Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-4.1
56
Sakana Fugu Ultra
100
Pick Sakana Fugu Ultra if you want the stronger benchmark profile. GPT-4.1 only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
Coding
+28.9 difference
Knowledge
+29.2 difference
GPT-4.1
Sakana Fugu Ultra
$2 / $8
$5 / $30
108 t/s
N/A
1.02s
N/A
1M
1M
Pick Sakana Fugu Ultra if you want the stronger benchmark profile. GPT-4.1 only becomes the better choice if you want the cheaper token bill or 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 56. 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 knowledge, where it averages 95.5 against 66.3. The single biggest benchmark swing on the page is GPQA, 66.3% to 95.5%.
Sakana Fugu Ultra is also the more expensive model on tokens at $5.00 input / $30.00 output per 1M tokens, versus $2.00 input / $8.00 output per 1M tokens for GPT-4.1. That is roughly 3.8x on output cost alone. Sakana Fugu Ultra is the reasoning model in the pair, while GPT-4.1 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 is ahead on BenchLM's provisional leaderboard, 100 to 56. The biggest single separator in this matchup is GPQA, where the scores are 66.3% and 95.5%.
Sakana Fugu Ultra has the edge for knowledge tasks in this comparison, averaging 95.5 versus 66.3. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Sakana Fugu Ultra has the edge for coding in this comparison, averaging 83.5 versus 54.6. GPT-4.1 stays close enough that the answer can still flip depending on your workload.
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