Model comparison
Llama 3-8B vs o4-mini
o4-mini is the stronger model overall, scoring 41.6 to 25.5 on the Noometry Index.
Last verified . 24 shared benchmarks.
MetaSummary
- They share 24 benchmarks with published results for both. Llama 3-8B scores higher in 0 categories and o4-mini in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where o4-mini leads 43.6 to 7.8.
- The biggest single-benchmark swing is MATH Level 5: 6.1% for Llama 3-8B and 97.8% for o4-mini.
- Llama 3-8B has downloadable open weights; the other is API-only.
Side by side
| Llama 3-8B | o4-mini | |
|---|---|---|
| Provider | Meta | OpenAI |
| Noometry Index | 25.5 | 41.6 |
| Released | 2024-04-18 | 2025-04-16 |
| Weights | Open | Proprietary |
| Context window | — | 200K |
| Max output | — | 100K |
| Input $ / M tokens | — | $1.10 |
| Output $ / M tokens | — | $4.40 |
| Results tracked | 34 | 60 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding o4-mini leads
Llama 3-8B: 31.0 (#289), o4-mini: 40.9 (#127)
| Benchmark | Llama 3-8B | o4-mini |
|---|---|---|
| LMArena Coding | 1152 | 1368 |
| SWE-bench Verified (bash only) | — | 45% |
| Aider Polyglot | — | 72% |
| GSO | — | 3.6% |
| WeirdML | — | 52.6% |
| BigCodeBench Instruct | 31.9% | — |
| BigCodeBench Complete | 36.9% | — |
| CadEval | — | 62% |
| ALE-Bench | — | 826.17 |
| AlgoTune | — | 1.72 |
| HumanEval+ | 56.7% | — |
| MBPP+ | 54.8% | — |
Agentic & Tool Use Not comparable
Llama 3-8B: —, o4-mini: 32.6 (#61)
| Benchmark | Llama 3-8B | o4-mini |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 53.2% |
| GDPval | — | 25.3% |
| METR Time Horizons | — | 63.9% |
Reasoning o4-mini leads
Llama 3-8B: 14.3 (#326), o4-mini: 24.6 (#162)
| Benchmark | Llama 3-8B | o4-mini |
|---|---|---|
| Chess Puzzles | 0% | 26% |
| LMArena Hard Prompts | 1133 | 1351 |
| DTBench | 43.9% | 77.6% |
| Epoch Capabilities Index | 116.45 | 145.64 |
| ForecastBench | 58.6 | 61.8 |
| ARC-AGI-2 | — | 6.1% |
| SimpleBench | — | 38.7% |
| Kagi LLM Benchmark | — | 67.6% |
| ARC-AGI-1 | — | 58.7% |
| CritPt | — | 0.6% |
| EnigmaEval | — | 9.2% |
| Mystery Game Puzzles | — | 5% |
| LMCA | — | 26.5% |
| Adversarial NLI | 57.3% | — |
| WinoGrande | 75.7% | — |
Math o4-mini leads
Llama 3-8B: 8.8 (#323), o4-mini: 40.8 (#89)
| Benchmark | Llama 3-8B | o4-mini |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 1.9% | 81.7% |
| LMArena Math | 1151 | 1389 |
| MATH Level 5 | 6.1% | 97.8% |
| FrontierMath (Tiers 1-3) | — | 36.1% |
| FrontierMath Tier 4 | — | 4.9% |
| Omni-MATH | — | 72% |
| FrontierMath (Feb 2025 set) | — | 24.8% |
| FrontierMath Tier 4 (v1) | — | 6.3% |
Knowledge o4-mini leads
Llama 3-8B: 7.8 (#308), o4-mini: 43.6 (#91)
| Benchmark | Llama 3-8B | o4-mini |
|---|---|---|
| GPQA Diamond | 26.1% | 79.6% |
| LMArena Expert | 1113 | 1343 |
| Humanity's Last Exam | — | 18.1% |
| SimpleQA Verified | — | 19.6% |
| MMLU-Pro | — | 82% |
| Confabulations | — | 15.8% |
| Vectara Hallucination Rate | — | 18.6% |
| GPQA (HELM) | — | 73.5% |
| ARC (AI2) Challenge | 82.8% | — |
| MMLU | 68.8% | — |
| OpenBookQA | 82.6% | — |
| TriviaQA | 67.7% | — |
Multimodal Not comparable
Llama 3-8B: —, o4-mini: 40.2 (#49)
| Benchmark | Llama 3-8B | o4-mini |
|---|---|---|
| LMArena Vision | — | 1194 |
| GeoBench | — | 64% |
| VPCT | — | 57.5% |
Multilingual o4-mini leads
Llama 3-8B: 30.8 (#261), o4-mini: 47.0 (#154)
| Benchmark | Llama 3-8B | o4-mini |
|---|---|---|
| LMArena Non-English | 1098 | 1337 |
| LMArena Chinese | 1076 | 1354 |
| LMArena French | 1159 | 1364 |
| LMArena German | 1104 | 1336 |
| LMArena Japanese | 967 | 1308 |
| LMArena Korean | 1004 | 1312 |
| LMArena Russian | 1109 | 1334 |
| LMArena Spanish | 1173 | 1347 |
Instruction Following o4-mini leads
Llama 3-8B: 58.4 (#260), o4-mini: 75.2 (#68)
| Benchmark | Llama 3-8B | o4-mini |
|---|---|---|
| LMArena Instruction Following | 1127 | 1321 |
| IFEval | — | 92.8% |
Long Context o4-mini leads
Llama 3-8B: 34.2 (#251), o4-mini: 45.5 (#33)
| Benchmark | Llama 3-8B | o4-mini |
|---|---|---|
| LMArena Longer Query | 1128 | 1315 |
| Fiction.LiveBench | — | 77.8% |
Writing & Preference o4-mini leads
Llama 3-8B: 37.5 (#256), o4-mini: 54.0 (#152)
| Benchmark | Llama 3-8B | o4-mini |
|---|---|---|
| LMArena Text | 1166 | 1353 |
| LMArena Creative Writing | 1150 | 1294 |
| LMArena Multi-Turn | 1152 | 1350 |
| Short-Story Creative Writing | — | 75% |
| WildBench | — | 85.4% |
Frequently asked questions
Is Llama 3-8B better than o4-mini?
o4-mini is the stronger model overall, scoring 41.6 to 25.5 on the Noometry Index.
Is Llama 3-8B or o4-mini better for coding?
o4-mini scores higher on coding benchmarks: 40.9 versus 31.0 in the Noometry coding category.
How many benchmarks do Llama 3-8B and o4-mini share?
24 benchmarks have published results for both models. Llama 3-8B has 34 scored results on Noometry and o4-mini has 60.