This page compares two models on one job: checking a finished draft against the sources supplied with it. It covers finding the sentences nothing supports, pointing at the passage that carries each claim, explaining the gap, and returning a reviewable audit.
Jul 30, 2026 · 12 min read
Kimi K3 is the economical auditor for every sentence in the draft. GPT-5.6 Sol is the cautious second reader for the verdicts that are disputed, partial or commercially sensitive.
The closest evidence to this job is a professional knowledge-work evaluation, and K3 leads it: a 51 percent rubric pass rate against 41.8 percent, with its strength concentrated in analytical quality rather than presentation9. On finding the passage inside a long pack the two are level, 74.7 against 73.7 on a long-context retrieval set built from roughly 100,000-token multi-document inputs1, 10. So the cheaper model is not the weaker reader here, which is the finding that shapes the whole workflow.
Sol's advantage is documentation and difficult reasoning. OpenAI published a model-specific factuality evaluation on conversations users had flagged for errors, and Moonshot has published no equivalent for K37. Sol also leads narrowly on hard reasoning sets and more clearly on a legal research set in Moonshot's own table, 48.1 against 44.21. That makes it the better final judge on a claim that turns on a qualification, and it is not proof that it flags unsupported sentences more reliably.
One aggregate score cannot tell you whether a checker misses claims or over-flags them. Measure the two separately or the number is decoration.
Where a claim turns on a qualification, the model with the reasoning lead is worth the higher rate, and a person still signs off after it.
Rates that stay flat across the window make an exhaustive pass affordable on packs that would re-price the whole request elsewhere.
If the evidence lives in scanned tables, reading the page correctly comes before judging the claim. That is where the parsing figure earns its place.
This page compares the two models through their API in one neutral setup, on the four parts of checking a draft that a person would otherwise do by hand.
Those four are marking the sentences that nothing in the sources supports, locating the passage that carries or nearly carries each claim, explaining what is missing without quietly adding a fact, and returning the whole thing in a shape a reviewer can work through. The third one is where a checker does the most damage, because a fabricated justification reads exactly like a real one.
Reference managers, document extraction pipelines and review interfaces are left out on purpose. They belong to the app around the model, so the same model behaves differently in a chat product, through the API, or inside a workspace. Judging them here would compare wrappers rather than the checking itself.
The published facts that affect a claim audit. The pricing rows are the practical difference, because a draft plus its full source pack is an input-heavy request.
Figures from Moonshot AI and OpenAI documentation, checked July 30, 2026. The document-parsing figure comes from Moonshot's own comparison table, so read it as the vendor's claim rather than an independent result. Both models can return the same audit schema, so the choice here is about reading and judgment rather than output format.
Two rows here are ties and one has no winner at all. That last one is the row a legal or editorial team should read first, because it is about the mistake that is hardest to see.
Better-choice calls map to what each source measured. Several figures come from Moonshot's own table or from runs using different harnesses, no public benchmark compares these two on sentence-level claim support, and the strongest factuality evidence on either side compares a model with its own predecessor rather than with the other model here.
Score both models on drafts whose sources you know well, and build the answer key first. The single most important design choice is to count missed unsupported claims and wrongly flagged supported claims separately, because one aggregate score hides which way a checker leans.
Take three to five real assignments and include one very long source pack, one built from scans, and one draft with deliberate errors you inserted. Label every sentence by hand as supported, partly supported, unsupported, contradicted, cited to the wrong passage, or unverifiable.
Identical prompt, sentence identifiers, source text, page labels, effort level and output schema on both sides, with no editing before scoring. Require a quoted passage for every verdict so the answer can be validated automatically.
Chat products and API harnesses differ in system prompts, document extraction and context handling, so test through the pipeline you will deploy. Start both at a middle effort setting and raise it only where a test shows the gain.
Record how many unsupported claims each model caught and how many supported ones it wrongly flagged, then check passage localisation, whether the quote really carries every part of the claim, and how many extra factual statements the checker introduced. Review blind on high-stakes work.
The most useful source here is not a leaderboard, it is a study of the task itself. Here is what each one measures and how far it can be pushed.
None of these predicts how often a model will invent an assertion while reviewing one particular company's draft against one particular source set. The published work is consistent on one point though: a checker's overall score tells you less than its two error rates measured separately.
