Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-5.2
81
Laguna M.1
46
Pick GPT-5.2 if you want the stronger benchmark profile. Laguna M.1 only becomes the better choice if you want the cheaper token bill.
Agentic
+14.5 difference
Coding
+8.3 difference
GPT-5.2
Laguna M.1
$1.75 / $14
$0 / $0
73 t/s
N/A
130.34s
N/A
400K
131K
Pick GPT-5.2 if you want the stronger benchmark profile. Laguna M.1 only becomes the better choice if you want the cheaper token bill.
GPT-5.2 is clearly ahead on the provisional aggregate, 81 to 46. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.2's sharpest advantage is in agentic, where it averages 55.2 against 40.7. The single biggest benchmark swing on the page is SWE-bench Pro, 55.6% to 46.9%.
GPT-5.2 is also the more expensive model on tokens at $1.75 input / $14.00 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. GPT-5.2 gives you the larger context window at 400K, compared with 131K for Laguna M.1.
GPT-5.2 is ahead on BenchLM's provisional leaderboard, 81 to 46. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 55.6% and 46.9%.
GPT-5.2 has the edge for coding in this comparison, averaging 64.7 versus 56.4. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
GPT-5.2 has the edge for agentic tasks in this comparison, averaging 55.2 versus 40.7. Laguna M.1 stays close enough that the answer can still flip depending on your workload.
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