Use case
Best long context LLM
Long-read guideChoose a model that maintains quality over very long documents, codebases, or conversation histories without context degradation.
Best long context LLM
Choose a model that maintains quality over very long documents, codebases, or conversation histories without context degradation.
Why this guide works
- Raw context window size doesn't equal practical effective context
- Test recall accuracy at your actual working length
- Consider cost per token at different context depths
Shortlist
These models have the largest context windows with proven quality retention at scale.
Meta
Llama 4 Scout
Llama
Meta's Llama 4 Scout (17Bx16E MoE, 109B total params) with an extraordinary 10M token context window.
- Context
- 10,485,760
- Input
- N/A
- Output
- N/A
- Action
- Compare-ready
Google DeepMind
Gemini 1.5 Pro
Gemini
Google's Gemini 1.5 Pro with 2M context for long document and media analysis.
- Context
- 2,097,152
- Input
- $0.0013/1K tok
- Output
- $0.005/1K tok
- Action
- Compare-ready
Google DeepMind
Gemini 3.1 Pro
Gemini 3.1
Google's Gemini 3.1 Pro, designed for complex tasks where simple answers aren't enough. Released Feb 2026 with enhanced reasoning and multimodal capabilities.
- Context
- 1,048,576
- Input
- $0.0013/1K tok
- Output
- $0.01/1K tok
- Action
- Compare-ready
Anthropic
Claude Sonnet 4.6
Claude 4.6
Anthropic's current Sonnet tier for fast frontier reasoning, coding, and long-context agent work.
- Context
- 1,000,000
- Input
- $0.003/1K tok
- Output
- $0.02/1K tok
- Action
- Compare-ready
Decision table
Choose based on your actual working context length and quality retention needs.
| Need | Why it fits | Model |
|---|---|---|
| Extreme context (10M tokens) | Best when you need to process entire codebases or document collections in a single prompt. | Llama 4 ScoutMeta |
| Ultra-long context (2M tokens) | Best when you need proven quality at very long context with multimodal support. | Gemini 1.5 ProGoogle DeepMind |
| High-quality 1M context | Best when you need top-tier reasoning quality with 1M token context for complex analysis. | Gemini 3.1 ProGoogle DeepMind |
| Balanced long context | Best when you need strong reasoning with 1M context and enterprise-grade safety. | Claude Sonnet 4.6Anthropic |
Evaluation framework
Long context quality degrades differently across models. Test at your actual working length.
Test recall at target length
Place key information at different positions in the context and test if the model recalls it accurately.
Measure quality degradation curve
Score output quality at 10K, 100K, 500K, and 1M tokens to find where each model drops off.
Calculate cost at working length
Long context models charge per token. Calculate total cost for your average document length.
Check retrieval vs. long context tradeoff
Sometimes RAG with a shorter context model beats a long context model. Compare both approaches.
Common scenarios
Long context needs vary by application pattern.
Legal document analysis
Use a model with proven quality at 200K+ tokens for analyzing contracts, depositions, and legal filings.
Codebase analysis
Use a model that can hold entire repositories in context for comprehensive code review and refactoring.
Research synthesis
Use a model that can process multiple papers or reports in a single session for cross-document analysis.
Methodology
This guide prioritizes effective context quality over raw window size.
We test recall accuracy at multiple positions in the context window.
We measure quality degradation curves rather than just listing context sizes.
We compare long context vs. RAG approaches for each scenario.
Next step
Pick the long context model for your documents
Compare models on effective context quality, cost at scale, and integration fit.