Both models need the same discipline: no outside knowledge, a quoted passage for every verdict, and a named fallback when the sources do not settle the question. What differs is what each one has to be stopped from doing.
Kimi K3 does best when the job is broken into repeated, concrete decisions at sentence level, and Moonshot's guidance is to state that only the supplied references may be used and to fix a fallback for when the answer is absent3. It can also widen an audit into a broader research task, so say plainly that recommendations and rewriting come later. Cap the length of each explanation and require a source identifier for every factual clause.
GPT-5.6 Sol does best with explicit success criteria, approval boundaries and instructions for what counts as an important ambiguity6. The failure to design out is a plausible completion presented as verified, so forbid outside knowledge, require the quote before the explanation, and tell it not to propose a replacement fact when nothing supports a claim. Start at a middle effort setting and test the higher ones rather than assuming they help.
A Kimi K3 prompt: one decision per sentence
Check every numbered draft sentence only against SOURCES.
Return JSON, one object per sentence.
Verdicts: supported, partial, unsupported, contradicted.
For each one give:
the shortest passage that carries the claim
its source and page
what is missing, if anything
For an unsupported claim, quote the closest relevant passage
and state exactly which element is not there.
Add no outside facts. If uncertain, use needs_human_review.
Do not suggest fixes or rewrite anything yet.A GPT-5.6 Sol prompt: closed-book adjudication
Act as a closed-book evidence auditor. Use only SOURCES.
For each draft sentence:
split it into atomic claims
classify each claim
identify the exact supporting or conflicting passage
Never repair a claim with your own knowledge.
If no passage supports it, say so and do not propose
a replacement fact.
Return the supplied JSON schema and nothing else.One model does too much, the other sounds too sure, and both can accept a passage that is merely about the right subject. The last one is the failure that survives review.
One question first. What costs more, missing an unsupported claim or paying for a second review? Then follow the branch that matches most of your drafts.
A starting point, not a rule. Score both on drafts whose sources you know.
If you need exhaustive first-pass coverage across many drafts, use Kimi K3 and spend the price difference on a second pass rather than on a single careful one9. If source packs regularly exceed 272,000 tokens or arrive as scans, that choice gets easier: the rates hold and the document-parsing figure is on the same side1, 2.
If one mistaken approval could cause legal, financial or reputational harm, use GPT-5.6 Sol and keep a person after it7. The same applies where claims turn on a qualification, a precedent or several linked sources, which is where its reasoning lead actually shows up1.
If you want both high recall and a cautious final judgment, run the two in sequence: K3 classifies every sentence, Sol reviews everything marked partial, contradicted, unsupported or low confidence, and a person approves the final list. And if what you need is a strict audit table, neither model decides it. The schema and an automatic check that every quote exists verbatim in the source matter more than the choice between them4, 5.
One limit applies to Playgram rather than the models. A newsroom or legal team that needs the flags delivered inside its own editorial system, attached to the sentence for a reviewer to clear one by one, needs that system's own integration. Playgram is a chat workspace, so the audit table comes back in the conversation to be worked through from there.
Use GPT-5.6 Sol if only one model can be deployed and false confidence is the expensive mistake. Use Kimi K3 as the first stage of the stronger two-model workflow, because its long-document and analytical results make it a credible checker at a materially lower price.
The conclusion is provisional and the reasons are specific. No public benchmark compares these exact models on sentence-level claim support, passage location, wrongly flagged claims and checker-introduced assertions12. Several figures come from one vendor's own table, the retrieval runs used different harnesses, and the one factuality study with real weight compares a model with its predecessor rather than with the other model here1, 7. Prices and behaviour will also move.
The safest final step is to test the shape of your own drafts, not a generic prompt from the internet. A fair test needs the same setup for both models: the same sources, the same sentence identifiers, the same prompt and the same place to run them, so the result reflects the models and not the tool around them. In practice that is harder than it sounds, since most teams end up running one model in one app and the other in a different one, on two separate subscriptions, which tilts the comparison before the first verdict comes back. The cleaner the setup, the more the difference you see is really Kimi K3 vs GPT-5.6 Sol, and not just which one happened to be easier to reach that day.
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