Frequently asked questions
Which LLM is best for summarization in 2026?
Right now Claude Fable 5, Gemini 2.5 Pro and Claude Opus 4.8 lead (graded on Arena Elo (Text), IFEval and MMLU-Pro plus an overall task grade). Best open-weight: Kimi K3. Best budget pick graded A or better: Qwen3.7 Flash ($0.03/$0.13 per 1M tokens).
What makes a model good at summarizing?
Three things: it follows the requested length and format, it does not add facts that are not in the source, and its context window is large enough to hold the whole document. Our grade combines instruction following, comprehension and human preference.
What is the best free LLM for summarization?
The top open-weight models for summarization right now are Kimi K3, DeepSeek V4.1 Flash and MiMo-V2.6-Flash. The cheapest model graded A or better is Qwen3.7 Flash ($0.03 in / $0.13 out per 1M tokens).
Should I summarize in one prompt or in chunks?
If the text fits in the context window, one prompt almost always gives a better summary. Summarizing chunks and then summarizing the summaries loses detail at every step; use it only when the document is too long to fit.
How do I stop a summary from inventing details?
Ask the model to quote the sentence that supports each point, and to write 'not stated' when the source is silent. Checking those quotes takes less time than re-reading the document.