Model comparison
Llama 3.1-8B vs o3-pro
o3-pro is the stronger model overall, scoring 42.9 to 23.0 on the Noometry Index. Llama 3.1-8B costs 609× less per token, which makes it the better buy when o3-pro's lead doesn't matter for your workload.
Last verified . 4 shared benchmarks.
MetaSummary
- They share 4 benchmarks with published results for both. Llama 3.1-8B scores higher in 0 categories and o3-pro in 5 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in long context, where o3-pro leads 72.2 to 35.8.
- The biggest single-benchmark swing is WeirdML: 1.7% for Llama 3.1-8B and 58.2% for o3-pro.
- Llama 3.1-8B is cheaper at $0.05 / $0.08 per million input/output tokens, against $20 / $80 for o3-pro.
- o3-pro 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-pro | |
|---|---|---|
| Provider | Meta | OpenAI |
| Noometry Index | 23.0 | 42.9 |
| Released | 2024-07-23 | 2025-06-10 |
| Weights | Open | Proprietary |
| Context window | 128K | 200K |
| Max output | 4K | 100K |
| Input $ / M tokens | $0.05 | $20 |
| Output $ / M tokens | $0.08 | $80 |
| Results tracked | 43 | 12 |
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Category by category
Coding o3-pro leads
Llama 3.1-8B: 20.2 (#340), o3-pro: 55.5 (#24)
| Benchmark | Llama 3.1-8B | o3-pro |
|---|---|---|
| WeirdML | 1.7% | 58.2% |
| Aider Polyglot | — | 84.9% |
| SciCode | 13.2% | — |
| BigCodeBench Instruct | 32.8% | — |
| LMArena Coding | 1195 | — |
| BigCodeBench Complete | 40.5% | — |
| HumanEval+ | 62.8% | — |
| MBPP+ | 55.6% | — |
Agentic & Tool Use Not comparable
Llama 3.1-8B: 22.5 (#131), o3-pro: —
| Benchmark | Llama 3.1-8B | o3-pro |
|---|---|---|
| Berkeley Function Calling Leaderboard | 25.8% | — |
| BALROG | 15.1% | — |
Reasoning o3-pro leads
Llama 3.1-8B: 14.9 (#321), o3-pro: 23.8 (#171)
| Benchmark | Llama 3.1-8B | o3-pro |
|---|---|---|
| DTBench | 50.9% | 86.9% |
| LMCA | 5.4% | 38.5% |
| Epoch Capabilities Index | 116.57 | 147.42 |
| ARC-AGI-2 | — | 4.9% |
| Kagi LLM Benchmark | — | 72.1% |
| ARC-AGI-1 | — | 59.3% |
| CritPt | 0% | — |
| Chess Puzzles | 0% | — |
| LMArena Hard Prompts | 1175 | — |
| PIQA | 81.2% | — |
Math Not comparable
Llama 3.1-8B: 10.2 (#317), o3-pro: —
| Benchmark | Llama 3.1-8B | o3-pro |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 1.7% | — |
| Omni-MATH | 13.7% | — |
| LMArena Math | 1179 | — |
| MATH Level 5 | 22.9% | — |
| GSM8K | 82.4% | — |
Knowledge o3-pro leads
Llama 3.1-8B: 8.0 (#307), o3-pro: 29.5 (#238)
| Benchmark | Llama 3.1-8B | o3-pro |
|---|---|---|
| GPQA Diamond | 27% | — |
| MMLU-Pro | 40.6% | — |
| Confabulations | — | 14.2% |
| Vectara Hallucination Rate | — | 23.3% |
| GPQA (HELM) | 24.7% | — |
| LMArena Expert | 1144 | — |
| BoolQ | 82.8% | — |
| MMLU | 56.1% | — |
Multilingual Not comparable
Llama 3.1-8B: 34.0 (#249), o3-pro: —
| Benchmark | Llama 3.1-8B | o3-pro |
|---|---|---|
| LMArena Non-English | 1148 | — |
| LMArena Chinese | 1151 | — |
| LMArena French | 1177 | — |
| LMArena German | 1144 | — |
| LMArena Japanese | 1061 | — |
| LMArena Korean | 1053 | — |
| LMArena Russian | 1158 | — |
| LMArena Spanish | 1169 | — |
Instruction Following Not comparable
Llama 3.1-8B: 58.9 (#258), o3-pro: —
| Benchmark | Llama 3.1-8B | o3-pro |
|---|---|---|
| IFEval | 74.3% | — |
| LMArena Instruction Following | 1159 | — |
Long Context o3-pro leads
Llama 3.1-8B: 35.8 (#238), o3-pro: 72.2 (#1)
| Benchmark | Llama 3.1-8B | o3-pro |
|---|---|---|
| Fiction.LiveBench | — | 97.2% |
| LMArena Longer Query | 1182 | — |
Writing & Preference o3-pro leads
Llama 3.1-8B: 29.7 (#290), o3-pro: 57.1 (#133)
| Benchmark | Llama 3.1-8B | o3-pro |
|---|---|---|
| LMArena Text | 1187 | — |
| LMArena Creative Writing | 1154 | — |
| Short-Story Creative Writing | — | 84.4% |
| EQ-Bench Creative Writing | 713 | — |
| WildBench | 68.7% | — |
| LMArena Multi-Turn | 1172 | — |
Frequently asked questions
Is Llama 3.1-8B better than o3-pro?
o3-pro is the stronger model overall, scoring 42.9 to 23.0 on the Noometry Index. Llama 3.1-8B costs 609× less per token, which makes it the better buy when o3-pro's lead doesn't matter for your workload.
Which is cheaper, Llama 3.1-8B or o3-pro?
Llama 3.1-8B is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; o3-pro lists at $20 and $80.
Is Llama 3.1-8B or o3-pro better for coding?
o3-pro scores higher on coding benchmarks: 55.5 versus 20.2 in the Noometry coding category.
Which has the bigger context window?
o3-pro does, with 200K tokens against 128K.
How many benchmarks do Llama 3.1-8B and o3-pro share?
4 benchmarks have published results for both models. Llama 3.1-8B has 43 scored results on Noometry and o3-pro has 12.