Model comparison
GPT-4o vs Llama 3.1-8B
GPT-4o is the stronger model overall, scoring 28.6 to 23.0 on the Noometry Index. Llama 3.1-8B costs 76× less per token, which makes it the better buy when GPT-4o's lead doesn't matter for your workload.
Last verified . 37 shared benchmarks.
MetaSummary
- They share 37 benchmarks with published results for both. GPT-4o scores higher in 7 categories and Llama 3.1-8B in 2 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GPT-4o leads 52.6 to 29.7.
- The biggest single-benchmark swing is MMLU-Pro: 71.3% for GPT-4o and 40.6% for Llama 3.1-8B.
- Llama 3.1-8B is cheaper at $0.05 / $0.08 per million input/output tokens, against $2.50 / $10 for GPT-4o.
- Llama 3.1-8B has downloadable open weights; the other is API-only.
Side by side
| GPT-4o | Llama 3.1-8B | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 28.6 | 23.0 |
| Released | 2024-05-13 | 2024-07-23 |
| Weights | Proprietary | Open |
| Context window | 128K | 128K |
| Max output | 16K | 4K |
| Input $ / M tokens | $2.50 | $0.05 |
| Output $ / M tokens | $10 | $0.08 |
| Results tracked | 72 | 43 |
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Category by category
Coding GPT-4o leads
GPT-4o: 24.8 (#328), Llama 3.1-8B: 20.2 (#340)
| Benchmark | GPT-4o | Llama 3.1-8B |
|---|---|---|
| WeirdML | 25.1% | 1.7% |
| BigCodeBench Instruct | 51.1% | 32.8% |
| LMArena Coding | 1297 | 1195 |
| BigCodeBench Complete | 61.1% | 40.5% |
| HumanEval+ | 87.2% | 62.8% |
| MBPP+ | 72.2% | 55.6% |
| SWE-bench Verified | 31% | — |
| SWE-bench Verified (bash only) | 21.6% | — |
| Aider Polyglot | 45.3% | — |
| SciCode | — | 13.2% |
| GSO | 0% | — |
| LiveBench Coding | 51.4% | — |
| CadEval | 26% | — |
Agentic & Tool Use Llama 3.1-8B leads
GPT-4o: 21.0 (#141), Llama 3.1-8B: 22.5 (#131)
| Benchmark | GPT-4o | Llama 3.1-8B |
|---|---|---|
| BALROG | 32.3% | 15.1% |
| Berkeley Function Calling Leaderboard | — | 25.8% |
| GDPval | 9.9% | — |
| TheAgentCompany | 8.6% | — |
| Cybench | 12.5% | — |
| LMArena Search | 1006 | — |
| METR Time Horizons | 40.8% | — |
Reasoning Llama 3.1-8B leads
GPT-4o: 9.4 (#343), Llama 3.1-8B: 14.9 (#321)
| Benchmark | GPT-4o | Llama 3.1-8B |
|---|---|---|
| CritPt | 0% | 0% |
| Chess Puzzles | 13% | 0% |
| LMArena Hard Prompts | 1281 | 1175 |
| DTBench | 64.5% | 50.9% |
| LMCA | 16.6% | 5.4% |
| Epoch Capabilities Index | 128.97 | 116.57 |
| ARC-AGI-2 | 0% | — |
| SimpleBench | 17.8% | — |
| ARC-AGI-1 | 4.5% | — |
| EnigmaEval | 0.8% | — |
| LiveBench Reasoning | 55.8% | — |
| LiveBench Data Analysis | 60.9% | — |
| ForecastBench | 57.7 | — |
| LiveBench | 55.3% | — |
| PIQA | — | 81.2% |
Math Too close to call
GPT-4o: 10.6 (#312), Llama 3.1-8B: 10.2 (#317)
| Benchmark | GPT-4o | Llama 3.1-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 6.4% | 1.7% |
| Omni-MATH | 29.3% | 13.7% |
| LMArena Math | 1285 | 1179 |
