Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Kimi K2.6
80
Ornith-1.0-35B
67
Verified leaderboard positions: Kimi K2.6 #13 · Ornith-1.0-35B unranked
Pick Kimi K2.6 if you want the stronger benchmark profile. Ornith-1.0-35B only becomes the better choice if you want the cheaper token bill or you need the larger 262K context window.
Agentic
+8.9 difference
Kimi K2.6
Ornith-1.0-35B
$0.95 / $4
$0 / $0
N/A
N/A
N/A
N/A
256K
262K
Pick Kimi K2.6 if you want the stronger benchmark profile. Ornith-1.0-35B only becomes the better choice if you want the cheaper token bill or you need the larger 262K context window.
Kimi K2.6 is clearly ahead on the provisional aggregate, 80 to 67. 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 agentic, where it averages 73.1 against 64.2. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 66.7% to 64.2%.
Kimi K2.6 is also the more expensive model on tokens at $0.95 input / $4.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Ornith-1.0-35B. That is roughly Infinityx on output cost alone. Ornith-1.0-35B gives you the larger context window at 262K, compared with 256K for Kimi K2.6.
Kimi K2.6 is ahead on BenchLM's provisional leaderboard, 80 to 67. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 66.7% and 64.2%.
Kimi K2.6 has the edge for agentic tasks in this comparison, averaging 73.1 versus 64.2. Inside this category, Claw-Eval is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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