Two of K3's strongest published numbers are search benchmarks: 91.2 on BrowseComp and 95.0 F1 on DeepSearchQA. Both measure something specific and useful — whether a model can chase a question across many sources and come back with a defensible answer rather than a plausible one. That is a different skill from writing a good essay, and it is the one research work depends on.
This page is written for researchers, analysts, and teams building research automation. If you want the model overview first, start with the complete Kimi K3 guide.
| Best effort level | max (the API default is max) |
| Context available | 1M tokens — enough for real working material, not just snippets |
| Cost | $3.00 per million input tokens, $15.00 per million output, $0.30 cached input |
Why Kimi K3 suits deep research
- Multi-hop search is a reasoning task, and reasoning is always on. There is no configuration in which K3 skims.
- The 1M tokens window holds dozens of retrieved sources at once, so synthesis happens across the full evidence set rather than a sliding window of it.
- Dynamic tool loading lets a research loop expose exactly the search and fetch tools a given step needs.
- K3 Swarm Max suits the fan-out phase of research — many parallel sub-queries — where a single sequential agent is just slow.
- Native vision reads the charts and tables in retrieved PDFs instead of skipping them.
A workflow that works
- Separate fan-out from synthesis. Use a parallel phase to gather, then a single deep call to reason over everything collected. Mixing the two produces shallow work.
- Demand a source per claim. Structure the output so every assertion carries a URL or document reference. Unsourced sentences are where research automation quietly goes wrong.
- Cache the brief. The research question, output schema, and quality criteria are stable — put them in the cached prefix and let only the retrieved material vary.
- Ask it to state what it could not find. An explicit gaps section is far more useful than a confident answer built on partial evidence, and models will produce one if asked.
- Keep a human on the conclusions. Verification of the sources it cites is cheap; discovering after the fact that a report rests on a misread paper is not.
Settings to start from
Set reasoning_effort to max. Research is the archetypal case where a wrong intermediate conclusion is expensive — everything downstream inherits it. Keep max for the synthesis pass. The fan-out phase, where each call is a narrow lookup, is a reasonable place to use low and save real money.
Remember that reasoning cannot be turned off on this model, and that the API default is max — the most expensive of the three levels. Setting it explicitly is the single highest-value line of configuration you will write. See reasoning effort explained if you want to tune it properly.
What to watch out for
- Benchmark scores are not a truthfulness guarantee. 91.2 on BrowseComp is a strong result on a specific evaluation. It does not mean the model will not cite something that does not support the claim.
- You supply the search. K3 is a model, not a research product. Web access, fetching, and citation plumbing are yours to build unless you use it through the Kimi apps.
- Search-heavy runs are token-heavy runs. Retrieved pages fill context fast, and at $3.00 per million uncached input this is the workload most likely to surprise you on cost.
- Recency depends entirely on your retrieval layer. The model's own knowledge has a cutoff; nothing about a 2.8T parameter count changes that.
The verdict
A strong engine for research automation, priced so that the token-hungry shape of research work is actually affordable. The published search numbers justify a trial; the citation discipline you build around it decides whether the output is trustworthy.
If cost is the constraint, read how to cut Kimi K3 costs before downgrading — caching and effort levels usually beat switching models.
More on Kimi K3
Start with the complete Kimi K3 guide for the overview, or go deeper:
Ready to go deeper?
Read the full Kimi K3 guideFrequently Asked Questions
Can Kimi K3 search the web?
The model supports tool calling, so it can search the web if you give it a search tool — but the API does not include web access by default. The Kimi consumer apps do provide search. If you are building your own research loop, retrieval and fetching are your responsibility.
How good is Kimi K3 at research compared with other models?
Its published search figures are among the strongest available: 91.2 on BrowseComp and 95.0 F1 on DeepSearchQA. These are the vendor's own reported results on public benchmarks, so treat them as a reason to test rather than as a settled ranking.
Does Kimi K3 cite its sources?
Only if you make it. Ask for a source reference attached to every claim, and validate that the cited material actually supports the statement. This is the single highest-value guardrail in any research pipeline, regardless of model.
