cost-efficient than internal hiring, and we’ll deliver high-quality results quickly.

Zappos, Twilio, and Veho are just a few companies that trust us to build their software and systems with a remote-first approach.

You can schedule a time to talk with us here. No hassle, no expectations, just answers.