| MATH Level 5 | 53.3% | 22.9% |
| FrontierMath (Tiers 1-3) | 0.4% | — |
| LiveBench Math | 49.5% | — |
| FrontierMath (Feb 2025 set) | 0.3% | — |
| GSM8K | — | 82.4% |
Knowledge GPT-4o leads
GPT-4o: 28.8 (#242), Llama 3.1-8B: 8.0 (#307)
| Benchmark | GPT-4o | Llama 3.1-8B |
|---|---|---|
| GPQA Diamond | 49.2% | 27% |
| MMLU-Pro | 71.3% | 40.6% |
| GPQA (HELM) | 52% | 24.7% |
| LMArena Expert | 1250 | 1144 |
| MMLU | 88.1% | 56.1% |
| Humanity's Last Exam | 2.7% | — |
| SimpleQA Verified | 26% | — |
| Confabulations | 15.3% | — |
| Vectara Hallucination Rate | 9.6% | — |
| BoolQ | — | 82.8% |
Multimodal Not comparable
GPT-4o: 34.5 (#91), Llama 3.1-8B: —
| Benchmark | GPT-4o | Llama 3.1-8B |
|---|---|---|
| LMArena Vision | 1137 | — |
| Video-MME | 71.9% | — |
| GeoBench | 71% | — |
| VPCT | 40% | — |
| ScienceQA | 88.5% | — |
Multilingual GPT-4o leads
GPT-4o: 43.2 (#186), Llama 3.1-8B: 34.0 (#249)
| Benchmark | GPT-4o | Llama 3.1-8B |
|---|---|---|
| LMArena Non-English | 1283 | 1148 |
| LMArena Chinese | 1277 | 1151 |
| LMArena French | 1304 | 1177 |
| LMArena German | 1282 | 1144 |
| LMArena Japanese | 1257 | 1061 |
| LMArena Korean | 1234 | 1053 |
| LMArena Russian | 1286 | 1158 |
| LMArena Spanish | 1292 | 1169 |
Instruction Following GPT-4o leads
GPT-4o: 66.6 (#207), Llama 3.1-8B: 58.9 (#258)
| Benchmark | GPT-4o | Llama 3.1-8B |
|---|---|---|
| IFEval | 81.7% | 74.3% |
| LMArena Instruction Following | 1278 | 1159 |
| LiveBench Instruction Following | 68.6% | — |
Long Context GPT-4o leads
GPT-4o: 39.4 (#179), Llama 3.1-8B: 35.8 (#238)
| Benchmark | GPT-4o | Llama 3.1-8B |
|---|---|---|
| LMArena Longer Query | 1289 | 1182 |
| Fiction.LiveBench | 66.7% | — |
Writing & Preference GPT-4o leads
GPT-4o: 52.6 (#166), Llama 3.1-8B: 29.7 (#290)
| Benchmark | GPT-4o | Llama 3.1-8B |
|---|---|---|
| LMArena Text | 1300 | 1187 |
| LMArena Creative Writing | 1292 | 1154 |
| WildBench | 82.8% | 68.7% |
| LMArena Multi-Turn | 1302 | 1172 |
| Short-Story Creative Writing | 81.8% | — |
| EQ-Bench Creative Writing | — | 713 |
| LiveBench Language | 47.6% | — |
Frequently asked questions
Is GPT-4o better than Llama 3.1-8B?
GPT-4o is the stronger model overall, scoring 28.6 to 23.0 on the Noometry Index. Llama 3.1-8B costs 76× less per token, which makes it the better buy when GPT-4o's lead doesn't matter for your workload.
Which is cheaper, GPT-4o or Llama 3.1-8B?
Llama 3.1-8B is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; GPT-4o lists at $2.50 and $10.
Is GPT-4o or Llama 3.1-8B better for coding?
GPT-4o scores higher on coding benchmarks: 24.8 versus 20.2 in the Noometry coding category.
Which has the bigger context window?
Both accept 128K tokens.
How many benchmarks do GPT-4o and Llama 3.1-8B share?
37 benchmarks have published results for both models. GPT-4o has 72 scored results on Noometry and Llama 3.1-8B has 43.