Model comparison
Llama 3.1-8B vs o1
o1 is the stronger model overall, scoring 40.9 to 23.0 on the Noometry Index. Llama 3.1-8B costs 457× less per token, which makes it the better buy when o1's lead doesn't matter for your workload.
Last verified . 27 shared benchmarks.
MetaSummary
- They share 27 benchmarks with published results for both. Llama 3.1-8B scores higher in 0 categories and o1 in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where o1 leads 41.5 to 8.0.
- The biggest single-benchmark swing is MATH Level 5: 22.9% for Llama 3.1-8B and 94.7% for o1.
- Llama 3.1-8B is cheaper at $0.05 / $0.08 per million input/output tokens, against $15 / $60 for o1.
- o1 accepts more context: 200K tokens versus 128K.
- Llama 3.1-8B has downloadable open weights; the other is API-only.
Side by side
| Llama 3.1-8B | o1 | |
|---|---|---|
| Provider | Meta | OpenAI |
| Noometry Index | 23.0 | 40.9 |
| Released | 2024-07-23 | 2024-09-12 |
| Weights | Open | Proprietary |
| Context window | 128K | 200K |
| Max output | 4K | 100K |
| Input $ / M tokens | $0.05 | $15 |
| Output $ / M tokens | $0.08 | $60 |
| Results tracked | 43 | 52 |
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Category by category
Coding o1 leads
Llama 3.1-8B: 20.2 (#340), o1: 46.1 (#70)
| Benchmark | Llama 3.1-8B | o1 |
|---|---|---|
| WeirdML | 1.7% | 47.6% |
| LMArena Coding | 1195 | 1367 |
| HumanEval+ | 62.8% | 89% |
| MBPP+ | 55.6% | 80.2% |
| Aider Polyglot | — | 61.7% |
| SciCode | 13.2% | — |
| BigCodeBench Instruct | 32.8% | — |
| LiveBench Coding | — | 69.7% |
| BigCodeBench Complete | 40.5% | — |
| CadEval | — | 56% |
Agentic & Tool Use o1 leads
Llama 3.1-8B: 22.5 (#131), o1: 24.6 (#117)
| Benchmark | Llama 3.1-8B | o1 |
|---|---|---|
| Berkeley Function Calling Leaderboard | 25.8% | — |
| Cybench | — | 10% |
| BALROG | 15.1% | — |
| METR Time Horizons | — | 51.1% |
Reasoning o1 leads
Llama 3.1-8B: 14.9 (#321), o1: 27.9 (#111)
| Benchmark | Llama 3.1-8B | o1 |
|---|---|---|
| Chess Puzzles | 0% | 15% |
| LMArena Hard Prompts | 1175 | 1371 |
| DTBench | 50.9% | 74.7% |
| LMCA | 5.4% | 22.3% |
| Epoch Capabilities Index | 116.57 | 141.91 |
| SimpleBench | — | 41.7% |
| ARC-AGI-1 | — | 30.7% |
| CritPt | 0% | — |
| EnigmaEval | — | 5.7% |
| LiveBench Reasoning | — | 91.6% |
| LiveBench Data Analysis | — | 65.5% |
| LiveBench | — | 75.7% |
| PIQA | 81.2% | — |
Math o1 leads
Llama 3.1-8B: 10.2 (#317), o1: 36.1 (#175)
| Benchmark | Llama 3.1-8B | o1 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 1.7% | 73.3% |
| LMArena Math | 1179 | 1388 |
| MATH Level 5 | 22.9% | 94.7% |
| FrontierMath (Tiers 1-3) | — | 14.7% |
| Omni-MATH | 13.7% | — |
| LiveBench Math | — | 80.3% |
| FrontierMath (Feb 2025 set) | — | 9.3% |
| GSM8K | 82.4% | — |
Knowledge o1 leads
Llama 3.1-8B: 8.0 (#307), o1: 41.5 (#110)
| Benchmark | Llama 3.1-8B | o1 |
|---|---|---|
| GPQA Diamond | 27% | 76.8% |
| LMArena Expert | 1144 | 1361 |
| Humanity's Last Exam | — | 8% |
| SimpleQA Verified | — | 41.1% |
| MMLU-Pro | 40.6% | — |
| Confabulations | — | 11.7% |
| GPQA (HELM) | 24.7% | — |
| BoolQ | 82.8% | — |
| MMLU | 56.1% | — |
Multimodal Not comparable
Llama 3.1-8B: —, o1: 34.2 (#93)
| Benchmark | Llama 3.1-8B | o1 |
|---|---|---|
| LMArena Vision | — | 1168 |
| GeoBench | — | 80% |
| VPCT | — | 37% |
| SpatialViz-Bench | — | 41.4% |
Multilingual o1 leads
Llama 3.1-8B: 34.0 (#249), o1: 48.6 (#142)
| Benchmark | Llama 3.1-8B | o1 |
|---|---|---|
| LMArena Non-English | 1148 | 1358 |
| LMArena Chinese | 1151 | 1394 |
| LMArena French | 1177 | 1344 |
| LMArena German | 1144 | 1337 |
| LMArena Japanese | 1061 | 1346 |
| LMArena Korean | 1053 | 1396 |
| LMArena Russian | 1158 | 1356 |
| LMArena Spanish | 1169 | 1345 |
Instruction Following o1 leads
Llama 3.1-8B: 58.9 (#258), o1: 74.8 (#86)
| Benchmark | Llama 3.1-8B | o1 |
|---|---|---|
| LMArena Instruction Following | 1159 | 1367 |
| LiveBench Instruction Following | — | 81.5% |
| IFEval | 74.3% | — |
Long Context o1 leads
Llama 3.1-8B: 35.8 (#238), o1: 50.3 (#9)
| Benchmark | Llama 3.1-8B | o1 |
|---|---|---|
| LMArena Longer Query | 1182 | 1378 |
| Fiction.LiveBench | — | 83.3% |
Writing & Preference o1 leads
Llama 3.1-8B: 29.7 (#290), o1: 55.6 (#144)
| Benchmark | Llama 3.1-8B | o1 |
|---|---|---|
| LMArena Text | 1187 | 1366 |
| LMArena Creative Writing | 1154 | 1348 |
| LMArena Multi-Turn | 1172 | 1369 |
| Short-Story Creative Writing | — | 70.2% |
| EQ-Bench Creative Writing | 713 | — |
| WildBench | 68.7% | — |
| LiveBench Language | — | 65.4% |
Frequently asked questions
Is Llama 3.1-8B better than o1?
o1 is the stronger model overall, scoring 40.9 to 23.0 on the Noometry Index. Llama 3.1-8B costs 457× less per token, which makes it the better buy when o1's lead doesn't matter for your workload.
Which is cheaper, Llama 3.1-8B or o1?
Llama 3.1-8B is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; o1 lists at $15 and $60.
Is Llama 3.1-8B or o1 better for coding?
o1 scores higher on coding benchmarks: 46.1 versus 20.2 in the Noometry coding category.
Which has the bigger context window?
o1 does, with 200K tokens against 128K.
How many benchmarks do Llama 3.1-8B and o1 share?
27 benchmarks have published results for both models. Llama 3.1-8B has 43 scored results on Noometry and o1 has 52.