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.
- #82 of 106 open models
- 12 boards · updated Nov 2026
- 100 = average open model
- Hardware requirements →
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.
50.9%
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
IndependentEpoch AI ·
23.0%
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
IndependentEpoch AI ·
0.0%
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-reportedEpoch AI ·
61.4%
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-reportedEpoch AI ·
62.6
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-reportedEpoch AI ·
Other results (1)
Leaderboard-only, not part of the llmrun Score
82.2%
82.2%
median 70.1%open leader 89.2%
88th percentile · #7 of 51 open · #13 of 79 all
Lab-reportedEpoch 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.
Llama 3.1 405B Instruct math benchmarks
Symbolic and quantitative problem-solving, from competition math to multi-step calculation.
9.7%
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
IndependentEpoch AI ·
49.8%
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
IndependentEpoch AI ·
Llama 3.1 405B Instruct knowledge benchmarks
Breadth and depth of factual knowledge across many subjects, paired with the reasoning to apply it.
73.3%
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
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.
1335.5
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
ArenaArena ·
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.
| Board | Llama 3.1 405B Instructthis model | Qwen3.8 27Bbest in classQ4_K_M · 18 GB | Mistral Large 3 675B Instruct 2512nearest by ScoreQ4_K_M · 454 GB | DeepSeek v3nearest by ScoreQ4_K_M · 412 GB | Llama 4 Maverick 17B 128E Instructnearest by ScoreQ4_K_M · 273 GB |
|---|---|---|---|---|---|
| llmrun Score | 83.5 | 122.0 | 89.6 | 89.0 | 87.1 |
| GPQA Diamond | 50.9% | — | — | 56.5% | 67.0% |
| SimpleBench | 23.0% | — | — | 18.9% | 27.7% |
| CritPt | 0.0% | 5.4% | 0.0% | 0.0% | 0.0% |
| DTBench | 61.4% | 88.0% | 65.1% | — | 61.9% |
| ForecastBench | 62.6 | — | — | 62.6 | — |
| WeirdML | 21.4% | — | — | — | 24.5% |
| AIME 2024/2025 | 9.7% | — | — | 15.8% | 20.6% |
| MATH Level 5 | 49.8% | — | — | 64.8% | 73.0% |
| MMLU-Pro | 73.3% | — | — | 75.9% | 80.5% |
| LMArena Text | 1335.5 | 1435.6 | 1414.3 | 1358.4 | 1327.0 |
| Compare head to head | Compare | Compare | Compare | Compare |
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.