Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Claude 4.1 Opus
51
Sakana Fugu Ultra
100
Pick Sakana Fugu Ultra if you want the stronger benchmark profile. Claude 4.1 Opus only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Coding
+9.0 difference
Claude 4.1 Opus
Sakana Fugu Ultra
$15 / $75
$5 / $30
29 t/s
N/A
1.66s
N/A
200K
1M
Pick Sakana Fugu Ultra if you want the stronger benchmark profile. Claude 4.1 Opus only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Sakana Fugu Ultra is clearly ahead on the provisional aggregate, 100 to 51. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Sakana Fugu Ultra's sharpest advantage is in coding, where it averages 83.5 against 74.5.
Claude 4.1 Opus is also the more expensive model on tokens at $15.00 input / $75.00 output per 1M tokens, versus $5.00 input / $30.00 output per 1M tokens for Sakana Fugu Ultra. That is roughly 2.5x on output cost alone. Sakana Fugu Ultra is the reasoning model in the pair, while Claude 4.1 Opus 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 200K for Claude 4.1 Opus.
Sakana Fugu Ultra is ahead on BenchLM's provisional leaderboard, 100 to 51.
Sakana Fugu Ultra has the edge for coding in this comparison, averaging 83.5 versus 74.5. Claude 4.1 Opus stays close enough that the answer can still flip depending on your workload.
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