Model comparison
Llama 3.2 3B vs o3-mini
o3-mini is the stronger model overall, scoring 36.7 to 28.9 on the Noometry Index. Llama 3.2 3B costs 16× less per token, which makes it the better buy when o3-mini's lead doesn't matter for your workload.
Last verified . 13 shared benchmarks.
MetaSummary
- They share 13 benchmarks with published results for both. Llama 3.2 3B scores higher in 2 categories and o3-mini in 7 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where o3-mini leads 50.3 to 24.7.
- Llama 3.2 3B is cheaper at $0.05 / $0.33 per million input/output tokens, against $1.10 / $4.40 for o3-mini.
- o3-mini accepts more context: 200K tokens versus 131K.
- Llama 3.2 3B has downloadable open weights; the other is API-only.
Side by side
| Llama 3.2 3B | o3-mini | |
|---|---|---|
| Provider | Meta | OpenAI |
| Noometry Index | 28.9 | 36.7 |
| Released | 2024-09-24 | 2024-12-20 |
| Weights | Open | Proprietary |
| Context window | 131K | 200K |
| Max output | 118K | 100K |
| Input $ / M tokens | $0.05 | $1.10 |
| Output $ / M tokens | $0.33 | $4.40 |
| Results tracked | 18 | 51 |
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Category by category
Coding o3-mini leads
Llama 3.2 3B: 27.6 (#319), o3-mini: 40.8 (#132)
| Benchmark | Llama 3.2 3B | o3-mini |
|---|---|---|
| LMArena Coding | 1098 | 1378 |
| Aider Polyglot | — | 60.4% |
| SciCode | — | 39.8% |
| GSO | — | 1.3% |
| WeirdML | — | 43.7% |
| BigCodeBench Instruct | 23.4% | — |
| LiveBench Coding | — | 82.7% |
| BigCodeBench Complete | 28.3% | — |
| CadEval | — | 54% |
Agentic & Tool Use o3-mini leads
Llama 3.2 3B: 20.1 (#143), o3-mini: 29.6 (#84)
| Benchmark | Llama 3.2 3B | o3-mini |
|---|---|---|
| Berkeley Function Calling Leaderboard | 21.9% | — |
| Cybench | — | 22.5% |
| BALROG | 10.1% | — |
Reasoning Llama 3.2 3B leads
Llama 3.2 3B: 21.0 (#228), o3-mini: 16.3 (#305)
| Benchmark | Llama 3.2 3B | o3-mini |
|---|---|---|
| LMArena Hard Prompts | 1095 | 1366 |
| ARC-AGI-2 | — | 3% |
| SimpleBench | — | 22.8% |
| ARC-AGI-1 | — | 34.5% |
| CritPt | — | 0.3% |
| Chess Puzzles | — | 17% |
| LiveBench Reasoning | — | 89.6% |
| Mystery Game Puzzles | — | 7% |
| DTBench | — | 68.8% |
| LiveBench Data Analysis | — | 70.6% |
| LMCA | — | 19% |
| Epoch Capabilities Index | — | 140.34 |
| ForecastBench | — | 59.6 |
| LiveBench | — | 75.9% |
Math Llama 3.2 3B leads
Llama 3.2 3B: 32.4 (#214), o3-mini: 28.1 (#244)
| Benchmark | Llama 3.2 3B | o3-mini |
|---|---|---|
| LMArena Math | 1126 | 1396 |
| FrontierMath (Tiers 1-3) | — | 18.6% |
| FrontierMath Tier 4 | — | 0% |
| OTIS Mock AIME 2024-2025 | — | 76.9% |
| LiveBench Math | — | 77.3% |
| MATH Level 5 | — | 96.5% |
| FrontierMath (Feb 2025 set) | — | 12.4% |
| FrontierMath Tier 4 (v1) | — | 4.2% |
Knowledge o3-mini leads
Llama 3.2 3B: 29.7 (#235), o3-mini: 38.3 (#146)
| Benchmark | Llama 3.2 3B | o3-mini |
|---|---|---|
| LMArena Expert | 1090 | 1364 |
| GPQA Diamond | — | 77% |
| SimpleQA Verified | — | 15.3% |
| Confabulations | — | 17.9% |
Multilingual o3-mini leads
Llama 3.2 3B: 26.2 (#281), o3-mini: 45.7 (#164)
| Benchmark | Llama 3.2 3B | o3-mini |
|---|---|---|
| LMArena Non-English | 1019 | 1319 |
| LMArena Chinese | 1017 | 1379 |
| LMArena German | 1056 | 1303 |
| LMArena Russian | 949 | 1304 |
| LMArena French | — | 1334 |
| LMArena Japanese | — | 1286 |
| LMArena Korean | — | 1314 |
| LMArena Spanish | — | 1321 |
Instruction Following o3-mini leads
Llama 3.2 3B: 56.0 (#275), o3-mini: 75.1 (#72)
| Benchmark | Llama 3.2 3B | o3-mini |
|---|---|---|
| LMArena Instruction Following | 1089 | 1337 |
| LiveBench Instruction Following | — | 84.4% |
Long Context Too close to call
Llama 3.2 3B: 33.4 (#261), o3-mini: 33.8 (#256)
| Benchmark | Llama 3.2 3B | o3-mini |
|---|---|---|
| LMArena Longer Query | 1100 | 1343 |
| Fiction.LiveBench | — | 50% |
Writing & Preference o3-mini leads
Llama 3.2 3B: 24.7 (#307), o3-mini: 50.3 (#182)
| Benchmark | Llama 3.2 3B | o3-mini |
|---|---|---|
| LMArena Text | 1110 | 1337 |
| LMArena Creative Writing | 1094 | 1286 |
| LMArena Multi-Turn | 1105 | 1320 |
| Short-Story Creative Writing | — | 61.7% |
| EQ-Bench Creative Writing | 595 | — |
| LiveBench Language | — | 50.7% |
Frequently asked questions
Is Llama 3.2 3B better than o3-mini?
o3-mini is the stronger model overall, scoring 36.7 to 28.9 on the Noometry Index. Llama 3.2 3B costs 16× less per token, which makes it the better buy when o3-mini's lead doesn't matter for your workload.
Which is cheaper, Llama 3.2 3B or o3-mini?
Llama 3.2 3B is cheaper. It lists at $0.05 per million input tokens and $0.33 per million output tokens; o3-mini lists at $1.10 and $4.40.
Is Llama 3.2 3B or o3-mini better for coding?
o3-mini scores higher on coding benchmarks: 40.8 versus 27.6 in the Noometry coding category.
Which has the bigger context window?
o3-mini does, with 200K tokens against 131K.
How many benchmarks do Llama 3.2 3B and o3-mini share?
13 benchmarks have published results for both models. Llama 3.2 3B has 18 scored results on Noometry and o3-mini has 51.