Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Qwen3 235B 2507
32
Sakana Fugu Ultra
100
Pick Sakana Fugu Ultra if you want the stronger benchmark profile. Qwen3 235B 2507 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.
Knowledge
+19.3 difference
Qwen3 235B 2507
Sakana Fugu Ultra
$0 / $0
$5 / $30
N/A
N/A
N/A
N/A
128K
1M
Pick Sakana Fugu Ultra if you want the stronger benchmark profile. Qwen3 235B 2507 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 32. 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 76.2. The single biggest benchmark swing on the page is GPQA, 77.5% 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 $0.00 input / $0.00 output per 1M tokens for Qwen3 235B 2507. That is roughly Infinityx on output cost alone. Sakana Fugu Ultra is the reasoning model in the pair, while Qwen3 235B 2507 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 128K for Qwen3 235B 2507.
Sakana Fugu Ultra is ahead on BenchLM's provisional leaderboard, 100 to 32. The biggest single separator in this matchup is GPQA, where the scores are 77.5% and 95.5%.
Sakana Fugu Ultra has the edge for knowledge tasks in this comparison, averaging 95.5 versus 76.2. Inside this category, GPQA is the benchmark that creates the most daylight between them.
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