Model comparison
Llama 3.1-8B vs o3
o3 is the stronger model overall, scoring 47.5 to 23.0 on the Noometry Index. Llama 3.1-8B costs 61× less per token, which makes it the better buy when o3's lead doesn't matter for your workload.
Last verified . 33 shared benchmarks.
MetaSummary
- They share 33 benchmarks with published results for both. Llama 3.1-8B scores higher in 0 categories and o3 in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where o3 leads 54.6 to 8.0.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 1.7% for Llama 3.1-8B and 84.4% for o3.
- Llama 3.1-8B is cheaper at $0.05 / $0.08 per million input/output tokens, against $2 / $8 for o3.
- o3 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 | |
|---|---|---|
| Provider | Meta | OpenAI |
| Noometry Index | 23.0 | 47.5 |
| Released | 2024-07-23 | 2025-04-16 |
| Weights | Open | Proprietary |
| Context window | 128K | 200K |
| Max output | 4K | 100K |
| Input $ / M tokens | $0.05 | $2 |
| Output $ / M tokens | $0.08 | $8 |
| Results tracked | 43 | 63 |
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Category by category
Coding o3 leads
Llama 3.1-8B: 20.2 (#340), o3: 46.8 (#64)
| Benchmark | Llama 3.1-8B | o3 |
|---|---|---|
| WeirdML | 1.7% | 52.4% |
| LMArena Coding | 1195 | 1408 |
| SWE-bench Verified | — | 62.3% |
| SWE-bench Verified (bash only) | — | 58.4% |
| Aider Polyglot | — | 81.3% |
| SciCode | 13.2% | — |
| GSO | — | 8.8% |
| BigCodeBench Instruct | 32.8% | — |
| BigCodeBench Complete | 40.5% | — |
| CadEval | — | 74% |
| ALE-Bench | — | 933.55 |
| HumanEval+ | 62.8% | — |
| MBPP+ | 55.6% | — |
Agentic & Tool Use o3 leads
Llama 3.1-8B: 22.5 (#131), o3: 34.5 (#44)
| Benchmark | Llama 3.1-8B | o3 |
|---|---|---|
| Berkeley Function Calling Leaderboard | 25.8% | 63% |
| GDPval | — | 30.8% |
| DeepResearch Bench | — | 45.2% |
| OSWorld | — | 23% |
| BALROG | 15.1% | — |
| LMArena Search | — | 1144 |
| METR Time Horizons | — | 65.4% |
Reasoning o3 leads
Llama 3.1-8B: 14.9 (#321), o3: 32.0 (#78)
| Benchmark | Llama 3.1-8B | o3 |
|---|---|---|
| CritPt | 0% | 1.4% |
| Chess Puzzles | 0% | 38% |
| LMArena Hard Prompts | 1175 | 1402 |
| DTBench | 50.9% | 84.8% |
| LMCA | 5.4% | 39.7% |
| Epoch Capabilities Index | 116.57 | 146.86 |
| ARC-AGI-2 | — | 6.5% |
| SimpleBench | — | 53.1% |
| Kagi LLM Benchmark | — | 67.6% |
| ARC-AGI-1 | — | 60.8% |
| EnigmaEval | — | 13.1% |
| Mystery Game Puzzles | — | 29% |
| ForecastBench | — | 62.5 |
| PIQA | 81.2% | — |
Math o3 leads
Llama 3.1-8B: 10.2 (#317), o3: 50.2 (#58)
| Benchmark | Llama 3.1-8B | o3 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 1.7% | 84.4% |
| Omni-MATH | 13.7% | 71.4% |
| LMArena Math | 1179 | 1426 |
| MATH Level 5 | 22.9% | 97.8% |
| FrontierMath (Tiers 1-3) | — | 33.3% |
| FrontierMath (Feb 2025 set) | — | 18.7% |
| FrontierMath Tier 4 (v1) | — | 2.1% |
| GSM8K | 82.4% | — |
Knowledge o3 leads
Llama 3.1-8B: 8.0 (#307), o3: 54.6 (#52)
| Benchmark | Llama 3.1-8B | o3 |
|---|---|---|
| GPQA Diamond | 27% | 81.8% |
| MMLU-Pro | 40.6% | 85.9% |
| GPQA (HELM) | 24.7% | 75.3% |
| LMArena Expert | 1144 | 1402 |
| Humanity's Last Exam | — | 20.3% |
| SimpleQA Verified | — | 49.4% |
| Confabulations | — | 14.4% |
| BoolQ | 82.8% | — |
| MMLU | 56.1% | — |
Multimodal Not comparable
Llama 3.1-8B: —, o3: 41.4 (#36)
| Benchmark | Llama 3.1-8B | o3 |
|---|---|---|
| LMArena Vision | — | 1214 |
| GeoBench | — | 74% |
| VPCT | — | 52% |
Multilingual o3 leads
Llama 3.1-8B: 34.0 (#249), o3: 51.7 (#105)
| Benchmark | Llama 3.1-8B | o3 |
|---|---|---|
| LMArena Non-English | 1148 | 1401 |
| LMArena Chinese | 1151 | 1437 |
| LMArena French | 1177 | 1430 |
| LMArena German | 1144 | 1420 |
| LMArena Japanese | 1061 | 1403 |
| LMArena Korean | 1053 | 1370 |
| LMArena Russian | 1158 | 1406 |
| LMArena Spanish | 1169 | 1395 |
Instruction Following o3 leads
Llama 3.1-8B: 58.9 (#258), o3: 72.8 (#127)
| Benchmark | Llama 3.1-8B | o3 |
|---|---|---|
| IFEval | 74.3% | 86.9% |
| LMArena Instruction Following | 1159 | 1368 |
Long Context o3 leads
Llama 3.1-8B: 35.8 (#238), o3: 53.3 (#6)
| Benchmark | Llama 3.1-8B | o3 |
|---|---|---|
| LMArena Longer Query | 1182 | 1372 |
| Fiction.LiveBench | — | 88.9% |
| CL-bench | — | 17.8% |
Writing & Preference o3 leads
Llama 3.1-8B: 29.7 (#290), o3: 63.5 (#64)
| Benchmark | Llama 3.1-8B | o3 |
|---|---|---|
| LMArena Text | 1187 | 1410 |
| LMArena Creative Writing | 1154 | 1359 |
| EQ-Bench Creative Writing | 713 | 1676 |
| WildBench | 68.7% | 86.1% |
| LMArena Multi-Turn | 1172 | 1405 |
| Short-Story Creative Writing | — | 83.9% |
Frequently asked questions
Is Llama 3.1-8B better than o3?
o3 is the stronger model overall, scoring 47.5 to 23.0 on the Noometry Index. Llama 3.1-8B costs 61× less per token, which makes it the better buy when o3's lead doesn't matter for your workload.
Which is cheaper, Llama 3.1-8B or o3?
Llama 3.1-8B is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; o3 lists at $2 and $8.
Is Llama 3.1-8B or o3 better for coding?
o3 scores higher on coding benchmarks: 46.8 versus 20.2 in the Noometry coding category.
Which has the bigger context window?
o3 does, with 200K tokens against 128K.
How many benchmarks do Llama 3.1-8B and o3 share?
33 benchmarks have published results for both models. Llama 3.1-8B has 43 scored results on Noometry and o3 has 63.