Laguna M.1 — Benchmarks

Benchmark scores for Laguna M.1 aggregated from public leaderboards (3 boards), with how it ranks among open models and how it compares with its nearest neighbours. See hardware requirements for what you need to run it.

No llmrun Composite Score for Laguna M.1: the score is only published for a model measured on at least 4 of the boards in the score panel, spanning at least 2 benchmark categories and including a reasoning or coding board — or on 3 boards that narrow its score to a 90% range of at most 15.6 points. Either way the results must come from at least two different benchmarks. Its individual scores below stand on their own — see how the llmrun Composite Score gets calculated.

Laguna M.1 coding benchmarks

Writing, editing, and fixing real code — measured by whether the resulting program actually runs and passes tests.

Surface Evolver Bench
Surface Evolver Bench tests whether a model can write a correct simulation file for Surface Evolver, a physics tool that models surfaces shaped by tension, gravity and volume constraints, defining a 3D structure in its domain-specific format from only a few examples. Scores compare the resulting geometry — volume, area and energy — against reference solutions; the benchmark was built by an independent developer (GitHub: yhenon).
15.6%
15.6%
median 45.0%open leader 95.0%
lowest · #13 of 13 open · #28 of 28 all
#13 of 13
#28 of 28
Lab-reported
Epoch AI · Apr 2026

Other results (1)· leaderboard-only, not part of the llmrun Score

LMArena WebDev
Code Arena (formerly WebDev Arena) is Arena's head-to-head coding leaderboard: two models each build a real web app or UI from the same prompt, and people vote blind on which result is better. The rating shown is a relative Bradley-Terry score, not a percentage — compare it only to other models on this board.
1349.7
1349.7
median 1411.4open leader 1655.2
Overall best: Claude Opus 5.5 (Max) 1807.3
25th percentile · #39 of 52 open · #110 of 138 all
#39 of 52
#110 of 138
Arena
Arena · Oct 2026

Laguna M.1 math benchmarks

Symbolic and quantitative problem-solving, from competition math to multi-step calculation.

ProofBench
ProofBench evaluates a model's ability to write and check rigorous mathematical proofs, rather than just produce a final numeric answer. It probes step-by-step logical correctness, which is a harder and more distinct skill than solving for a number.
0.0%
0.0%
median 17.0%open leader 87.0%
Overall best: Claude Fable 5.1 Max 100.0%
4th percentile · #24 of 26 open · #80 of 82 all
#24 of 26
#80 of 82
Lab-reported
Epoch AI · Apr 2026

Scores aggregated from public benchmark sources (Epoch AI: 2 boards, Arena: 1 board) — each linked from the benchmark pages. llmrun does not run these benchmarks. Percentiles and ranks are among open models in the llmrun catalogue; the median and leader marks refer to the same field.