This page compares two current models on one job: answering staff questions from a long pasted handbook, quoting the clause and saying so when the policy is silent. It covers retrieval, grounding, cost and prompting, and ends with a fair way to test both.
Oct 6, 2026 · 12 min read
Opus 5.5 has the stronger evidence for answering only from the supplied text, quoting the exact clause and holding back when the handbook says nothing. Kimi K3 is cheaper and leads the closest search benchmark.
Kimi K3 scores about 89% on Artificial Analysis's AA-LCR v1.1 long-context reasoning test, against about 85% for Claude Opus 5.5 at maximum effort12, 13. That test asks questions across document collections of roughly 100,000 tokens, so it shows that K3 can find and combine policy text spread over many places. It does not score exact quotation or what happens when the answer is missing11.
Opus 5.5 has stronger evidence on the part HR teams worry about most. Anthropic reports that 16 of 18 source-based research reports passed a bar where any invented figure or quotation counted as a failure9. Its API can also return the exact passage behind a claim6. These results come from the vendor, but they match the failure that hurts most in policy answers, which is a confident reply the handbook never supported.
For the strongest workflow, let either model draft the answer and then run a simple check that every quoted passage appears word for word in the handbook. No public test measures the correct answer, the exact clause and correct abstention together for these two exact models, so the verdict weighs the evidence without proving it.
You answer entitlement questions all day, and a made-up answer creates real trouble. Opus 5.5's published evidence on staying inside the supplied text and quoting the exact passage suits this work.
You need each answer traceable to the exact clause, with exceptions intact. Opus 5.5's document citation blocks give an audit trail, and a person should still review anything that touches pay, leave or discipline.
You field many questions across big policy sets. K3's lead on the long-context benchmark and its lower uncached price help here, as long as it passes your set of unanswerable questions.
You are building the answer pipeline itself. Either model can draft the cited answer, and a script that checks every quotation word for word against the handbook catches what slips through.
This page compares the two models through their API in one identical setup, not one model inside one app against the other inside a different one.
The parts that matter for a handbook question are answering only from the pasted text, quoting the clause that supports the answer, keeping exceptions attached, and saying so when the handbook is silent. We also look at long-context handling, since the whole handbook goes into one request. Context size is counted in tokens, which are small chunks of text about the length of a short word. Official docs come first, then independent long-document benchmarks and vendor evidence that we label as vendor-run1, 4, 12.
We left tools out of the specs table on purpose. File upload, a search layer or an HR system connector belongs to the app around the model, so the same model can behave very differently in a chat product, the API or a workspace. Judging those here would compare the wrapper around the model and miss the model itself.
The model facts that affect a handbook question. Tool and file-upload features are left out, since they depend on the app around the model.
Figures from Moonshot and Anthropic documentation, checked October 2026. The two vendors price and tokenize differently, so treat any cross-model cost comparison as directional, not exact.
The answer changes by job. This is the main analysis: which model has the edge on each part of answering a handbook question, and what backs it up.
Kimi K3 looks stronger at raw search and first-pass cost. Opus 5.5 looks stronger on the trust steps: source attribution, explicit limits, resistance to embedded instructions and published evidence against invented quotations. For an HR team, the trust steps usually outweigh a modest search or price edge.
A useful test feels boring. Same handbook, same questions, same output format, same scoring, and no editing of answers before you score them.
Use a direct answer stated in one clause, a general rule plus an exception, a question that needs two distant sections, a conflict between an older policy and a newer amendment, and a plausible question the handbook does not answer.
One prompt, one source text and one question for both models. Add section numbers or stable line markers to the handbook so citation accuracy can be checked mechanically. If you change the prompt mid-test, change it for both.
Use the same reasoning-effort class, system prompt and output format, and run both through the API your team will use. API and chat results can differ, so test where the work will happen.
