Claude Sonnet 4.6
Editor's pick: 1M context with excellent retrieval + prompt caching
Gemini 3.1 Pro is the best LLM for long documents (100k+ tokens) in April 2026, followed by Claude Sonnet 4.6 and Claude Opus 4.7. 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: 1M context with excellent retrieval + prompt caching
Editor's pick: 1M context when you need Opus-grade reasoning over the whole doc
Editor's pick: 2M context with reasoning mode; US-only
Top-tier benchmarks for this use case (90/100)
Expand any question for the full answer. Last reviewed 2026-04-19.
Gemini 3.1 Pro is the best LLM for long documents (100k+ tokens) in April 2026, followed by Claude Sonnet 4.6 and Claude Opus 4.7. 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 Flash is the cheapest credible option for long documents (100k+ tokens) at $0.50 / $3 per 1M, coming in at roughly $235.00/month at typical volume. Prompt caching brings the effective cost down another 80–90% on repeat prompts.
Yes — Gemini 3 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 4.6 is the top Anthropic pick, Gemini 3.1 Pro is the top Google pick. For long documents (100k+ tokens) workloads in April 2026, Gemini 3.1 Pro 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.