Model comparison
GPT-4o vs Llama 2-7B
GPT-4o and Llama 2-7B score almost the same on the Noometry Index (28.6 vs 29.1), so choose on price, context window or the category you care about most.
Last verified . 19 shared benchmarks.
MetaSummary
- They share 19 benchmarks with published results for both. GPT-4o scores higher in 5 categories and Llama 2-7B in 3 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GPT-4o leads 52.6 to 28.0.
- The biggest single-benchmark swing is Chess Puzzles: 13% for GPT-4o and 0% for Llama 2-7B.
- Llama 2-7B has downloadable open weights; the other is API-only.
Side by side
| GPT-4o | Llama 2-7B | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 28.6 | 29.1 |
| Released | 2024-05-13 | 2023-07-18 |
| Weights | Proprietary | Open |
| Context window | 128K | — |
| Max output | 16K | — |
| Input $ / M tokens | $2.50 | — |
| Output $ / M tokens | $10 | — |
| Results tracked | 72 | 29 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Llama 2-7B leads
GPT-4o: 24.8 (#328), Llama 2-7B: 29.2 (#307)
| Benchmark | GPT-4o | Llama 2-7B |
|---|---|---|
| LMArena Coding | 1297 | 1002 |
| SWE-bench Verified | 31% | — |
| SWE-bench Verified (bash only) | 21.6% | — |
| Aider Polyglot | 45.3% | — |
| GSO | 0% | — |
| WeirdML | 25.1% | — |
| BigCodeBench Instruct | 51.1% | — |
| LiveBench Coding | 51.4% | — |
| BigCodeBench Complete | 61.1% | — |
| CadEval | 26% | — |
| HumanEval+ | 87.2% | — |
| MBPP+ | 72.2% | — |
Agentic & Tool Use Not comparable
GPT-4o: 21.0 (#141), Llama 2-7B: —
| Benchmark | GPT-4o | Llama 2-7B |
|---|---|---|
| GDPval | 9.9% | — |
| TheAgentCompany | 8.6% | — |
| Cybench | 12.5% | — |
| BALROG | 32.3% | — |
| LMArena Search | 1006 | — |
| METR Time Horizons | 40.8% | — |
Reasoning Llama 2-7B leads
GPT-4o: 9.4 (#343), Llama 2-7B: 15.7 (#312)
| Benchmark | GPT-4o | Llama 2-7B |
|---|---|---|
| Chess Puzzles | 13% | 0% |
| LMArena Hard Prompts | 1281 | 1009 |
| Epoch Capabilities Index | 128.97 | 99.06 |
| ARC-AGI-2 | 0% | — |
| SimpleBench | 17.8% | — |
| ARC-AGI-1 | 4.5% | — |
| CritPt | 0% | — |
| EnigmaEval | 0.8% | — |
| LiveBench Reasoning | 55.8% | — |
| DTBench | 64.5% | — |
| LiveBench Data Analysis | 60.9% | — |
| LMCA | 16.6% | — |
| BIG-Bench Hard | — | 39.2% |
| ForecastBench | 57.7 | — |
| HellaSwag | — | 77.2% |
| LAMBADA | — | 73.3% |
| LiveBench | 55.3% | — |
| PIQA | — | 78.8% |
| WinoGrande | — | 69.2% |
Math Llama 2-7B leads
GPT-4o: 10.6 (#312), Llama 2-7B: 30.7 (#233)
| Benchmark | GPT-4o | Llama 2-7B |
|---|---|---|
| LMArena Math | 1285 | 1042 |
| FrontierMath (Tiers 1-3) | 0.4% | — |
| OTIS Mock AIME 2024-2025 | 6.4% | — |
| Omni-MATH | 29.3% | — |
| LiveBench Math | 49.5% | — |
| MATH Level 5 | 53.3% | — |
| FrontierMath (Feb 2025 set) | 0.3% | — |
| GSM8K | — | 16.7% |
Knowledge Too close to call
GPT-4o: 28.8 (#242), Llama 2-7B: 28.2 (#248)
| Benchmark | GPT-4o | Llama 2-7B |
|---|---|---|
| LMArena Expert | 1250 | 1036 |
| MMLU | 88.1% | 45.8% |
| GPQA Diamond | 49.2% | — |
| Humanity's Last Exam | 2.7% | — |
| SimpleQA Verified | 26% | — |
| MMLU-Pro | 71.3% | — |
| Confabulations | 15.3% | — |
| Vectara Hallucination Rate | 9.6% | — |
| GPQA (HELM) | 52% | — |
| ARC (AI2) Challenge | — | 45.9% |
| BoolQ | — | 77.9% |
| OpenBookQA | — | 58.6% |
| TriviaQA | — | 73.7% |
Multimodal Not comparable
GPT-4o: 34.5 (#91), Llama 2-7B: —
| Benchmark | GPT-4o | Llama 2-7B |
|---|---|---|
| ScienceQA | 88.5% | 43.1% |
| LMArena Vision | 1137 | — |
| Video-MME | 71.9% | — |
| GeoBench | 71% | — |
| VPCT | 40% | — |
Multilingual GPT-4o leads
GPT-4o: 43.2 (#186), Llama 2-7B: 23.8 (#293)
| Benchmark | GPT-4o | Llama 2-7B |
|---|---|---|
| LMArena Non-English | 1283 | 973 |
| LMArena Chinese | 1277 | 973 |
| LMArena French | 1304 | 970 |
| LMArena German | 1282 | 978 |
| LMArena Russian | 1286 | 995 |
| LMArena Spanish | 1292 | 1007 |
| LMArena Japanese | 1257 | — |
| LMArena Korean | 1234 | — |
Instruction Following GPT-4o leads
GPT-4o: 66.6 (#207), Llama 2-7B: 50.8 (#298)
| Benchmark | GPT-4o | Llama 2-7B |
|---|---|---|
| LMArena Instruction Following | 1278 | 1006 |
| LiveBench Instruction Following | 68.6% | — |
| IFEval | 81.7% | — |
Long Context GPT-4o leads
GPT-4o: 39.4 (#179), Llama 2-7B: 30.4 (#287)
| Benchmark | GPT-4o | Llama 2-7B |
|---|---|---|
| LMArena Longer Query | 1289 | 999 |
| Fiction.LiveBench | 66.7% | — |
Writing & Preference GPT-4o leads
GPT-4o: 52.6 (#166), Llama 2-7B: 28.0 (#298)
| Benchmark | GPT-4o | Llama 2-7B |
|---|---|---|
| LMArena Text | 1300 | 1053 |
| LMArena Creative Writing | 1292 | 1033 |
| LMArena Multi-Turn | 1302 | 1029 |
| Short-Story Creative Writing | 81.8% | — |
| WildBench | 82.8% | — |
| LiveBench Language | 47.6% | — |
Frequently asked questions
Is GPT-4o better than Llama 2-7B?
GPT-4o and Llama 2-7B score almost the same on the Noometry Index (28.6 vs 29.1), so choose on price, context window or the category you care about most.
Is GPT-4o or Llama 2-7B better for coding?
Llama 2-7B scores higher on coding benchmarks: 29.2 versus 24.8 in the Noometry coding category.
How many benchmarks do GPT-4o and Llama 2-7B share?
19 benchmarks have published results for both models. GPT-4o has 72 scored results on Noometry and Llama 2-7B has 29.