llmrun

Llama 3.1 405B Instruct — Benchmarks

Benchmark scores for Llama 3.1 405B Instruct aggregated from public leaderboards (12 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.

llmrun Composite Score →

83.5

90% range 79.3–87.7 · from 8 boards

Llama 3.1 405B Instruct reasoning benchmarks

Multi-step logic and problem-solving — can the model think through a hard problem rather than recall an answer.

  • GPQA Diamond

    50.9%

    median 55.7%open leader 93.2%

    Overall best: Gemini 4 Argon (high) 96.4%

    45th percentile · #55 of 100 open · #232 of 309 all

    Independent

    Epoch AI ·

  • SimpleBench

    23.0%

    median 40.8%open leader 61.1%

    Overall best: Claude Fable 5 Max 81.9%

    21st percentile · #20 of 25 open · #90 of 101 all

    Independent

    Epoch AI ·

  • CritPt

    0.0%

    median 0.9%open leader 26.6%

    Overall best: Gemini 4 Carbon (high) 36.0%

    19th percentile · #63 of 84 open · #217 of 244 all

    Lab-reported

    Epoch AI ·

  • DTBench

    61.4%

    median 68.7%open leader 93.9%

    Overall best: Claude Opus 5.5 Max 98.9%

    38th percentile · #44 of 70 open · #186 of 235 all

    Lab-reported

    Epoch AI ·

  • ForecastBench

    62.6

    median 61.5open leader 64.7

    Overall best: GPT 5.5 (high) 67.0

    76th percentile · #7 of 24 open · #59 of 92 all

    Lab-reported

    Epoch AI ·

Other results (1)

Leaderboard-only, not part of the llmrun Score

  • WinoGrande

    82.2%

    median 70.1%open leader 89.2%

    88th percentile · #7 of 51 open · #13 of 79 all

    Lab-reported

    Epoch AI ·

Llama 3.1 405B Instruct coding benchmarks

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

  • WeirdML

    21.4%

    median 39.5%open leader 82.6%

    Overall best: GPT 6 Astra Promax 93.6%

    13th percentile · #41 of 47 open · #147 of 162 all

    Independent

    Epoch AI ·

Llama 3.1 405B Instruct math benchmarks

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

  • AIME 2024/2025

    9.7%

    median 54.4%open leader 99.4%

    Overall best: Claude Fable 5 (high) 100.0%

    32nd percentile · #60 of 88 open · #242 of 292 all

    Independent

    Epoch AI ·

  • MATH Level 5

    49.8%

    median 41.6%open leader 96.6%

    Overall best: GPT 5 (Aug 07, 2025, high) 98.1%

    63rd percentile · #15 of 39 open · #64 of 107 all

    Independent

    Epoch AI ·

Llama 3.1 405B Instruct knowledge benchmarks

Breadth and depth of factual knowledge across many subjects, paired with the reasoning to apply it.

  • MMLU-Pro

    73.3%

    median 52.0%open leader 88.0%

    Overall best: Gemini-3.1-Pro 91.2%

    77th percentile · #34 of 146 open · #90 of 257 all

    Independent

    TIGER-Lab

Other results (1)

Leaderboard-only, not part of the llmrun Score

  • MMLU

    84.5%

    median 62.5%open leader 87.2%

    Overall best: GPT 4o (Nov 20, 2024) 88.1%

    94th percentile · #6 of 85 open · #13 of 136 all

    Lab-reported

    Epoch AI ·

Llama 3.1 405B 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.

  • LMArena Text

    1335.5

    median 1321.4open leader 1488.2

    Overall best: Gemini 4 Carbon (High) 1527.5

    54th percentile · #87 of 188 open · #236 of 408 all

    Arena

    Arena ·

Score vs VRAM in the ≤512 GB tier

  • Llama 3.1 405B Instruct
  • Other models
  • Best Score for its VRAM

#78 of 102 that fit ≤512 GB

Each dot is a model at the best quant that fits ≤512 GB. The line steps up to the best Score available at each amount of VRAM; a dot below it is beaten by a model that needs no more memory.

Full ≤512 GB ranking →

Llama 3.1 405B Instruct vs its neighbours

Same boards for the nearest open models per llmrun Score. The highest score in each row is marked.

BoardLlama 3.1 405B Instructthis modelQwen3.8 27Bbest in classQ4_K_M · 18 GBMistral Large 3 675B Instruct 2512nearest by ScoreQ4_K_M · 454 GBDeepSeek v3nearest by ScoreQ4_K_M · 412 GBLlama 4 Maverick 17B 128E Instructnearest by ScoreQ4_K_M · 273 GB
llmrun Score83.5122.089.689.087.1
GPQA Diamond50.9%——56.5%67.0%
SimpleBench23.0%——18.9%27.7%
CritPt0.0%5.4%0.0%0.0%0.0%
DTBench61.4%88.0%65.1%—61.9%
ForecastBench62.6——62.6—
WeirdML21.4%———24.5%
AIME 2024/20259.7%——15.8%20.6%
MATH Level 549.8%——64.8%73.0%
MMLU-Pro73.3%——75.9%80.5%
LMArena Text1335.51435.61414.31358.41327.0
Compare head to headCompareCompareCompareCompare

Scores aggregated from public benchmark sources (Epoch AI: 10 boards, Arena: 1 board, TIGER-Lab: 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.