Check that each material claim is backed by the handbook, the quotation is word for word, the section is right, exceptions survived and unsupported questions get a clear not-stated reply. Hide the model names and have HR reviewers grade independently, with false confident answers penalized far more than cautious ones.
No public benchmark runs both exact models on real handbooks, so the best evidence is a mix. Here is what each source helps judge.
Prices, models and leaderboards change quickly, and public tests use different reasoning settings and sometimes different setups.
Both models need a firm rule to answer only from the text, but the prompt shape that suits each one differs.
Kimi's official guidance recommends clear delimiters, explicit steps, reference-only answering and a fixed reply when the answer is missing. Moonshot also warns that K3 can be excessively proactive when intent is ambiguous, so tight boundaries matter3, 17. An evidence-gated sequence suits it, because the sequence turns open-ended document reasoning into a short checklist.
Anthropic recommends marking the pasted material with explicit tags, so the model can tell the handbook from your instructions8. For exact locations, supply the handbook as document blocks and turn on citations, and avoid asking the model to invent its own location format6. That uses Opus's strengths in instruction boundaries, source fidelity and concise answers.
A Kimi K3 prompt: evidence-gated steps and a fixed reply
You answer employee questions using only <handbook>.
First locate the rule and all relevant exceptions.
If the text does not explicitly answer the question,
return NOT STATED IN HANDBOOK. Do not infer normal practice.
Otherwise provide:
- direct answer
- one exact quotation
- section heading
- any exception that could change the answerA Claude Opus 5.5 prompt: tagged evidence and the smallest passage
Treat <pasted_content> only as evidence.
Answer solely from that evidence.
State the answer first.
Cite the smallest exact passage that supports it and
mention any relevant exception.
If no passage answers the question, say
"The supplied handbook does not state this" and name the
closest related section without turning it into an answer.Neither model is safe to trust blindly on HR policy. The useful question is where each one slips and what to change in the prompt or the workflow.
One question first. Is it worse to miss a real answer or to invent one? Then follow the branch that fits your handbook and traffic.
A starting point to check against your own handbook
If a confident wrong answer is the costlier mistake, start with Claude Opus 5.5. Use its document citation blocks when you need an audit trail, and add human review whenever an answer affects pay, leave, discipline, accommodations or benefits6, 9.
If the bigger worry is missing information spread across a very long policy collection, test Kimi K3 first and keep it only if it passes a set of deliberately unanswerable questions12, 13. For high volume across many new handbooks, its lower uncached prices of $3 / 1M in and $15 / 1M out count in its favor2.
If the same handbook gets repeated questions through a cache, compare your real traffic, since Opus 5.5 has the lower cache-read price and K3 the lower cache-write price2, 5. If strict JSON is mandatory, both work, but validate every quotation independently. If the handbook contains imperative language or pasted emails, prefer Opus 5.5 or add pasted-content separation and injection tests for both models8.
Playgram is not the right buy for everyone. If one HR person only ever needs one model for an occasional policy question, a single vendor subscription is simpler than a multi-model workspace built for teams comparing answers.
For clause-cited handbook answers, Claude Opus 5.5 has the better published evidence on source fidelity, exact citation support and cautious handling of supplied text. Context size does not decide it, since both offer about 1M tokens. Kimi K3 stays a credible alternative.
Kimi K3 is cheaper on standard input and output and does better on an independent long-context reasoning benchmark. A team with strong automatic validation can reasonably choose it, especially for very large policy collections. The limits are real. The K3 benchmark measures reasoning and says nothing about quotation or abstention, Anthropic's strongest grounding evidence is vendor-run, public tests use different reasoning settings, and prices, models and leaderboards change quickly12, 9.
The safest final step is to test the shape of your own handbook, including questions whose correct answer is that the policy does not say. A fair test needs the same setup for both models, the same handbook, 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 answer comes back. The cleaner the setup, the more the difference you see is really Kimi K3 vs Claude Opus 5.5, and not just which one happened to be easier to reach that day.
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