Which AI model is best for summarizing long documents?

The best AI model for summarizing long documents depends on your specific needs, but there are clear frontrunners for different use cases. For most organizations requiring high accuracy and long-context handling, Claude Opus 4.5 (or Claude 3.5-Opus) is the top choice, offering a ~100k token context window, state-of-the-art factuality, and domain-agnostic performance. It excels in legal, scientific, and technical summaries with minimal prompt engineering.

If you need an open-source alternative, Qwen 3-30B-A3B-Instruct-2507 (or the newer 45B variant) is the best option, providing strong performance for self-hosted deployments with up to 96k tokens of context. For documents with multimodal content (e.g., PDFs with images or charts), GLM-4.5V is the top pick, as it handles both text and visual elements seamlessly.

For academic and research-focused summarization, Quillbot stands out with its adjustable summary length, grammar checking, and tone preservation, making it ideal for essays and papers. Meanwhile, a Scalable Transformer Architecture (by NAVER LABS Europe) is particularly effective for extremely long documents, leveraging advanced Transformer layers to outperform other models on ROUGE scores.

If budget is a constraint or you require on-premise deployment, Qwen 3 offers a cost-effective solution. For absolute maximum context length (up to 250k tokens), GPT-5 (beta) is the current leader, though it may require early access.

Practical considerations:

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  For Claude Opus 4.5, use a structured prompt like: text Summarize in ≤ 300 words, preserve technical terms/key conclusions, and use bullet points.

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  Chunk documents exceeding 100k tokens at natural breaks and summarize iteratively.

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  For open-source models, fine-tuning on domain-specific data can further enhance performance.