Llama 3.1 8B Instruct — Benchmarks
Benchmark scores for Llama 3.1 8B Instruct aggregated from public leaderboards (15 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.
- #98 of 100 open models
- #34 of 35 that fit 24 GB
- 15 boards · updated Oct 2026
- 100 = average open model
- Hardware requirements →
Llama 3.1 8B Instruct reasoning benchmarks
Multi-step logic and problem-solving — can the model think through a hard problem rather than recall an answer.
27.0%
27.0%
median 54.0%open leader 93.1%
Overall best: Gemini 4 Argon (high) 96.4%
11th percentile · #87 of 98 open · #287 of 300 all
IndependentEpoch AI ·
15.1%
15.1%
median 16.2%open leader 34.9%
Overall best: GPT 6 Astra Max 68.3%
43rd percentile · #9 of 15 open · #33 of 40 all
IndependentEpoch AI ·
0.0%
0.0%
median 1.1%open leader 26.6%
Overall best: GPT 5.6 Sol Max 32.3%
15th percentile · #53 of 66 open · #200 of 219 all
Lab-reportedEpoch AI ·
50.9%
50.9%
median 67.7%open leader 93.9%
Overall best: Claude Opus 5.5 Max 98.9%
14th percentile · #59 of 68 open · #197 of 214 all
Lab-reportedEpoch AI ·
5.4%
5.4%
median 24.7%open leader 55.5%
Overall best: Claude Opus 5.5 Max 68.2%
6th percentile · #51 of 54 open · #173 of 176 all
Lab-reportedEpoch AI ·
Llama 3.1 8B Instruct coding benchmarks
Writing, editing, and fixing real code — measured by whether the resulting program actually runs and passes tests.
Llama 3.1 8B Instruct math benchmarks
Symbolic and quantitative problem-solving, from competition math to multi-step calculation.
1.7%
1.7%
median 52.8%open leader 98.6%
Overall best: Claude Fable 5 (high) 100.0%
11th percentile · #77 of 86 open · #271 of 283 all
IndependentEpoch AI ·
22.9%
22.9%
median 41.6%open leader 96.6%
Overall best: GPT 5 (Aug 07, 2025, high) 98.1%
29th percentile · #28 of 39 open · #86 of 107 all
IndependentEpoch AI ·
Llama 3.1 8B Instruct knowledge benchmarks
Breadth and depth of factual knowledge across many subjects, paired with the reasoning to apply it.
44.3%
44.3%
median 52.1%open leader 88.0%
Overall best: Gemini-3.1-Pro 91.2%
40th percentile · #88 of 145 open · #181 of 257 all
Independent
Other results (2)· leaderboard-only, not part of the llmrun Score
Llama 3.1 8B 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.
1211.2
1211.2
median 1321.5open leader 1487.1
Overall best: Gemini 4 Argon (High) 1523.9
24th percentile · #142 of 187 open · #333 of 405 all
ArenaArena ·
Score vs VRAM in the 24 GB tier
- Llama 3.1 8B Instruct
- Other models
- Best Score for its VRAM
#34 of 35 that fit 24 GB
Each dot is a model at the best quant that fits a 24 GB GPU. The line joins the models with the best Score for their VRAM; a dot below it is beaten by a model that needs no more memory.
Full 24 GB ranking →Llama 3.1 8B Instruct vs its neighbours
Same boards for the nearest open models per llmrun Score and the best model that fits 24 GB. The highest score in each row is marked.
| Board | Llama 3.1 8B Instructthis model | Qwen3.8 27Bbest in classQ4_K_M · 18 GB | Qwen2.5 7B Instructnearest by ScoreQ4_K_M · 5.8 GB | Gemma 3 4B ITnearest by ScoreQ4_K_M · 5.0 GB | Meta Llama 3 8B Instructnearest by ScoreQ4_K_M · 7.9 GB |
|---|---|---|---|---|---|
| llmrun Score | 62.5 | 122.1 | 65.1 | 64.0 | 61.7 |
| GPQA Diamond | 27.0% | — | 35.5% | 23.2% | 26.1% |
| BALROG | 15.1% | — | 7.8% | — | — |
| CritPt | 0.0% | 5.4% | — | — | — |
| DTBench | 50.9% | 88.0% | 47.7% | 50.9% | 43.9% |
| LMCA | 5.4% | 41.4% | 6.4% | 2.8% | — |
| SciCode | 13.2% | 46.6% | — | — | — |
| AIME 2024/2025 | 1.7% | — | 2.5% | 7.5% | 1.9% |
| MATH Level 5 | 22.9% | — | — | — | 6.1% |
| MMLU-Pro | 44.3% | — | — | 43.6% | 41.0% |
| LMArena Text | 1211.2 | 1437.8 | — | 1303.3 | 1223.7 |
| Compare head to head | Compare | Compare | Compare | Compare |
Scores aggregated from public benchmark sources (Epoch AI: 13 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.