Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-4.1 mini
45
LongCat-2.0
80
Pick LongCat-2.0 if you want the stronger benchmark profile. GPT-4.1 mini 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
+35.9 difference
GPT-4.1 mini
LongCat-2.0
$0.4 / $1.6
$0.75 / $2.95
80 t/s
N/A
0.76s
N/A
1M
1M
Pick LongCat-2.0 if you want the stronger benchmark profile. GPT-4.1 mini 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.
LongCat-2.0 is clearly ahead on the provisional aggregate, 80 to 45. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
LongCat-2.0's sharpest advantage is in coding, where it averages 59.5 against 23.6.
LongCat-2.0 is also the more expensive model on tokens at $0.75 input / $2.95 output per 1M tokens, versus $0.40 input / $1.60 output per 1M tokens for GPT-4.1 mini. LongCat-2.0 is the reasoning model in the pair, while GPT-4.1 mini 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.
LongCat-2.0 is ahead on BenchLM's provisional leaderboard, 80 to 45.
LongCat-2.0 has the edge for coding in this comparison, averaging 59.5 versus 23.6. GPT-4.1 mini stays close enough that the answer can still flip depending on your workload.
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