LLMs with largest context windows
Read Time 16 mins | Written by: Cole
[Last updated: March 2026]
The largest LLMs today support context windows ranging from 400K to 1 million input tokens—enough to ingest entire codebases, hundreds of legal contracts, full video transcripts, or months of agent session history in a single pass. A million tokens is roughly 750,000 words, or the equivalent of 10–15 full-length novels processed at once.
That scale unlocks a new tier of practical use cases: coding across multi-repo projects without chunking, agentic workflows that hold full tool-call history in context, long-term memory for AI assistants, end-to-end analysis of legal or financial document sets, and multimodal pipelines combining text, images, and video in one request.
The engineering workarounds that used to define long-context work—RAG, sliding windows, lossy summarization—are increasingly optional (but still important).
LLMs with largest context windows:
- 100M tokens: Magic.dev’s LTM-2-Mini enables processing of enormous datasets—entire code repositories (up to 10 million lines of code) or large-scale document collections (750 novels). We still haven’t seen evidence anyone is using this model or its 100 million token context.
- 10M tokens: Meta’s Llama 4 Scout runs on a single GPU, perfect for on-device multimodal workflows, deep video/audio transcript analysis, and full-book summarization.
- 1M tokens: Claude Opus 4.6, Claude Sonnet 4.6, Google’s Gemini 3.1 Pro, Gemini 3 Flash, and Meta’s Llama 4 Maverick are the current frontier for complex multimodal tasks, enterprise-grade document analysis, and large-scale codebase comprehension.
Note: Claude Opus 4.6 and Sonnet 4.6 reached full GA of their 1M context window on March 13, 2026—at standard pricing with no long-context surcharge. A 900K-token request costs the same per-token rate as a 9K one. - 272K–1M tokens: OpenAI’s GPT-5.4 is OpenAI’s current flagship with a 272K standard context window, expandable to 1M in the API and Codex (2x pricing surcharge above 272K). First general-purpose OpenAI model with native computer use built in.
- 400K tokens: OpenAI’s GPT-5.4 mini and GPT-5.4 nano deliver extended context at lower cost, with a large 128K output window and strong long-context performance for agentic tasks.
- 256K tokens: Kimi K2.5 (Moonshot AI) brings strong agentic coding and visual capabilities—and is the foundation of Cursor’s Composer 2 proprietary model, built using RL fine-tuning on K2.5 weights.
- 256K tokens: Qwen 3.5 and Mistral Large 3 are strong open-weight alternatives. Qwen 3.5 is a 397B MoE model (Apache 2.0); Mistral Large 3 is a 675B MoE model (Apache 2.0) built for regulated industry deployments.
- 128K tokens: OpenAI’s GPT-4o, DeepSeek V3.2/R1, and Mistral Magistral balance efficiency and performance across 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.
Model details for LLMs with large context windows
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
We still haven’t seen evidence anyone outside of Magic.dev is using this model or its 100 million token context.
Input Context Window – Up to 10 million tokens
A 109B parameter MoE model with 17B active parameters and 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 while supporting native multimodality.
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
Input Context Window – 1 million tokens
As of March 13, 2026, Claude Opus 4.6 includes the full 1M context window at standard pricing—$5/$25 per million input/output tokens with no long-context surcharge. A 900K-token request costs the same per-token rate as a 9K one.
Full rate limits apply at every context length, and up to 600 images or PDF pages per request (6x increase from the prior limit). Opus 4.6 scores 78.3% on MRCR v2 at 1M tokens—the highest long-context recall rate among frontier models. Supports 128K max output tokens and is available on Claude Platform natively, Microsoft Foundry, Amazon Bedrock, and Google Cloud’s Vertex AI.
Primary Use Cases
- Large-scale codebase comprehension and multi-file refactoring without chunking
- Enterprise contract, legal, and research document analysis across hundreds of pages
- Long-running agentic workflows where full session history, tool calls, and intermediate reasoning stay intact
Input Context Window – 1 million tokens
Claude Sonnet 4.6 also received full 1M context on March 13, 2026, at standard pricing—$3/$15 per million input/output tokens. No beta header required; requests over 200K tokens work automatically. Supports 64K output tokens.
