Head-to-head comparison across 6benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Kimi K2.5
63
Nemotron 3 Ultra
68
Verified leaderboard positions: Kimi K2.5 #18 · Nemotron 3 Ultra #24
Pick Nemotron 3 Ultra if you want the stronger benchmark profile. Kimi K2.5 only becomes the better choice if instruction following is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Agentic
+2.9 difference
Coding
+10.0 difference
Reasoning
+0.9 difference
Knowledge
+2.5 difference
Multilingual
+0.7 difference
Inst. Following
+12.2 difference
Kimi K2.5
Nemotron 3 Ultra
$0.6 / $3
$0 / $0
45 t/s
N/A
2.38s
N/A
256K
1M
Pick Nemotron 3 Ultra if you want the stronger benchmark profile. Kimi K2.5 only becomes the better choice if instruction following is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Nemotron 3 Ultra is clearly ahead on the provisional aggregate, 68 to 63. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Nemotron 3 Ultra's sharpest advantage is in coding, where it averages 74.2 against 64.2. The single biggest benchmark swing on the page is BrowseComp, 60.6% to 44.4%. Kimi K2.5 does hit back in instruction following, so the answer changes if that is the part of the workload you care about most.
Kimi K2.5 is also the more expensive model on tokens at $0.60 input / $3.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Nemotron 3 Ultra. That is roughly Infinityx on output cost alone. Nemotron 3 Ultra is the reasoning model in the pair, while Kimi K2.5 is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. Nemotron 3 Ultra gives you the larger context window at 1M, compared with 256K for Kimi K2.5.
Nemotron 3 Ultra is ahead on BenchLM's provisional leaderboard, 68 to 63. The biggest single separator in this matchup is BrowseComp, where the scores are 60.6% and 44.4%.
Kimi K2.5 has the edge for knowledge tasks in this comparison, averaging 65.1 versus 62.6. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
Nemotron 3 Ultra has the edge for coding in this comparison, averaging 74.2 versus 64.2. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
Nemotron 3 Ultra has the edge for reasoning in this comparison, averaging 61.9 versus 61. Inside this category, AA-LCR is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for agentic tasks in this comparison, averaging 54.6 versus 51.7. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for instruction following in this comparison, averaging 93.9 versus 81.7. Inside this category, AA-IFBench is the benchmark that creates the most daylight between them.
Nemotron 3 Ultra has the edge for multilingual tasks in this comparison, averaging 83 versus 82.3. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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