Model comparison
Llama 3-8B vs o3
o3 is the stronger model overall, scoring 47.4 to 25.3 on the Noometry Index. Llama 3-8B costs 25× less per token, which makes it the better buy when o3's lead doesn't matter for your workload.
Last verified . 24 shared benchmarks.
MetaSummary
- They share 24 benchmarks with published results for both. Llama 3-8B scores higher in 0 categories and o3 in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where o3 leads 54.5 to 7.8.
- The biggest single-benchmark swing is MATH Level 5: 6.1% for Llama 3-8B and 97.8% for o3.
- Llama 3-8B is cheaper at $0.14 / $0.14 per million input/output tokens, against $2 / $8 for o3.
- o3 accepts more context: 200K tokens versus 8K.
- Llama 3-8B has downloadable open weights; the other is API-only.
Side by side
| Llama 3-8B | o3 | |
|---|---|---|
| Provider | Meta | OpenAI |
| Noometry Index | 25.3 | 47.4 |
| Released | 2024-04-18 | 2025-04-16 |
| Weights | Open | Proprietary |
| Context window | 8K | 200K |
| Max output | 8K | 100K |
| Input $ / M tokens | $0.14 | $2 |
| Output $ / M tokens | $0.14 | $8 |
| Results tracked | 34 | 63 |
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Category by category
Coding o3 leads
Llama 3-8B: 30.5 (#285), o3: 47.3 (#57)
| Benchmark | Llama 3-8B | o3 |
|---|---|---|
| LMArena Coding | 1152 | 1408 |
| SWE-bench Verified | — | 62.3% |
| SWE-bench Verified (bash only) | — | 58.4% |
| Aider Polyglot | — | 81.3% |
| GSO | — | 8.8% |
| WeirdML | — | 52.4% |
| BigCodeBench Instruct | 31.9% | — |
| BigCodeBench Complete | 36.9% | — |
| CadEval | — | 74% |
| ALE-Bench | — | 933.55 |
| HumanEval+ | 56.7% | — |
| MBPP+ | 54.8% | — |
Agentic & Tool Use Not comparable
Llama 3-8B: —, o3: 34.3 (#42)
| Benchmark | Llama 3-8B | o3 |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 63% |
| GDPval | — | 30.8% |
| DeepResearch Bench | — | 45.2% |
| OSWorld | — | 23% |
| LMArena Search | — | 1144 |
| METR Time Horizons | — | 65.4% |
Reasoning o3 leads
Llama 3-8B: 13.8 (#321), o3: 32.1 (#66)
| Benchmark | Llama 3-8B | o3 |
|---|---|---|
| Chess Puzzles | 0% | 38% |
| LMArena Hard Prompts | 1133 | 1402 |
| DTBench | 43.9% | 84.8% |
| Epoch Capabilities Index | 116.33 | 146.88 |
| ForecastBench | 58.6 | 62.5 |
| ARC-AGI-2 | — | 6.5% |
| SimpleBench | — | 53.1% |
| Kagi LLM Benchmark | — | 67.6% |
| ARC-AGI-1 | — | 60.8% |
| CritPt | — | 1.4% |
| EnigmaEval | — | 13.1% |
| Mystery Game Puzzles | — | 29% |
| LMCA | — | 39.7% |
| Adversarial NLI | 57.3% | — |
| WinoGrande | 75.7% | — |
Math o3 leads
Llama 3-8B: 8.3 (#317), o3: 48.9 (#49)
| Benchmark | Llama 3-8B | o3 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 1.9% | 84.4% |
| LMArena Math | 1151 | 1426 |
| MATH Level 5 | 6.1% | 97.8% |
| FrontierMath (Tiers 1-3) | — | 33.3% |
| Omni-MATH | — | 71.4% |
| FrontierMath (Feb 2025 set) | — | 18.7% |
| FrontierMath Tier 4 (v1) | — | 2.1% |
Knowledge o3 leads
Llama 3-8B: 7.8 (#303), o3: 54.5 (#47)
| Benchmark | Llama 3-8B | o3 |
|---|---|---|
| GPQA Diamond | 26.1% | 81.8% |
| LMArena Expert | 1113 | 1403 |
| Humanity's Last Exam | — | 20.3% |
| SimpleQA Verified | — | 49.4% |
| MMLU-Pro | — | 85.9% |
| Confabulations | — | 14.4% |
| GPQA (HELM) | — | 75.3% |
| ARC (AI2) Challenge | 82.8% | — |
| MMLU | 68.8% | — |
| OpenBookQA | 82.6% | — |
| TriviaQA | 67.7% | — |
Multimodal Not comparable
Llama 3-8B: —, o3: 40.7 (#35)
| Benchmark | Llama 3-8B | o3 |
|---|---|---|
| LMArena Vision | — | 1214 |
| GeoBench | — | 74% |
| VPCT | — | 52% |
Multilingual o3 leads
Llama 3-8B: 31.0 (#256), o3: 51.9 (#99)
| Benchmark | Llama 3-8B | o3 |
|---|---|---|
| LMArena Non-English | 1098 | 1402 |
| LMArena Chinese | 1076 | 1438 |
| LMArena French | 1158 | 1430 |
| LMArena German | 1104 | 1420 |
| LMArena Japanese | 967 | 1404 |
| LMArena Korean | 1003 | 1371 |
| LMArena Russian | 1109 | 1407 |
| LMArena Spanish | 1172 | 1395 |
Instruction Following o3 leads
Llama 3-8B: 58.7 (#255), o3: 73.0 (#122)
| Benchmark | Llama 3-8B | o3 |
|---|---|---|
| LMArena Instruction Following | 1127 | 1368 |
| IFEval | — | 86.9% |
Long Context o3 leads
Llama 3-8B: 34.2 (#246), o3: 53.2 (#6)
| Benchmark | Llama 3-8B | o3 |
|---|---|---|
| LMArena Longer Query | 1128 | 1372 |
| Fiction.LiveBench | — | 88.9% |
| CL-bench | — | 17.8% |
Writing & Preference o3 leads
Llama 3-8B: 37.7 (#251), o3: 63.7 (#62)
| Benchmark | Llama 3-8B | o3 |
|---|---|---|
| LMArena Text | 1166 | 1410 |
| LMArena Creative Writing | 1150 | 1359 |
| LMArena Multi-Turn | 1152 | 1405 |
| Short-Story Creative Writing | — | 83.9% |
| EQ-Bench Creative Writing | — | 1676 |
| WildBench | — | 86.1% |
Frequently asked questions
Is Llama 3-8B better than o3?
o3 is the stronger model overall, scoring 47.4 to 25.3 on the Noometry Index. Llama 3-8B costs 25× less per token, which makes it the better buy when o3's lead doesn't matter for your workload.
Which is cheaper, Llama 3-8B or o3?
Llama 3-8B is cheaper. It lists at $0.14 per million input tokens and $0.14 per million output tokens; o3 lists at $2 and $8.
Is Llama 3-8B or o3 better for coding?
o3 scores higher on coding benchmarks: 47.3 versus 30.5 in the Noometry coding category.
Which has the bigger context window?
o3 does, with 200K tokens against 8K.
How many benchmarks do Llama 3-8B and o3 share?
24 benchmarks have published results for both models. Llama 3-8B has 34 scored results on Noometry and o3 has 63.