For most production workflows, it’s the default choice—delivering Opus-class performance on the majority of tasks at roughly 60% lower cost than Opus 4.6.
Primary Use Cases
- High-throughput document processing and synthesis
- Production developer applications requiring large codebase context
- Cost-efficient alternative to Opus 4.6 for long-context use cases at scale
Google Gemini 3.1 Pro, Gemini 3 Flash
Input Context Window – Up to 1 million tokens
Gemini 3 models support a 1 million token input context window and up to 64K tokens of output. Gemini 3.1 Pro, released February 19, 2026, maintains the full 1M context window while pushing output to 65K tokens—and achieves 77.1% on ARC-AGI-2, more than double the reasoning performance of the original Gemini 3 Pro.
Gemini 3 Flash offers the same 1M input context optimized for speed and high-volume workloads at $2/$12 per million tokens on input/output.
Primary Use Cases
- Complex multimodal workflows (video, audio, images, and text in one shot)
- Advanced coding assistants and autonomous agent pipelines
- Semantic search and enterprise document analysis at scale
Context Window – 272K standard / up to 1 million tokens (API and Codex)
Released March 5, 2026, GPT-5.4 is OpenAI’s current flagship model, available across ChatGPT, the API, and Codex. It supports a 1M token context window, with native computer use built in for the first time in a general-purpose OpenAI model.
Unlike Claude’s flat-rate 1M pricing, prompts exceeding 272K input tokens are priced at 2x input and 1.5x output for the full session—a meaningful cost difference for production workloads at scale.
Primary Use Cases
- Agentic workflows requiring native desktop control and computer use
- Large-scale code generation and enterprise multi-step automation
- Token-efficient workloads (up to 47% fewer tokens vs. GPT-5.2 on tool-heavy tasks)
Input Context Window – Up to 1 million tokens
A 400B parameter MoE model with 17B active parameters and 128 experts, Maverick delivers flagship-level performance for enterprise applications while maintaining cost efficiency.
Primary Use Cases
- Enterprise-grade multimodal applications
- Advanced image and text understanding across 12 languages
- High-performance chat and assistant applications
OpenAI GPT-5.4 mini, GPT-5.4 nano
Input Context Window – Up to 400,000 tokens
GPT-5.4 mini and GPT-5.4 nano are OpenAI’s compact models in the GPT-5.4 family, built for subagent roles in hierarchical AI systems. Both support a 400K context window with a 128K output window.
GPT-5.4 mini ($0.75/M input) nearly matches flagship performance on coding and computer use; GPT-5.4 nano ($0.20/M input) is API-only, optimized for classification, data extraction, and simple coding subtasks at the lowest cost in the GPT-5 lineup.
Primary Use Cases
- Advanced reasoning and problem-solving tasks
- Large-scale code generation and refactoring
- Multi-step agentic workflows requiring extended output generation
Input Context Window – Up to 256,000 tokens
Kimi K2.5 provides a 256K context window with strong reasoning capabilities, supporting multi-step tool invocation across complex coding, mathematical, and agentic tasks. Released January 27, 2026, it’s a 1 trillion parameter open-weight MoE model.
Notably, Cursor shipped Composer 2 on March 19 as its “most capable proprietary coding model,” but within 24 hours a developer discovered the internal model ID—kimi-k2p5-rl-0317-s515-fast—revealing it as Kimi K2.5 with reinforcement learning fine-tuning applied on top. Moonshot confirmed the tokenizer similarity and flagged potential licensing concerns.
The base model is available via Moonshot’s API and major providers including OpenRouter, at $0.60/M input tokens.
