Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Claude Opus 4.7 (Adaptive)
83
Sakana Fugu Ultra
100
Verified leaderboard positions: Claude Opus 4.7 (Adaptive) #10 · Sakana Fugu Ultra unranked
Pick Sakana Fugu Ultra if you want the stronger benchmark profile. Claude Opus 4.7 (Adaptive) only becomes the better choice if you want the cheaper token bill.
Agentic
+7.2 difference
Coding
+10.6 difference
Knowledge
+27.3 difference
Claude Opus 4.7 (Adaptive)
Sakana Fugu Ultra
$5 / $25
$5 / $30
N/A
N/A
N/A
N/A
1M
1M
Pick Sakana Fugu Ultra if you want the stronger benchmark profile. Claude Opus 4.7 (Adaptive) only becomes the better choice if you want the cheaper token bill.
Sakana Fugu Ultra is clearly ahead on the provisional aggregate, 100 to 83. 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 68.2. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 69.4% to 82.1%.
Sakana Fugu Ultra is also the more expensive model on tokens at $5.00 input / $30.00 output per 1M tokens, versus $5.00 input / $25.00 output per 1M tokens for Claude Opus 4.7 (Adaptive).
Sakana Fugu Ultra is ahead on BenchLM's provisional leaderboard, 100 to 83. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 69.4% and 82.1%.
Sakana Fugu Ultra has the edge for knowledge tasks in this comparison, averaging 95.5 versus 68.2. 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 72.9. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Sakana Fugu Ultra has the edge for agentic tasks in this comparison, averaging 82.1 versus 74.9. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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