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Technical Analysis
Updated August 10, 20248 min read

LLM Context Window Comparison: Which API Handles Long Documents Best?

From 4K to 100M tokens, compare context window sizes across all major LLMs and find the best solution for your long document processing needs.

Context Window Sizes in 2025

Context window sizes have expanded dramatically in 2024-2025, with some models now supporting up to 100 million tokens[1]. This dramatic expansion enables entirely new use cases but comes with significant trade-offs in cost, speed, and reliability[2]:

ModelContext WindowTokens → Pages*Cost ImpactBest Use Case
Magic LTM-2-Mini100M tokens~75,000 pagesResearch prototypeFull codebase analysis
Llama 3.1 405B128K tokens~96 pagesHigh (self-host)Complex reasoning
Gemini 1.5 Pro1M tokens~750 pages+20-30% premiumResearch, books
GPT-4o128K tokens~96 pagesStandardComplex analysis
Claude 3.5 Sonnet200K tokens~150 pagesStandardLong conversations
GPT-4 Turbo128K tokens~96 pagesStandardTechnical docs
Mistral Large32K tokens~24 pagesBudgetStandard tasks
GPT-3.5 Turbo16K tokens~12 pagesBudgetChat, Q&A

*Approximate page count based on ~1,333 tokens per page[3]

Technical Limitations of Large Contexts

While larger context windows enable impressive capabilities, they come with significant technical challenges[2][4]:

Performance Degradation
  • • Attention dilution: Models lose focus on important details[2]
  • • Middle content neglect: Information in the middle gets ignored[4]
  • • Increased hallucination: More context = more confusion[2]
  • • Slower inference: 10-50x slower for million-token contexts[5]
Infrastructure Requirements

Memory and compute requirements for large contexts[5]:

  • • 100K tokens: 25-40GB VRAM (depending on model size)[5]
  • • 1M tokens: Hundreds of GB VRAM required[5]
  • • 10M+ tokens: Multi-GPU cluster required[5]
  • • 100M tokens: Specialized infrastructure (Magic LTM)[1]

Pricing Impact of Context Size

Larger context windows significantly impact costs, both directly through token pricing and indirectly through infrastructure requirements[6]:

Cost Scaling by Context Size
Monthly costs for processing 1,000 documents
Document Size16K Context128K Context1M ContextBest Strategy
10 pages$150$450$900Use smallest context
50 pages$750*$450$900Use 128K context
200 pages$3,000*$1,800*$900Use 1M or chunking
1000 pages$15,000*$9,000*$4,500*Use RAG instead

*Requires multiple API calls with overlap. Costs assume GPT-4 pricing[6]

Best Practices for Long Documents

Effectively handling long documents requires more than just throwing them at a large context window[2][6]. Here are proven strategies:

Retrieval-Augmented Generation (RAG)
Recommended for most cases

Instead of loading entire documents, use semantic search to retrieve only relevant sections[6]:

  • ✓ 90% cost reduction[6]
  • ✓ Better accuracy[6]
  • ✓ Faster responses[6]
  • ✓ Scales to any size[6]
Smart Chunking Strategies
For structured docs

Break documents intelligently:

  • • Semantic boundaries
  • • Section headers
  • • Sliding windows
  • • Hierarchical summaries

Use Cases by Context Size

Small Context (4-32K)
  • • Chat conversations[3]
  • • Email responses[3]
  • • Code snippets[3]
  • • Short articles[6]
  • • Q&A systems[6]

Best value for most applications

Medium Context (128-200K)
  • • Technical documentation[3][7]
  • • Research papers[2]
  • • Legal contracts[7]
  • • Code file analysis[4]
  • • Meeting transcripts[6]

Sweet spot for document tasks

Large Context (1M+)
  • • Entire codebases[1]
  • • Book analysis
  • • Legal discovery
  • • Medical records
  • • Enterprise knowledge

Specialized use cases only

Model Recommendations by Use Case

Use CaseRecommended ModelContext SizeWhy
Code Repository AnalysisMagic LTM-2-Mini100M tokensPurpose-built for code[1]
Book SummarizationGemini 1.5 Pro1M tokensBest quality/cost ratio[8]
Legal Document ReviewClaude 3.5200K tokensAccuracy + sufficient size[7]
Technical DocumentationGPT-4 Turbo128K tokensGood balance[3]
Chat ApplicationsGPT-3.5 Turbo16K tokensCost-effective[3]

Workarounds for Context Limitations

When your documents exceed available context windows, these strategies can help[6]:

  1. Hierarchical Summarization: Process documents in chunks, summarize each, then analyze summaries together
  2. Semantic Chunking: Split by meaning rather than arbitrary token counts
  3. Question-Specific Retrieval: Extract only sections relevant to the current query
  4. Progressive Refinement: Start with summaries, then drill down to details as needed
  5. External Memory Systems: Maintain context in vector databases for retrieval

Conclusion

While context windows have grown dramatically—from 4K tokens to 100M tokens[1]—bigger isn't always better. For most applications, 128K-200K tokens provide the sweet spot of capability and cost[6]. Mega-context models like Magic LTM-2-Mini serve specialized needs but come with significant trade-offs in speed, cost, and reliability[1][2].

The key is matching context size to your specific needs: use smaller contexts for chat and Q&A, medium contexts for document analysis, and reserve million-token contexts for truly massive documents where RAG isn't sufficient. In many cases, intelligent chunking and retrieval strategies outperform brute-force large context approaches in both cost and quality.

References

  1. [1] Magic AI. "LTM-2-Mini: 100M Token Context Windows" (2024)
  2. [2] Liu, Nelson F., et al. "Lost in the Middle: How Language Models Use Long Contexts" arXiv preprint (2023)
  3. [3] OpenAI. "Model Documentation - Context Windows" (2024)
  4. [4] Hugging Face. "Llama 3.1: 128K Context Length and More" (2024)
  5. [5] Deepset AI. "Long Context LLMs vs RAG: When to Use What" (2024)
  6. [6] IBM Research. "Understanding LLM Context Windows" (2024)
  7. [7] Anthropic. "Claude Model Specifications" (2024)
  8. [8] Google DeepMind. "Gemini Model Family Overview" (2024)
  9. [9] Meta AI. "Llama 3.2: Revolutionary AI Models" (2024)