Primary Use Cases
- Frontend development from visual designs and screenshots (generates UI code from images/video)
- Large codebase comprehension and multi-file refactoring
- Agentic workflows using Agent Swarm parallel sub-agent execution
Input Context Window – Up to 256,000 tokens
Qwen 3.5 is Alibaba’s 397B total / 17B active parameter MoE model, released under Apache 2.0, supporting 201 languages. It supports 256K tokens natively and delivers competitive benchmark performance at 10–17x lower cost than Claude or GPT for comparable tasks ($0.40/M input).
Primary Use Cases
- Complex reasoning, coding, and instruction-following tasks
- Multilingual and multi-domain production workflows
- Cost-efficient self-hosted deployments on enterprise or consumer hardware
Input Context Window – Up to 256,000 tokens
Mistral’s current flagship, Mistral Large 3, is a 675B total / 41B active parameter sparse MoE model under Apache 2.0.
It delivers state-of-the-art performance with flexible enterprise deployment—on-premises, hybrid, and in-VPC—and is a top choice for teams with EU data residency requirements. Devstral 2, its coding-focused variant, powers the Mistral Vibe 2.0 terminal coding agent.
Primary Use Cases
- Professional coding and STEM workflows requiring high accuracy
- On-premise and hybrid deployments for regulated industries
- Multimodal understanding in enterprise settings with custom fine-tuning
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
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. Coding and codebase analysis: Query, refactor, and audit entire code repositories faster.
Large context windows let developers load entire multi-repo codebases, documentation, and test files simultaneously—no chunking, no lost cross-file dependencies.
This is where the jump from 200K to 1M tokens has the most immediate practical impact. For a deeper look at which models developers use most for coding, see our roundup of the best LLMs for coding.
Examples
- Code completion and generation with full repository context, preserving style and architecture
- Refactoring and documentation across entire software systems in one session
- Security audits by reviewing an entire codebase for vulnerabilities at once
- Legacy code modernization: loading an entire COBOL system into context so Claude Code can map dependencies, flag risks, and generate modern equivalents without losing the thread across thousands of files
2. Agentic AI workflows: Run long-horizon agents that hold full task history in context.
Agentic systems make dozens or hundreds of tool calls per task—searching databases, reading files, executing code, verifying outputs. With a 1M context window, the entire trace stays intact: every tool call, observation, and intermediate reasoning step.
That eliminates the compaction and context-clearing that used to cause agents to lose the plot mid-task.
Examples
- Autonomous coding agents that plan, execute, and verify complex multi-file tasks without restarting
- Research agents synthesizing hundreds of papers, datasets, and codebases in a single pass
- AI project managers tracking decisions, dependencies, and progress across long engagements
3. Comprehensive document analysis: Analyze entire books, contracts, or research corpora without splitting.
Models retain full context throughout, making summarization, Q&A, and insight extraction more accurate than sliding-window approaches. Legal, financial, and research workflows benefit most.
Examples
- Financial reports analyzed end-to-end for trends and anomalies
- Legal contracts reviewed for risks, clauses, and inconsistencies across hundreds of pages
- Research literature synthesized across dozens of papers in a single request
4. Multimodal data processing: Handle large datasets combining text, images, and video.
Models like Gemini 3.1 Pro and Claude Opus 4.6 process text, images, video, and audio natively within the same context window—making them practical for multimedia workflows that previously required multiple separate pipelines.
Examples
- Medical imaging analyzed alongside patient history and clinical notes
- Video transcript analysis cross-referenced with supporting documentation for tagging or summarization
- Visual coding generating front-end interfaces directly from design screenshots or screen recordings
5. Enterprise knowledge management: Build systems that retrieve and reason across large internal document sets.
Large context windows reduce dependence on RAG pipelines for many use cases—entire knowledge bases, policy libraries, or case archives can be loaded directly, with the model reasoning across all of it at once.
Examples
- Corporate knowledge assistants referencing multiple internal documents to answer complex questions
- Legal research tools scanning thousands of cases for relevant precedents
- Healthcare AI consulting vast medical literature to support diagnosis or treatment decisions
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 to build with 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.
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Cole
Cole is Codingscape's Content Marketing Strategist & Copywriter.