Model comparison
Llama 3.1-8B vs o3-mini
o3-mini is the stronger model overall, scoring 36.7 to 23.0 on the Noometry Index. Llama 3.1-8B costs 33× less per token, which makes it the better buy when o3-mini'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 1 category and o3-mini in 8 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where o3-mini leads 38.3 to 8.0.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 1.7% for Llama 3.1-8B and 76.9% for o3-mini.
- Llama 3.1-8B is cheaper at $0.05 / $0.08 per million input/output tokens, against $1.10 / $4.40 for o3-mini.
- o3-mini 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 | o3-mini | |
|---|---|---|
| Provider | Meta | OpenAI |
| Noometry Index | 23.0 | 36.7 |
| Released | 2024-07-23 | 2024-12-20 |
| Weights | Open | Proprietary |
| Context window | 128K | 200K |
| Max output | 4K | 100K |
| Input $ / M tokens | $0.05 | $1.10 |
| Output $ / M tokens | $0.08 | $4.40 |
| Results tracked | 43 | 51 |
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Category by category
Coding o3-mini leads
Llama 3.1-8B: 20.2 (#340), o3-mini: 40.8 (#132)
| Benchmark | Llama 3.1-8B | o3-mini |
|---|---|---|
| SciCode | 13.2% | 39.8% |
| WeirdML | 1.7% | 43.7% |
| LMArena Coding | 1195 | 1378 |
| Aider Polyglot | — | 60.4% |
| GSO | — | 1.3% |
| BigCodeBench Instruct | 32.8% | — |
| LiveBench Coding | — | 82.7% |
| BigCodeBench Complete | 40.5% | — |
| CadEval | — | 54% |
| HumanEval+ | 62.8% | — |
| MBPP+ | 55.6% | — |
Agentic & Tool Use o3-mini leads
Llama 3.1-8B: 22.5 (#131), o3-mini: 29.6 (#84)
| Benchmark | Llama 3.1-8B | o3-mini |
|---|---|---|
| Berkeley Function Calling Leaderboard | 25.8% | — |
| Cybench | — | 22.5% |
| BALROG | 15.1% | — |
Reasoning o3-mini leads
Llama 3.1-8B: 14.9 (#321), o3-mini: 16.3 (#305)
| Benchmark | Llama 3.1-8B | o3-mini |
|---|---|---|
| CritPt | 0% | 0.3% |
| Chess Puzzles | 0% | 17% |
| LMArena Hard Prompts | 1175 | 1366 |
| DTBench | 50.9% | 68.8% |
| LMCA | 5.4% | 19% |
| Epoch Capabilities Index | 116.57 | 140.34 |
| ARC-AGI-2 | — | 3% |
| SimpleBench | — | 22.8% |
| ARC-AGI-1 | — | 34.5% |
| LiveBench Reasoning | — | 89.6% |
| Mystery Game Puzzles | — | 7% |
| LiveBench Data Analysis | — | 70.6% |
| ForecastBench | — | 59.6 |
| LiveBench | — | 75.9% |
| PIQA | 81.2% | — |
Math o3-mini leads
Llama 3.1-8B: 10.2 (#317), o3-mini: 28.1 (#244)
| Benchmark | Llama 3.1-8B | o3-mini |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 1.7% | 76.9% |
| LMArena Math | 1179 | 1396 |
| MATH Level 5 | 22.9% | 96.5% |
| FrontierMath (Tiers 1-3) | — | 18.6% |
| FrontierMath Tier 4 | — | 0% |
| Omni-MATH | 13.7% | — |
| LiveBench Math | — | 77.3% |
| FrontierMath (Feb 2025 set) | — | 12.4% |
| FrontierMath Tier 4 (v1) | — | 4.2% |
| GSM8K | 82.4% | — |
Knowledge o3-mini leads
Llama 3.1-8B: 8.0 (#307), o3-mini: 38.3 (#146)
| Benchmark | Llama 3.1-8B | o3-mini |
|---|---|---|
| GPQA Diamond | 27% | 77% |
| LMArena Expert | 1144 | 1364 |
| SimpleQA Verified | — | 15.3% |
| MMLU-Pro | 40.6% | — |
| Confabulations | — | 17.9% |
| GPQA (HELM) | 24.7% | — |
| BoolQ | 82.8% | — |
| MMLU | 56.1% | — |
Multilingual o3-mini leads
Llama 3.1-8B: 34.0 (#249), o3-mini: 45.7 (#164)
| Benchmark | Llama 3.1-8B | o3-mini |
|---|---|---|
| LMArena Non-English | 1148 | 1319 |
| LMArena Chinese | 1151 | 1379 |
| LMArena French | 1177 | 1334 |
| LMArena German | 1144 | 1303 |
| LMArena Japanese | 1061 | 1286 |
| LMArena Korean | 1053 | 1314 |
| LMArena Russian | 1158 | 1304 |
| LMArena Spanish | 1169 | 1321 |
Instruction Following o3-mini leads
Llama 3.1-8B: 58.9 (#258), o3-mini: 75.1 (#72)
| Benchmark | Llama 3.1-8B | o3-mini |
|---|---|---|
| LMArena Instruction Following | 1159 | 1337 |
| LiveBench Instruction Following | — | 84.4% |
| IFEval | 74.3% | — |
Long Context Llama 3.1-8B leads
Llama 3.1-8B: 35.8 (#238), o3-mini: 33.8 (#256)
| Benchmark | Llama 3.1-8B | o3-mini |
|---|---|---|
| LMArena Longer Query | 1182 | 1343 |
| Fiction.LiveBench | — | 50% |
Writing & Preference o3-mini leads
Llama 3.1-8B: 29.7 (#290), o3-mini: 50.3 (#182)
| Benchmark | Llama 3.1-8B | o3-mini |
|---|---|---|
| LMArena Text | 1187 | 1337 |
| LMArena Creative Writing | 1154 | 1286 |
| LMArena Multi-Turn | 1172 | 1320 |
| Short-Story Creative Writing | — | 61.7% |
| EQ-Bench Creative Writing | 713 | — |
| WildBench | 68.7% | — |
| LiveBench Language | — | 50.7% |
Frequently asked questions
Is Llama 3.1-8B better than o3-mini?
o3-mini is the stronger model overall, scoring 36.7 to 23.0 on the Noometry Index. Llama 3.1-8B costs 33× less per token, which makes it the better buy when o3-mini's lead doesn't matter for your workload.
Which is cheaper, Llama 3.1-8B or o3-mini?
Llama 3.1-8B is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; o3-mini lists at $1.10 and $4.40.
Is Llama 3.1-8B or o3-mini better for coding?
o3-mini scores higher on coding benchmarks: 40.8 versus 20.2 in the Noometry coding category.
Which has the bigger context window?
o3-mini does, with 200K tokens against 128K.
How many benchmarks do Llama 3.1-8B and o3-mini share?
27 benchmarks have published results for both models. Llama 3.1-8B has 43 scored results on Noometry and o3-mini has 51.