Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-4.1 nano
27
Interfaze Beta
76
Pick Interfaze Beta if you want the stronger benchmark profile. GPT-4.1 nano 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
+39.6 difference
GPT-4.1 nano
Interfaze Beta
$0.1 / $0.4
$1.5 / $3.5
181 t/s
N/A
0.63s
N/A
1M
1M
Pick Interfaze Beta if you want the stronger benchmark profile. GPT-4.1 nano 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.
Interfaze Beta is clearly ahead on the provisional aggregate, 76 to 27. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Interfaze Beta's sharpest advantage is in knowledge, where it averages 89.9 against 50.3. The single biggest benchmark swing on the page is GPQA, 50.3% to 89.9%.
Interfaze Beta is also the more expensive model on tokens at $1.50 input / $3.50 output per 1M tokens, versus $0.10 input / $0.40 output per 1M tokens for GPT-4.1 nano. That is roughly 8.8x on output cost alone. Interfaze Beta is the reasoning model in the pair, while GPT-4.1 nano 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.
Interfaze Beta is ahead on BenchLM's provisional leaderboard, 76 to 27. The biggest single separator in this matchup is GPQA, where the scores are 50.3% and 89.9%.
Interfaze Beta has the edge for knowledge tasks in this comparison, averaging 89.9 versus 50.3. Inside this category, GPQA is the benchmark that creates the most daylight between them.
For engineers, researchers, and the plain curious — a weekly brief on new models, ranking shifts, and pricing changes.
Free. No spam. Unsubscribe anytime.