Best LLM for Long Context (2026)
Long-context models can ingest entire codebases, legal documents, research papers, or conversation histories in a single API call. Context window size matters — but so does retrieval quality (how well the model uses information from the middle of a long context) and price per token at scale.
Top picks for long context
1M-token context window — ingest entire repositories, legal case files, or multi-book corpora in one call. Strong retrieval quality across the full context length.
1M-token context at $0.15/1M input — the cheapest per-token rate for million-token context. Ideal for high-volume document processing pipelines.
1M-token context with strong instruction following. Reliable for large codebase Q&A, multi-file refactoring, and repository-level code review.
200K context with exceptional instruction following and nuanced comprehension. Best for legal documents, research synthesis, and tasks requiring careful reading of dense text.
Model comparison — long context
0 models| # | Model | Provider | Tier | Intelligence | MMLU | Input/1M | Context | Confidence |
|---|
Which model for which task?
1M-token context lets you ingest an entire codebase. Gemini 2.5 Pro has the largest window; GPT-4.1 has stronger instruction following for complex refactoring tasks.
Legal tasks require careful reading and nuanced comprehension. Claude Opus 4.5 excels at instruction following; Gemini 2.5 Pro handles larger document sets.
$0.15/1M input with 1M-token context. For pipelines processing thousands of long documents, the cost difference vs. frontier models is 5–10×.
Both handle 200K–1M token conversation histories. Claude Sonnet 4.5 has better context retention in very long conversations; GPT-4.1 is faster.