Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Holo3-35B-A3B
100
Sakana Fugu Ultra
100
Treat this as a split decision. Holo3-35B-A3B makes more sense if agentic is the priority or you would rather avoid the extra latency and token burn of a reasoning model; Sakana Fugu Ultra is the better fit if you need the larger 1M context window or you want the stronger reasoning-first profile.
Agentic
+0.5 difference
Holo3-35B-A3B
Sakana Fugu Ultra
$null / $null
$5 / $30
N/A
N/A
N/A
N/A
64K
1M
Treat this as a split decision. Holo3-35B-A3B makes more sense if agentic is the priority or you would rather avoid the extra latency and token burn of a reasoning model; Sakana Fugu Ultra is the better fit if you need the larger 1M context window or you want the stronger reasoning-first profile.
Holo3-35B-A3B and Sakana Fugu Ultra finish on the same provisional overall score, so this is less about a single winner and more about where the edge shows up. The provisional headline says tie; the benchmark table is where the real choice happens.
Sakana Fugu Ultra is the reasoning model in the pair, while Holo3-35B-A3B 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. Sakana Fugu Ultra gives you the larger context window at 1M, compared with 64K for Holo3-35B-A3B.
Holo3-35B-A3B and Sakana Fugu Ultra are tied on the provisional overall score, so the right pick depends on which category matters most for your use case.
Holo3-35B-A3B has the edge for agentic tasks in this comparison, averaging 82.6 versus 82.1. Sakana Fugu Ultra stays close enough that the answer can still flip depending on your workload.
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