Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Kimi K2.5
64
Step 3.7 Flash
72
Verified leaderboard positions: Kimi K2.5 #15 · Step 3.7 Flash unranked
Pick Step 3.7 Flash 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.
Agentic
+11.3 difference
Coding
+7.9 difference
Kimi K2.5
Step 3.7 Flash
$0.6 / $3
$0.2 / $1.15
45 t/s
N/A
2.38s
N/A
256K
256K
Pick Step 3.7 Flash 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.
Step 3.7 Flash is clearly ahead on the provisional aggregate, 72 to 64. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Step 3.7 Flash's sharpest advantage is in agentic, where it averages 65.9 against 54.6. The single biggest benchmark swing on the page is BrowseComp, 60.6% to 75.8%. Kimi K2.5 does hit back in coding, 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 $0.20 input / $1.15 output per 1M tokens for Step 3.7 Flash. That is roughly 2.6x on output cost alone. Step 3.7 Flash 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.
Step 3.7 Flash is ahead on BenchLM's provisional leaderboard, 72 to 64. The biggest single separator in this matchup is BrowseComp, where the scores are 60.6% and 75.8%.
Kimi K2.5 has the edge for coding in this comparison, averaging 64.2 versus 56.3. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Step 3.7 Flash has the edge for agentic tasks in this comparison, averaging 65.9 versus 54.6. Inside this category, Toolathlon is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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