Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Claude 3.5 Sonnet
41
Laguna M.1
46
Pick Laguna M.1 if you want the stronger benchmark profile. Claude 3.5 Sonnet only becomes the better choice if you need the larger 200K context window or you would rather avoid the extra latency and token burn of a reasoning model.
Coding
+7.4 difference
Claude 3.5 Sonnet
Laguna M.1
$3 / $15
$0 / $0
N/A
N/A
N/A
N/A
200K
131K
Pick Laguna M.1 if you want the stronger benchmark profile. Claude 3.5 Sonnet only becomes the better choice if you need the larger 200K context window or you would rather avoid the extra latency and token burn of a reasoning model.
Laguna M.1 is clearly ahead on the provisional aggregate, 46 to 41. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Laguna M.1's sharpest advantage is in coding, where it averages 56.4 against 49. The single biggest benchmark swing on the page is SWE-bench Verified, 49% to 72.5%.
Claude 3.5 Sonnet is also the more expensive model on tokens at $3.00 input / $15.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Laguna M.1. That is roughly Infinityx on output cost alone. Laguna M.1 is the reasoning model in the pair, while Claude 3.5 Sonnet 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. Claude 3.5 Sonnet gives you the larger context window at 200K, compared with 131K for Laguna M.1.
Laguna M.1 is ahead on BenchLM's provisional leaderboard, 46 to 41. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 49% and 72.5%.
Laguna M.1 has the edge for coding in this comparison, averaging 56.4 versus 49. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
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