Gemini 3.1 Pro
Editor's pick: 2M context window — the largest from any frontier lab
Claude Sonnet 5 is the best LLM for long documents (100k+ tokens) in April 2026, followed by Gemini 3.1 Pro and Claude Opus 4.8. Rankings reflect real benchmarks, pricing, and compliance for a typical long documents (100k+ tokens) workload; see the breakdown below or take the quiz for a pick tailored to your volume and constraints. Last verified 2026-04-19.
Editor's pick: 2M context window — the largest from any frontier lab
Editor's pick: 1M context when you need Opus-grade reasoning over the whole doc
Editor's pick: 1M context with reasoning mode; US-only
Top-tier benchmarks for this use case (93/100)
Expand any question for the full answer. Last reviewed 2026-04-19.
Claude Sonnet 5 is the best LLM for long documents (100k+ tokens) in April 2026, followed by Gemini 3.1 Pro and Claude Opus 4.8. The ranking is based on benchmarks relevant to long documents (100k+ tokens) — instruction following, reasoning, tool use where applicable — combined with cost at a typical production volume and caching behavior. All picks are verified against arena.ai/leaderboard and the provider's published pricing as of 2026-04-19.
Gemini 3.5 Flash is the cheapest credible option for long documents (100k+ tokens) at $1.5 / $9 per 1M, coming in at roughly $579.00/month at typical volume. Prompt caching brings the effective cost down another 80–90% on repeat prompts.
Yes — Gemini 3.5 Flash offers a free tier usable for prototyping long documents (100k+ tokens) workloads. Free tiers have rate limits and daily quotas, so they're fine for validation but not production. See the model pages for exact quotas.
Claude Sonnet 5 is the top Anthropic pick, Gemini 3.1 Pro is the top Google pick. For long documents (100k+ tokens) workloads in April 2026, Claude Sonnet 5 ranks first overall in our picker. The gap between top picks is small — you should pick primarily on API ergonomics, deployment region, and caching behavior rather than raw benchmark score.
Rankings combine (1) benchmark scores weighted by what matters for long documents (100k+ tokens) — for example coding benchmarks dominate for coding, long-context retrieval dominates for RAG and long documents, (2) cost at a typical production volume, (3) speed and latency tier, (4) ergonomics like prompt caching and structured output, (5) recency of release, and (6) a curated editorial boost for provider-specific strengths that generic benchmarks miss (e.g. Gemini's advantage on maps and geospatial tasks). Every rank shows its exact score breakdown on the quiz result page.