Both models passed every hidden test in the same coding task. One commentator calculated that K3 cost about half as much per task as Claude Opus 5.5, but took about twice as long. If you looked only at the pass rate, you would miss the difference between paying less and getting the answer sooner.
Both Passed the Same Coding Test
Atomic Agent gave K3 and Claude Opus 5.5 a Python package with planted bugs and hidden tests. Both passed every hidden test. They also did so in repeated low-effort runs, so the full pass was not confined to a single run at that setting.
That gives this comparison a useful starting point: on this package, neither model lost a hidden test. It does not make cost and completion time interchangeable. The reported difference was between two runs that both finished the task, with K3 costing less and taking longer.
The scope matters as much as the result. This was one small coding task with one package and its hidden tests. A full pass here tells us what happened on that task; it does not settle which model to choose for other work.
The Cheaper Run Took Longer
The commentator’s calculation puts the contrast plainly:
- K3 cost about half as much per task as Claude Opus 5.5.
- K3 took about twice as long.
Those numbers describe the same coding comparison. The lower reported cost came with a longer wait, even though both models passed the tests. That is why the pass rate alone gives an incomplete account of this run.
Changing the effort setting changed the cost comparison. At maximum effort, the price gap disappeared. Atomic Agent also counted about four times as many model calls for K3 at that setting.
The cheap result, then, should stay attached to the setting that produced it. It would be easy to carry the half-cost figure into a discussion of K3 at maximum effort, but the script reports a different result there: no price gap. The model-call count gives another concrete difference at maximum effort, without by itself explaining the completion time in the cheaper run.
For this task, the trade-off is specific. The models matched on hidden-test correctness in the reported runs. At low effort, the commentator calculated a lower cost and a longer completion time for K3; at maximum effort, the reported cost advantage disappeared.
The Broader Scores Do Not Pick One Winner
Moonshot reports 51.7 for Kimi K3 one Pro against 46.3 for Opus 5.5 on SW Marathon. On Terminal Bench 4.0, Moonshot reports 65.7 against 66.4, with Opus ahead. Artificial Analysis ranks K3 one Pro fourth at 55 points, behind Opus 5.5 at 58.
Those comparisons do not all point the same way. Moonshot’s SW Marathon score favors K3 one Pro, while its Terminal Bench 4.0 score favors Opus 5.5. Artificial Analysis also places Opus ahead in its ranking.
There is a setting difference to keep in view, too. Moonshot ran its reported scores at maximum reasoning effort. That is not the setting of the cheaper coding run, and the coding test’s price gap disappeared at maximum effort. The broader scores and the lower-cost run therefore answer different questions.
On the small Python task, the clearest result is straightforward: both models passed every hidden test, while reported cost and completion time pulled in different directions. Outside benchmark runs would help show how well Moonshot’s broader comparisons hold up.