Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Kimi K2.6
85
Step 3.7 Flash
72
Verified leaderboard positions: Kimi K2.6 #9 · Step 3.7 Flash unranked
Pick Kimi K2.6 if you want the stronger benchmark profile. Step 3.7 Flash only becomes the better choice if you want the cheaper token bill.
Agentic
+7.2 difference
Coding
+15.7 difference
Kimi K2.6
Step 3.7 Flash
$0.95 / $4
$0.2 / $1.15
N/A
N/A
N/A
N/A
256K
256K
Pick Kimi K2.6 if you want the stronger benchmark profile. Step 3.7 Flash only becomes the better choice if you want the cheaper token bill.
Kimi K2.6 is clearly ahead on the provisional aggregate, 85 to 72. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Kimi K2.6's sharpest advantage is in coding, where it averages 72 against 56.3. The single biggest benchmark swing on the page is BrowseComp, 83.2% to 75.8%.
Kimi K2.6 is also the more expensive model on tokens at $0.95 input / $4.00 output per 1M tokens, versus $0.20 input / $1.15 output per 1M tokens for Step 3.7 Flash. That is roughly 3.5x on output cost alone.
Kimi K2.6 is ahead on BenchLM's provisional leaderboard, 85 to 72. The biggest single separator in this matchup is BrowseComp, where the scores are 83.2% and 75.8%.
Kimi K2.6 has the edge for coding in this comparison, averaging 72 versus 56.3. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Kimi K2.6 has the edge for agentic tasks in this comparison, averaging 73.1 versus 65.9. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
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.