LLMs with largest context windows

[Last updated: Jun. 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 analyzing codebases, 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 context 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).
• Meta’s Llama 4 Scout extends ultra-long context capabilities with a 10 million token context window on a single GPU, perfect for on-device multimodal workflows, deep video/audio transcript analysis, and full-book summarization.
• OpenAI’s GPT-4.1, Google’s Gemini 2.5 Flash & 2.5 Pro, and Meta’s Llama 4 Maverick all offer massive 1 million token context windows, ideal for complex multimodal tasks, enterprise-grade document analysis, “Deep Think” hypothesis generation, and large-scale codebase comprehension.
• Anthropic’s Claude 4 (Opus 4 & Sonnet 4), Anthropic’s Claude 3.7 Sonnet & 3.5 Sonnet, and OpenAI’s o3 & o4 models provide robust 200k token context windows, optimized for high-precision multi-step workflows, deep research, and cost-efficient reasoning with full toolchain support.
• OpenAI’s GPT-4o, Mistral Large 2, and DeepSeek R1 & V3 deliver flexible solutions with 128k token context windows, balancing efficiency and performance across tasks including vision-language understanding, advanced summarization, code generation, and resource-efficient on-device deployments.

Let’s take a closer look at what these models can do with their large context windows. 

Use cases for 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

• Ultra-long codebase comprehension and refactoring
• Legal-contract and policy analysis spanning thousands of pages
• Full-book summarization and knowledge extraction

Meta Llama 4 Scout

Input Context Window – Up to 10 million tokens

A 17 B-parameter MoE (109 B total) model with 16 experts, Scout delivers an unprecedented 10 million-token window on a single NVIDIA H100 GPU. It outperforms competitors like Google’s Gemma 3 and Mistral 3.1 across benchmarks.

Primary Use Cases

• On-device multimodal workflows requiring ultra-long context
• Large-scale codebase comprehension and automated refactoring
• Full-book summarization and deep video/audio transcript analysis

OpenAI GPT-4.1

Input Context Window – Up to 1 million tokens

The API-only flagship in the GPT-4 family, available in Standard, Mini, and Nano variants. It delivers ~21% better coding performance than GPT-4o, reduces extraneous edits from 9%→2%, and costs ~26% less per token.

Primary Use Cases

• Large-scale codebase refactoring and generation
• End-to-end enterprise document analysis (full books, technical manuals)
• Multi-pass reasoning over extensive datasets (legal discovery, scientific literature)

Input Context Window – Up to 1 million tokens

At I/O 2025, Google refined reasoning, multimodal throughput, and code performance in the 2.5 line. Both Flash and Pro share the 1 million-token window; Pro adds “Deep Think,” letting it consider multiple hypotheses before answering.

Primary Use Cases

• Complex multimodal workflows (video, audio, text in one shot)
• Advanced coding assistants and in-browser AI agents
• Semantic search across billion-token corpora

Input Context Window – Up to 200,000 tokens

Opus 4 is optimized for frontier intelligence (complex agents, deep research), while Sonnet 4 prioritizes speed and cost. Key innovations include parallel tool execution, beta “extended thinking” with tool use, and enhanced memory via file-based context.

Primary Use Cases

• High-precision, multi-step coding tasks
• Constitutional-AI driven document analysis and safe multi-turn dialogues
• Agentic workflows requiring tool-chaining and long-term context tracking

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
• Sophisticated coding tasks

OpenAI o3 & o4-mini 

Input Context Window – Up to 200,000 tokens (100 K output)

The first reasoning-focused models with full ChatGPT tool access (web search, Python, file analysis, image). o3 is the flagship with SOTA on coding, math, vision; o4 prioritizes speed/cost, excelling on AIME 2025 with 99.5% pass@1 via Python.

Primary Use Cases

• Complex multi-step reasoning and agentic tasks
• Large codebases and data analysis requiring tool use
• Vision-language workflows
  
  

Mistral Medium 3

Input Context Window – Up to 128,000 tokens

Mistral Medium 3 delivers state-of-the-art performance at 8× lower cost with radically simplified enterprise deployments. It matches or exceeds benchmarks like Claude 3.7 Sonnet across coding and multimodal tasks at just $0.40 per million input tokens and $2 per million output tokens. It supports hybrid, on-premises, and in-VPC setups, plus custom post-training for deep integration into enterprise systems.
Primary Use Cases

• Professional coding and STEM workflows requiring high accuracy
• Multimodal understanding (text, code, images) in enterprise settings
• On-premise and hybrid deployments with continuous fine-tuning

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

• Vision-language assistants (charts, diagrams)
• Extended code and text analysis
• Multimodal enterprise agents

DeepSeek R1 & V3 

Input Context Window – Up to 128,000 tokens​

DeepSeek R1 and V3 both leverage a Mixture-of-Experts architecture to deliver exceptional chain-of-thought reasoning across multi-step workflows. R1’s 671 B-parameter MoE (37 B activated per token) was trained via multi-stage reinforcement learning to excel on math and coding benchmarks, while V3 builds on that foundation with smarter tool-use capabilities, enhanced reasoning pathways, and optimized inference, all available as open-source under the MIT license. 

Primary Use Cases

• Extended document summarization and deep Q&A over long texts
• Multi-step mathematical problem solving and chain-of-thought reasoning
• Complex code generation, debugging, and automated refactoring
• Efficient on-device inference in resource-constrained environments
• Agentic workflows with integrated external tool use

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.