Head-to-head comparison across 4benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Grok 4.3
79
Kimi K2.5
64
Verified leaderboard positions: Grok 4.3 unranked · Kimi K2.5 #11
Pick Grok 4.3 if you want the stronger benchmark profile. Kimi K2.5 only becomes the better choice if coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Coding
+16.9 difference
Knowledge
+11.2 difference
Multimodal
+0.4 difference
Inst. Following
+12.6 difference
Grok 4.3
Kimi K2.5
$1.25 / $2.5
$0.6 / $3
209 t/s
45 t/s
12.36s
2.38s
1M
256K
Pick Grok 4.3 if you want the stronger benchmark profile. Kimi K2.5 only becomes the better choice if coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Grok 4.3 is clearly ahead on the provisional aggregate, 79 to 64. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Kimi K2.5 is also the more expensive model on tokens at $0.60 input / $3.00 output per 1M tokens, versus $1.25 input / $2.50 output per 1M tokens for Grok 4.3. Grok 4.3 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. Grok 4.3 gives you the larger context window at 1M, compared with 256K for Kimi K2.5.
Grok 4.3 is ahead on BenchLM's provisional leaderboard, 79 to 64. The biggest single separator in this matchup is HLE, where the scores are 35% and 30.1%.
Kimi K2.5 has the edge for knowledge tasks in this comparison, averaging 65.1 versus 53.9. Inside this category, HLE is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for coding in this comparison, averaging 64.2 versus 47.3. Inside this category, SciCode is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for multimodal and grounded tasks in this comparison, averaging 78.5 versus 78.1. Inside this category, MMMU-Pro 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 81.3. Grok 4.3 stays close enough that the answer can still flip depending on your workload.
Estimates at 50,000 req/day · 1000 tokens/req average.
For engineers, researchers, and the plain curious — a weekly brief on new models, ranking shifts, and pricing changes.
Free. No spam. Unsubscribe anytime.