Model comparison
Llama-3.3-70B-Instruct vs o3-mini
o3-mini is the stronger model overall, scoring 36.7 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 12× less per token, which makes it the better buy when o3-mini's lead doesn't matter for your workload.
Last verified . 37 shared benchmarks.
MetaSummary
- They share 37 benchmarks with published results for both. Llama-3.3-70B-Instruct scores higher in 0 categories and o3-mini in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where o3-mini leads 27.6 to 14.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 5.1% for Llama-3.3-70B-Instruct and 76.9% for o3-mini.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $1.10 / $4.40 for o3-mini.
- o3-mini accepts more context: 200K tokens versus 128K.
- Llama-3.3-70B-Instruct has downloadable open weights; the other is API-only.
Side by side
| Llama-3.3-70B-Instruct | o3-mini | |
|---|---|---|
| Provider | Meta | OpenAI |
| Noometry Index | 30.3 | 36.7 |
| Released | 2024-12-06 | 2024-12-20 |
| Weights | Open | Proprietary |
| Context window | 128K | 200K |
| Max output | 4K | 100K |
| Input $ / M tokens | $0.10 | $1.10 |
| Output $ / M tokens | $0.32 | $4.40 |
| Results tracked | 43 | 51 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding o3-mini leads
Llama-3.3-70B-Instruct: 30.6 (#284), o3-mini: 41.4 (#116)
| Benchmark | Llama-3.3-70B-Instruct | o3-mini |
|---|---|---|
| SciCode | 26% | 39.8% |
| WeirdML | 14.4% | 43.7% |
| LiveBench Coding | 36.6% | 82.7% |
| LMArena Coding | 1268 | 1378 |
| Aider Polyglot | — | 60.4% |
| GSO | — | 1.3% |
| BigCodeBench Instruct | 46.9% | — |
| BigCodeBench Complete | 57.5% | — |
| CadEval | — | 54% |
Agentic & Tool Use o3-mini leads
Llama-3.3-70B-Instruct: 25.6 (#100), o3-mini: 29.3 (#79)
| Benchmark | Llama-3.3-70B-Instruct | o3-mini |
|---|---|---|
| Berkeley Function Calling Leaderboard | 31.9% | — |
| Cybench | — | 22.5% |
| BALROG | 23% | — |
Reasoning o3-mini leads
Llama-3.3-70B-Instruct: 13.7 (#325), o3-mini: 16.3 (#295)
| Benchmark | Llama-3.3-70B-Instruct | o3-mini |
|---|---|---|
| SimpleBench | 19.9% | 22.8% |
| CritPt | 0% | 0.3% |
| LiveBench Reasoning | 50.8% | 89.6% |
| LMArena Hard Prompts | 1257 | 1366 |
| DTBench | 59.5% | 68.8% |
| LiveBench Data Analysis | 49.5% | 70.6% |
| LMCA | 17.5% | 19% |
| Epoch Capabilities Index | 127.32 | 140.35 |
| ForecastBench | 58.6 | 59.6 |
| LiveBench | 50.2% | 75.9% |
| ARC-AGI-2 | — | 3% |
| ARC-AGI-1 | — | 34.5% |
| Chess Puzzles | — | 17% |
| Mystery Game Puzzles | — | 7% |
Math o3-mini leads
Llama-3.3-70B-Instruct: 14.3 (#292), o3-mini: 27.6 (#236)
| Benchmark | Llama-3.3-70B-Instruct | o3-mini |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 5.1% | 76.9% |
| LiveBench Math | 42.2% | 77.3% |
| LMArena Math | 1268 | 1396 |
| MATH Level 5 | 41.6% | 96.5% |
| FrontierMath (Tiers 1-3) | — | 18.6% |
| FrontierMath Tier 4 | — | 0% |
| FrontierMath (Feb 2025 set) | — | 12.4% |
| FrontierMath Tier 4 (v1) | — | 4.2% |
Knowledge o3-mini leads
Llama-3.3-70B-Instruct: 30.5 (#220), o3-mini: 38.3 (#135)
| Benchmark | Llama-3.3-70B-Instruct | o3-mini |
|---|---|---|
| GPQA Diamond | 47.4% | 77% |
| Confabulations | 22.8% | 17.9% |
| LMArena Expert | 1225 | 1364 |
| SimpleQA Verified | — | 15.3% |
| Vectara Hallucination Rate | 4.1% | — |
| MMLU | 86.3% | — |
Multilingual o3-mini leads
Llama-3.3-70B-Instruct: 40.1 (#215), o3-mini: 45.9 (#159)
| Benchmark | Llama-3.3-70B-Instruct | o3-mini |
|---|---|---|
| LMArena Non-English | 1236 | 1319 |
| LMArena Chinese | 1217 | 1379 |
| LMArena French | 1280 | 1333 |
| LMArena German | 1251 | 1303 |
| LMArena Japanese | 1149 | 1286 |
| LMArena Korean | 1142 | 1314 |
| LMArena Russian | 1252 | 1304 |
| LMArena Spanish | 1269 | 1321 |
Instruction Following o3-mini leads
Llama-3.3-70B-Instruct: 71.3 (#152), o3-mini: 75.3 (#67)
| Benchmark | Llama-3.3-70B-Instruct | o3-mini |
|---|---|---|
| LiveBench Instruction Following | 82.7% | 84.4% |
| LMArena Instruction Following | 1242 | 1338 |
Long Context o3-mini leads
Llama-3.3-70B-Instruct: 26.4 (#290), o3-mini: 33.8 (#251)
| Benchmark | Llama-3.3-70B-Instruct | o3-mini |
|---|---|---|
| Fiction.LiveBench | 33.3% | 50% |
| LMArena Longer Query | 1256 | 1343 |
Writing & Preference o3-mini leads
Llama-3.3-70B-Instruct: 47.8 (#203), o3-mini: 50.4 (#177)
| Benchmark | Llama-3.3-70B-Instruct | o3-mini |
|---|---|---|
| LMArena Text | 1274 | 1337 |
| LMArena Creative Writing | 1250 | 1286 |
| LMArena Multi-Turn | 1279 | 1320 |
| LiveBench Language | 39.2% | 50.7% |
| Short-Story Creative Writing | — | 61.7% |
Frequently asked questions
Is Llama-3.3-70B-Instruct better than o3-mini?
o3-mini is the stronger model overall, scoring 36.7 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 12× less per token, which makes it the better buy when o3-mini's lead doesn't matter for your workload.
Which is cheaper, Llama-3.3-70B-Instruct or o3-mini?
Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; o3-mini lists at $1.10 and $4.40.
Is Llama-3.3-70B-Instruct or o3-mini better for coding?
o3-mini scores higher on coding benchmarks: 41.4 versus 30.6 in the Noometry coding category.
Which has the bigger context window?
o3-mini does, with 200K tokens against 128K.
How many benchmarks do Llama-3.3-70B-Instruct and o3-mini share?
37 benchmarks have published results for both models. Llama-3.3-70B-Instruct has 43 scored results on Noometry and o3-mini has 51.