Llama 3.2 1B Instruct — Benchmarks
Benchmark scores for Llama 3.2 1B Instruct aggregated from public leaderboards (4 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 Llama 3.2 1B Instruct: 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.
Llama 3.2 1B Instruct reasoning benchmarks
Multi-step logic and problem-solving — can the model think through a hard problem rather than recall an answer.
Llama 3.2 1B Instruct math benchmarks
Symbolic and quantitative problem-solving, from competition math to multi-step calculation.
0.6%
0.6%
median 52.8%open leader 98.6%
Overall best: Claude Fable 5 (high) 100.0%
4th percentile · #83 of 86 open · #280 of 283 all
IndependentEpoch AI ·
Llama 3.2 1B Instruct instruction following benchmarks
How precisely a model obeys explicit, verifiable instructions — format, length, and content constraints — and, more broadly, how much people prefer its responses head-to-head in blind human voting.
1111.2
1111.2
median 1321.5open leader 1487.1
Overall best: Gemini 4 Argon (High) 1523.9
3rd percentile · #181 of 187 open · #387 of 405 all
ArenaArena ·
Scores aggregated from public benchmark sources (Epoch AI: 3 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.