Model comparison
Llama 3.1-70B vs o3
o3 is the stronger model overall, scoring 47.5 to 29.6 on the Noometry Index. Llama 3.1-70B costs 8.8× less per token, which makes it the better buy when o3's lead doesn't matter for your workload.
Last verified . 30 shared benchmarks.
MetaSummary
- They share 30 benchmarks with published results for both. Llama 3.1-70B scores higher in 0 categories and o3 in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where o3 leads 50.2 to 13.5.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 3.6% for Llama 3.1-70B and 84.4% for o3.
- Llama 3.1-70B is cheaper at $0.40 / $0.40 per million input/output tokens, against $2 / $8 for o3.
- o3 accepts more context: 200K tokens versus 128K.
- Llama 3.1-70B has downloadable open weights; the other is API-only.
Side by side
| Llama 3.1-70B | o3 | |
|---|---|---|
| Provider | Meta | OpenAI |
| Noometry Index | 29.6 | 47.5 |
| Released | 2024-07-23 | 2025-04-16 |
| Weights | Open | Proprietary |
| Context window | 128K | 200K |
| Max output | 4K | 100K |
| Input $ / M tokens | $0.40 | $2 |
| Output $ / M tokens | $0.40 | $8 |
| Results tracked | 35 | 63 |
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Category by category
Coding o3 leads
Llama 3.1-70B: 30.3 (#296), o3: 46.8 (#64)
| Benchmark | Llama 3.1-70B | o3 |
|---|---|---|
| WeirdML | 9% | 52.4% |
| LMArena Coding | 1260 | 1408 |
| SWE-bench Verified | — | 62.3% |
| SWE-bench Verified (bash only) | — | 58.4% |
| Aider Polyglot | — | 81.3% |
| GSO | — | 8.8% |
| BigCodeBench Instruct | 46.1% | — |
| BigCodeBench Complete | 54.8% | — |
| CadEval | — | 74% |
| ALE-Bench | — | 933.55 |
Agentic & Tool Use o3 leads
Llama 3.1-70B: 25.1 (#112), o3: 34.5 (#44)
| Benchmark | Llama 3.1-70B | o3 |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 63% |
| GDPval | — | 30.8% |
| TheAgentCompany | 6.9% | — |
| DeepResearch Bench | — | 45.2% |
| OSWorld | — | 23% |
| BALROG | 27.9% | — |
| LMArena Search | — | 1144 |
| METR Time Horizons | — | 65.4% |
Reasoning o3 leads
Llama 3.1-70B: 21.6 (#220), o3: 32.0 (#78)
| Benchmark | Llama 3.1-70B | o3 |
|---|---|---|
| LMArena Hard Prompts | 1241 | 1402 |
| DTBench | 60% | 84.8% |
| LMCA | 14.8% | 39.7% |
| Epoch Capabilities Index | 125.92 | 146.86 |
| ARC-AGI-2 | — | 6.5% |
| SimpleBench | — | 53.1% |
| Kagi LLM Benchmark | — | 67.6% |
| ARC-AGI-1 | — | 60.8% |
| CritPt | — | 1.4% |
| Chess Puzzles | — | 38% |
| EnigmaEval | — | 13.1% |
| Mystery Game Puzzles | — | 29% |
| ForecastBench | — | 62.5 |
Math o3 leads
Llama 3.1-70B: 13.5 (#304), o3: 50.2 (#58)
| Benchmark | Llama 3.1-70B | o3 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 3.6% | 84.4% |
| Omni-MATH | 21% | 71.4% |
| LMArena Math | 1252 | 1426 |
| MATH Level 5 | 36.7% | 97.8% |
| FrontierMath (Tiers 1-3) | — | 33.3% |
| FrontierMath (Feb 2025 set) | — | 18.7% |
| FrontierMath Tier 4 (v1) | — | 2.1% |
Knowledge o3 leads
Llama 3.1-70B: 24.2 (#269), o3: 54.6 (#52)
| Benchmark | Llama 3.1-70B | o3 |
|---|---|---|
| GPQA Diamond | 44.2% | 81.8% |
| MMLU-Pro | 65.3% | 85.9% |
| GPQA (HELM) | 42.6% | 75.3% |
| LMArena Expert | 1209 | 1402 |
| Humanity's Last Exam | — | 20.3% |
| SimpleQA Verified | — | 49.4% |
| Confabulations | — | 14.4% |
| MMLU | 80.1% | — |
Multimodal Not comparable
Llama 3.1-70B: —, o3: 41.4 (#36)
| Benchmark | Llama 3.1-70B | o3 |
|---|---|---|
| LMArena Vision | — | 1214 |
| GeoBench | — | 74% |
| VPCT | — | 52% |
Multilingual o3 leads
Llama 3.1-70B: 38.8 (#225), o3: 51.7 (#105)
| Benchmark | Llama 3.1-70B | o3 |
|---|---|---|
| LMArena Non-English | 1219 | 1401 |
| LMArena Chinese | 1215 | 1437 |
| LMArena French | 1261 | 1430 |
| LMArena German | 1222 | 1420 |
| LMArena Japanese | 1132 | 1403 |
| LMArena Korean | 1140 | 1370 |
| LMArena Russian | 1234 | 1406 |
| LMArena Spanish | 1253 | 1395 |
Instruction Following o3 leads
Llama 3.1-70B: 65.3 (#223), o3: 72.8 (#127)
| Benchmark | Llama 3.1-70B | o3 |
|---|---|---|
| IFEval | 82.1% | 86.9% |
| LMArena Instruction Following | 1231 | 1368 |
Long Context o3 leads
Llama 3.1-70B: 37.6 (#214), o3: 53.3 (#6)
| Benchmark | Llama 3.1-70B | o3 |
|---|---|---|
| LMArena Longer Query | 1241 | 1372 |
| Fiction.LiveBench | — | 88.9% |
| CL-bench | — | 17.8% |
Writing & Preference o3 leads
Llama 3.1-70B: 35.4 (#267), o3: 63.5 (#64)
| Benchmark | Llama 3.1-70B | o3 |
|---|---|---|
| LMArena Text | 1261 | 1410 |
| LMArena Creative Writing | 1232 | 1359 |
| EQ-Bench Creative Writing | 784 | 1676 |
| WildBench | 75.8% | 86.1% |
| LMArena Multi-Turn | 1256 | 1405 |
| Short-Story Creative Writing | — | 83.9% |
Frequently asked questions
Is Llama 3.1-70B better than o3?
o3 is the stronger model overall, scoring 47.5 to 29.6 on the Noometry Index. Llama 3.1-70B costs 8.8× less per token, which makes it the better buy when o3's lead doesn't matter for your workload.
Which is cheaper, Llama 3.1-70B or o3?
Llama 3.1-70B is cheaper. It lists at $0.40 per million input tokens and $0.40 per million output tokens; o3 lists at $2 and $8.
Is Llama 3.1-70B or o3 better for coding?
o3 scores higher on coding benchmarks: 46.8 versus 30.3 in the Noometry coding category.
Which has the bigger context window?
o3 does, with 200K tokens against 128K.
How many benchmarks do Llama 3.1-70B and o3 share?
30 benchmarks have published results for both models. Llama 3.1-70B has 35 scored results on Noometry and o3 has 63.