Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V4 Pro Base
43
Kimi K2.5 (Reasoning)
77
Pick Kimi K2.5 (Reasoning) if you want the stronger benchmark profile. DeepSeek V4 Pro Base only becomes the better choice if you need the larger 1M context window or you would rather avoid the extra latency and token burn of a reasoning model.
Knowledge
+23.9 difference
DeepSeek V4 Pro Base
Kimi K2.5 (Reasoning)
$null / $null
$0.6 / $3
N/A
N/A
N/A
N/A
1M
128K
Pick Kimi K2.5 (Reasoning) if you want the stronger benchmark profile. DeepSeek V4 Pro Base only becomes the better choice if you need the larger 1M context window or you would rather avoid the extra latency and token burn of a reasoning model.
Kimi K2.5 (Reasoning) is clearly ahead on the provisional aggregate, 77 to 43. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Kimi K2.5 (Reasoning)'s sharpest advantage is in knowledge, where it averages 87.3 against 63.4. The single biggest benchmark swing on the page is MMLU-Pro, 73.5% to 87.1%.
Kimi K2.5 (Reasoning) is the reasoning model in the pair, while DeepSeek V4 Pro Base 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. DeepSeek V4 Pro Base gives you the larger context window at 1M, compared with 128K for Kimi K2.5 (Reasoning).
Kimi K2.5 (Reasoning) is ahead on BenchLM's provisional leaderboard, 77 to 43. The biggest single separator in this matchup is MMLU-Pro, where the scores are 73.5% and 87.1%.
Kimi K2.5 (Reasoning) has the edge for knowledge tasks in this comparison, averaging 87.3 versus 63.4. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
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