Model comparison
Llama 2-7B vs o4-mini
o4-mini is the stronger model overall, scoring 41.6 to 29.1 on the Noometry Index.
Last verified . 17 shared benchmarks.
MetaSummary
- They share 17 benchmarks with published results for both. Llama 2-7B scores higher in 0 categories and o4-mini in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where o4-mini leads 54.0 to 28.0.
- The biggest single-benchmark swing is Chess Puzzles: 0% for Llama 2-7B and 26% for o4-mini.
- Llama 2-7B has downloadable open weights; the other is API-only.
Side by side
| Llama 2-7B | o4-mini | |
|---|---|---|
| Provider | Meta | OpenAI |
| Noometry Index | 29.1 | 41.6 |
| Released | 2023-07-18 | 2025-04-16 |
| Weights | Open | Proprietary |
| Context window | — | 200K |
| Max output | — | 100K |
| Input $ / M tokens | — | $1.10 |
| Output $ / M tokens | — | $4.40 |
| Results tracked | 29 | 60 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding o4-mini leads
Llama 2-7B: 29.2 (#307), o4-mini: 40.9 (#127)
| Benchmark | Llama 2-7B | o4-mini |
|---|---|---|
| LMArena Coding | 1002 | 1368 |
| SWE-bench Verified (bash only) | — | 45% |
| Aider Polyglot | — | 72% |
| GSO | — | 3.6% |
| WeirdML | — | 52.6% |
| CadEval | — | 62% |
| ALE-Bench | — | 826.17 |
| AlgoTune | — | 1.72 |
Agentic & Tool Use Not comparable
Llama 2-7B: —, o4-mini: 32.6 (#61)
| Benchmark | Llama 2-7B | o4-mini |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 53.2% |
| GDPval | — | 25.3% |
| METR Time Horizons | — | 63.9% |
Reasoning o4-mini leads
Llama 2-7B: 15.7 (#312), o4-mini: 24.6 (#162)
| Benchmark | Llama 2-7B | o4-mini |
|---|---|---|
| Chess Puzzles | 0% | 26% |
| LMArena Hard Prompts | 1009 | 1351 |
| Epoch Capabilities Index | 99.06 | 145.64 |
| 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% |
| DTBench | — | 77.6% |
| LMCA | — | 26.5% |
| BIG-Bench Hard | 39.2% | — |
| ForecastBench | — | 61.8 |
| HellaSwag | 77.2% | — |
| LAMBADA | 73.3% | — |
| PIQA | 78.8% | — |
| WinoGrande | 69.2% | — |
Math o4-mini leads
Llama 2-7B: 30.7 (#233), o4-mini: 40.8 (#89)
| Benchmark | Llama 2-7B | o4-mini |
|---|---|---|
| LMArena Math | 1042 | 1389 |
| FrontierMath (Tiers 1-3) | — | 36.1% |
| FrontierMath Tier 4 | — | 4.9% |
| OTIS Mock AIME 2024-2025 | — | 81.7% |
| Omni-MATH | — | 72% |
| MATH Level 5 | — | 97.8% |
| FrontierMath (Feb 2025 set) | — | 24.8% |
| FrontierMath Tier 4 (v1) | — | 6.3% |
| GSM8K | 16.7% | — |
Knowledge o4-mini leads
Llama 2-7B: 28.2 (#248), o4-mini: 43.6 (#91)
| Benchmark | Llama 2-7B | o4-mini |
|---|---|---|
| LMArena Expert | 1036 | 1343 |
| GPQA Diamond | — | 79.6% |
| 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 | 45.9% | — |
| BoolQ | 77.9% | — |
| MMLU | 45.8% | — |
| OpenBookQA | 58.6% | — |
| TriviaQA | 73.7% | — |
Multimodal Not comparable
Llama 2-7B: —, o4-mini: 40.2 (#49)
| Benchmark | Llama 2-7B | o4-mini |
|---|---|---|
| LMArena Vision | — | 1194 |
| GeoBench | — | 64% |
| VPCT | — | 57.5% |
| ScienceQA | 43.1% | — |
Multilingual o4-mini leads
Llama 2-7B: 23.8 (#293), o4-mini: 47.0 (#154)
| Benchmark | Llama 2-7B | o4-mini |
|---|---|---|
| LMArena Non-English | 973 | 1337 |
| LMArena Chinese | 973 | 1354 |
| LMArena French | 970 | 1364 |
| LMArena German | 978 | 1336 |
| LMArena Russian | 995 | 1334 |
| LMArena Spanish | 1007 | 1347 |
| LMArena Japanese | — | 1308 |
| LMArena Korean | — | 1312 |
Instruction Following o4-mini leads
Llama 2-7B: 50.8 (#298), o4-mini: 75.2 (#68)
| Benchmark | Llama 2-7B | o4-mini |
|---|---|---|
| LMArena Instruction Following | 1006 | 1321 |
| IFEval | — | 92.8% |
Long Context o4-mini leads
Llama 2-7B: 30.4 (#287), o4-mini: 45.5 (#33)
| Benchmark | Llama 2-7B | o4-mini |
|---|---|---|
| LMArena Longer Query | 999 | 1315 |
| Fiction.LiveBench | — | 77.8% |
Writing & Preference o4-mini leads
Llama 2-7B: 28.0 (#298), o4-mini: 54.0 (#152)
| Benchmark | Llama 2-7B | o4-mini |
|---|---|---|
| LMArena Text | 1053 | 1353 |
| LMArena Creative Writing | 1033 | 1294 |
| LMArena Multi-Turn | 1029 | 1350 |
| Short-Story Creative Writing | — | 75% |
| WildBench | — | 85.4% |
Frequently asked questions
Is Llama 2-7B better than o4-mini?
o4-mini is the stronger model overall, scoring 41.6 to 29.1 on the Noometry Index.
Is Llama 2-7B or o4-mini better for coding?
o4-mini scores higher on coding benchmarks: 40.9 versus 29.2 in the Noometry coding category.
How many benchmarks do Llama 2-7B and o4-mini share?
17 benchmarks have published results for both models. Llama 2-7B has 29 scored results on Noometry and o4-mini has 60.