Kimi K3.1 Pro can read a million tokens. How do you test it on real documents?
A collected summary says Kimi K3.1 Pro supports a context of one million tokens. When testing long documents, keep the expected answers, source references and document versions so you can check both retrieval and understanding.
What to consider
Start with long-document tasks whose answers you already know, then decide whether to expand your trial. A collected summary says Kimi K3.1 Pro supports a context of one million tokens and mentions plans to release the full model weights later. That capacity does not tell you whether it will understand all of your material. Choose a document with references across sections, version differences and clear answers. Ask for answers with source references, then record omissions and misreadings. This article is based on the collected summary; we have not tested the model or checked the progress of the weight release.
Start with real questions that require reading across sections
A useful starting task has an answer spread across several sections that a person can still check. For example, list the requirement changes in a project document and identify the record behind each change. Save the correct references before asking the model to process the same material, so there is a clear basis for comparison.
If a question can be answered from one passage, keep your current approach as a comparison. Sending more material is not itself a successful result. Check whether the model missed anything that required comparing sections and whether its conclusions can be traced to the document.
Record missed information and misinterpretations separately
Ask for a section, page number or short reference to the source text for each key conclusion. Record missing information, misinterpretations and references that do not support the conclusion as separate issues. That will help you decide whether to change the question, organize the document differently or compare another approach.
When old and new versions appear in the same material, state which version to use and how to handle conflicts. Keep those rules fixed while checking whether the model follows them. Changing the standard after seeing different answers makes it hard to tell whether the model or the question caused the difference.
Let task results determine how far to expand the trial
Keep the input version, full output, human corrections and actual usage records for each trial. Expand to similar tasks only when a document type consistently produces answers with traceable references. If you keep having to find the key evidence again, narrow the task and continue using material checked by a person. This is an acceptance method, not a report of this model's tested performance.
The summary does not establish accuracy, reliability or deployment requirements for your document types, or confirm that the full weights are available. Teams considering local deployment should separately check which model they can actually obtain and what it needs to run. Treat the release as a reason to investigate, rather than a production selection decision.
Source links
The links below were supplied with the collected records. This article offers independent analysis of their summaries; we have not individually checked the original sources or tested the products.