Top picks for Long-Context Q&A (2026)
Answering questions over 100K+ token docs. Ranked from 340 live models on the OpenRouter catalog, weighted for context window, reasoning quality.
| # | Model | Score | In / 1M | Out / 1M | Context | |
|---|---|---|---|---|---|---|
| 1 | Anthropic: Claude Opus 4.8anthropic/claude-opus-4.8 | 163 | $5.00 | $25.00 | 1,000,000 | Details → |
| 2 | Anthropic: Claude Sonnet 4.6anthropic/claude-sonnet-4.6 | 161 | $3.00 | $15.00 | 1,000,000 | Details → |
| 3 | OpenAI: GPT-5openai/gpt-5 | 161 | $1.25 | $10.00 | 400,000 | Details → |
| 4 | Anthropic: Claude Opus 4.7anthropic/claude-opus-4.7 | 159 | $5.00 | $25.00 | 1,000,000 | Details → |
| 5 | OpenAI: o3openai/o3 | 145 | $2.00 | $8.00 | 200,000 | Details → |
| 6 | Google: Gemini 2.5 Progoogle/gemini-2.5-pro | 141 | $1.25 | $10.00 | 1,048,576 | Details → |
| 7 | OpenAI: GPT-4.1openai/gpt-4.1 | 140 | $2.00 | $8.00 | 1,047,576 | Details → |
| 8 | Google: Gemini 2.5 Flashgoogle/gemini-2.5-flash | 138 | $0.30 | $2.50 | 1,048,576 | Details → |
| 9 | Anthropic: Claude Sonnet 4anthropic/claude-sonnet-4 | 135 | $3.00 | $15.00 | 1,000,000 | Details → |
| 10 | Qwen: Qwen3.7 Plusqwen/qwen3.7-plus | 132 | $0.40 | $1.60 | 1,000,000 | Details → |
| 11 | MiniMax: MiniMax M3minimax/minimax-m3 | 132 | $0.30 | $1.20 | 1,048,576 | Details → |
| 12 | Google: Gemini 3.5 Flashgoogle/gemini-3.5-flash | 132 | $1.50 | $9.00 | 1,048,576 | Details → |
| 13 | Google: Gemini 3.1 Flash Litegoogle/gemini-3.1-flash-lite | 132 | $0.25 | $1.50 | 1,048,576 | Details → |
| 14 | xAI: Grok 4.3x-ai/grok-4.3 | 132 | $1.25 | $2.50 | 1,000,000 | Details → |
| 15 | OpenAI GPT Mini Latest~openai/gpt-mini-latest | 132 | $0.75 | $4.50 | 400,000 | Details → |
How we ranked these
For Long-Context Q&A, we weight models on context window, reasoning quality. Scores combine each model's public specs with independent benchmark results (Aider Polyglot coding scores, Artificial Analysis intelligence/coding/agentic indices) and live pricing. See full methodology →
About Long-Context Q&A
Long-context Q&A is the task of retrieving answers from documents exceeding 100,000 tokens, typically 50+ pages of dense text. You need this when search-based retrieval fails or when the answer depends on synthesizing information spread across an entire document. Good models maintain coherence and accuracy across the full context window without degrading performance at document end (the "lost in the middle" problem); poor ones either hallucinate, miss relevant sections, or fail to synthesize across distant passages. The primary trade-off is latency: processing 100K tokens costs 3-10x more compute time and API cost than typical 4K-token queries, so batch processing during off-peak hours or using cached context windows can reduce expenses significantly.
When to use: Use this when you need to answer questions about entire contracts, research papers, regulatory filings, or codebases that are too long for standard retrieval-augmented search, and when the answer requires understanding relationships between sections far apart in the document.
Common questions
What is the difference between long-context Q&A and retrieval-augmented generation (RAG)?
RAG splits documents into chunks and retrieves only relevant snippets before answering, keeping context windows small and costs low. Long-context Q&A feeds the entire document into the model at once, enabling answers that depend on synthesizing distant sections or understanding document structure. Use RAG for speed and cost; use long-context Q&A when retrieval might miss critical context or when documents are under 150K tokens and speed is less critical.
How much does it cost to run a 100K token query compared to a standard 4K prompt?
Claude 3.5 Sonnet and GPT-4 charge per token, so a 100K input runs approximately 25x the cost of a typical 2K-token query. With prompt caching (supported by Claude and GPT-4), you pay full price on first use but only 10% on subsequent queries with identical context, making it economical for repeated questions over the same document.