LLMs with largest context windows

[Last updated: Mar. 2025]

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 writing and analyzes code bases, 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:

• 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 (up to 10 million lines of code) or large-scale document collections (equivalent to 750 novels).
• Google's Gemini 2.0 Flash sets the bar high with its 1 million token context window, ideal for intricate multimodal tasks like combining text, images, audio, and video in complex workflows.
• Anthropic's Claude 3.7 Sonnet and Claude 3.5 Sonnet both offer robust 200,000 token windows, making them well-suited for extended, long-form content processing and sophisticated workflows.
• OpenAI's o3-mini and o1 both provide 200,000 token windows, optimized for reasoning tasks and complex problem-solving.
• OpenAI's GPT-4.5, o1-mini, GPT-4o, Mistral Large 2, Meta Llama 3.2, and DeepSeek R1 deliver flexible solutions with 128,000 token windows, balancing efficiency and performance across diverse tasks including large-scale document analysis, code generation, and multilingual processing.

Let’s get into the details.

LLMs with large context windows

Magic.dev LTM-2-Mini

Input 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, ideal for developers working with complex projects.

Google Gemini 2.0 Flash

Input Context Window – Up to 1 million tokens

Google’s Gemini 2.0 Flash features a substantial 1 million token context window, ideal for managing massive volumes of data in a single interaction. It excels in multimodal tasks, efficiently processing text, images, audio, and video, enabling diverse and sophisticated applications.

Primary use cases – Complex research papers, multimodal data interpretation, detailed content analysis, and extensive video transcriptions.
Multimodal capabilities – Processes text, images, audio, and video, offering robust support for diverse applications.

Anthropic Claude 3.7 Sonnet

Input Context Window – Up to 200,000 tokens

Claude 3.7 Sonnet maintains the 200,000 token context window of its predecessor, providing robust support for extensive documents, nuanced conversations, and coding tasks, making it ideal for detailed document analyses, conversational AI, and software development.

Primary use cases – Advanced customer support, complex workflow automation, detailed text processing, and sophisticated coding tasks. 

Performance – Further optimized performance for real-time and resource-intensive applications, particularly effective in coding and software development scenarios.

Anthropic Claude 3.5 Sonnet

Input 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, ideal for applications requiring understanding of long-form text over extended conversations or tasks. It features faster processing speeds and handles multimodal tasks efficiently.

Primary use cases – Customer support, multi-step workflows, in-depth document processing, and software development. 

Performance – Twice the speed of Claude 3 Opus, highly suitable for real-time applications.

OpenAI GPT-4.5

Input Context Window – Up to 128,000 tokens

GPT-4.5 is an advancement within OpenAI’s GPT series, offering a 128,000 token context window designed for sophisticated reasoning, comprehensive analysis, and seamless integration into real-time applications.

Primary use cases – Complex reasoning tasks, advanced content generation, enterprise-scale document management.
Performance – Balances efficiency with powerful reasoning capabilities for both consumer and enterprise applications.

OpenAI o3-mini

Input Context Window – Up to 200,000 tokens

OpenAI's o3-mini, released on January 31, 2025, is a compact yet powerful language model designed to enhance reasoning capabilities while maintaining efficiency. It offers three levels of reasoning effort – low, medium, and high – allowing users to balance response time and depth of analysis.

Primary use cases – Mathematical problem-solving, coding assistance, scientific analysis, and tasks requiring logical reasoning.​

Performance – o3-mini demonstrates faster response times and reduced computational requirements compared to its predecessors, making it suitable for both simple and complex queries. It matches or surpasses the performance of earlier models like o1 in various coding and reasoning tasks. ​

OpenAI o1

Input Context Window – Up to 200,000 tokens

OpenAI’s o1 model offers a robust 200,000 token context window, optimized for high-performance tasks with a strong balance of speed and reasoning capabilities over large datasets.

Primary use cases – Large-scale document analysis, complex coding tasks, and intensive customer interaction management.

Performance – Excels in complex reasoning tasks, competitive programming, and advanced problem-solving scenarios.

OpenAI o1-mini

Input Context Window – Up to 128,000 tokens

OpenAI’s o1-mini is a lighter, more efficient version designed for faster processing, suitable for edge devices or smaller applications with limited resources. It retains a substantial 128,000 token context window, ideal for handling large inputs.

Primary use cases – Resource-constrained environments, quick-turnaround coding tasks, and efficient customer interaction management.

Performance – Optimized for speed and efficiency, providing robust performance even on limited hardware.

OpenAI GPT-4o

Input Context Window – Up to 128,000 tokens

OpenAI’s GPT-4o boasts a 128,000 token context window, highly effective for handling long, complex documents, generating code, and performing document-based retrieval tasks. It maintains coherence and relevance across extended inputs, though challenges in reasoning can occasionally arise.

Primary use cases – Document summarization, long-form content generation, and code analysis.
Efficiency – Improved speed and cost-efficiency, accessible for diverse use cases.

Mistral Large 2

Input 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 effectively for handling complex, large-scale datasets across languages.

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

Meta Llama 3.2

Input Context Window – Up to 128,000 tokens

Meta’s Llama 3.2 models offer a 128,000 token context window, making it competitive for handling extensive datasets and supporting diverse multimodal and text-based tasks.

Primary use cases – Document analysis, creative AI tasks, multimodal applications.
Versions – Available in larger enterprise-focused models and lighter versions optimized for edge computing.

DeepSeek R1

Input Context Window – Up to 128,000 tokens​

DeepSeek's R1 model, released in January 2025, is designed for tasks requiring advanced reasoning, such as mathematical problem-solving and coding. It employs reinforcement learning techniques to enhance its reasoning capabilities without supervised fine-tuning.

Primary use cases – Mathematical reasoning, coding assistance, and complex logical inference.​

Performance – Recognized for its efficiency, DeepSeek R1 matches or surpasses the performance of models like OpenAI o1 in specific benchmarks, all while utilizing less computational power and resources.

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.

Claude 3.7 Sonnet is the current favorite for writing reliable code. 

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.