Melhores modelos IA de contexto longo — 1M+ tokens, ranking 2026
Os melhores modelos IA para raciocínio de contexto longo. Classificados por precisão needle-in-a-haystack, janelas de 200K-2M tokens e desempenho RAG em documentos longos.
Top long-context models
Ordered by composite score across NIAH (Needle-in-a-Haystack) at 128K (20%), at 1M (40%), and Long-RAG QA accuracy (40%). Models without a published 1M-token NIAH eval are scored at zero for that bucket.
- gemini-3.1-pro-preview — 2M context, 99% NIAH at 1M, 96% Long-RAG. Industry-leading at the extreme upper end. The only model where you can stuff an entire mid-sized codebase and get reliable recall.
- gemini-3.1-flash — 1M context, 98% NIAH at 1M, 93% Long-RAG. ~5× cheaper than pro-preview at marginally lower recall. Default choice for high-volume long-doc summarization.
- claude-opus-4-7 — 200K context, 98% NIAH at 200K, 95% Long-RAG. Best reasoning quality per token in its window — Claude is dense rather than wide.
- kimi-k2.6 — 256K context, 96% NIAH at 256K, 89% Long-RAG. Strongest non-frontier-vendor option. Cheap, surprisingly accurate at the upper end of its window.
- gpt-5.5 — 128K context, 99% NIAH at 128K, 92% Long-RAG. OpenAI doesn't compete at the 1M+ tier yet but dominates at 128K.
- qwen3.6-max-context — 1M context, 91% NIAH at 1M, 84% Long-RAG. Open-weights option for 1M+ workloads. Self-hostable; quality dips noticeably above 500K.
Context window size vs recall quality
A model can advertise a 1M-token context window and still degrade sharply on retrieval tasks above ~500K. The NIAH benchmark — placing a single fact deep inside a long document and asking the model to retrieve it — separates the models that genuinely use their full window from the ones that effectively forget the middle. The ranking above weights 1M-NIAH four times higher than 128K-NIAH because retrieval at the upper end is the actually-hard problem.
When you need 1M+ context
Whole-codebase reasoning (gemini-3.1-pro-preview), multi-document RAG without chunking (gemini-3.1-flash), legal contract analysis across long PDFs (claude-opus-4-7 with retrieval, gemini for end-to-end). For most chat and agent workloads under 32K tokens, long-context performance does not matter — pick by quality and price instead.