Model comparison
Llama 4 Scout vs o3-mini
o3-mini is the stronger model overall, scoring 36.7 to 27.4 on the Noometry Index. Llama 4 Scout costs 13× less per token, which makes it the better buy when o3-mini's lead doesn't matter for your workload.
Last verified . 30 shared benchmarks.
MetaSummary
- They share 30 benchmarks with published results for both. Llama 4 Scout scores higher in 0 categories and o3-mini in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in coding, where o3-mini leads 41.4 to 19.9.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 7.8% for Llama 4 Scout and 76.9% for o3-mini.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $1.10 / $4.40 for o3-mini.
- o3-mini accepts more context: 200K tokens versus 128K.
- Llama 4 Scout has downloadable open weights; the other is API-only.
Side by side
| Llama 4 Scout | o3-mini | |
|---|---|---|
| Provider | Meta | OpenAI |
| Noometry Index | 27.4 | 36.7 |
| Released | 2025-04-05 | 2024-12-20 |
| Weights | Open | Proprietary |
| Context window | 128K | 200K |
| Max output | 4K | 100K |
| Input $ / M tokens | $0.10 | $1.10 |
| Output $ / M tokens | $0.30 | $4.40 |
| Results tracked | 43 | 51 |
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Category by category
Coding o3-mini leads
Llama 4 Scout: 19.9 (#334), o3-mini: 41.4 (#116)
| Benchmark | Llama 4 Scout | o3-mini |
|---|---|---|
| SciCode | 17% | 39.8% |
| LMArena Coding | 1286 | 1378 |
| SWE-bench Verified (bash only) | 9.1% | — |
| Aider Polyglot | — | 60.4% |
| GSO | — | 1.3% |
| WeirdML | — | 43.7% |
| LiveBench Coding | — | 82.7% |
| BigCodeBench Complete | 43.1% | — |
| CadEval | — | 54% |
Agentic & Tool Use o3-mini leads
Llama 4 Scout: 24.4 (#109), o3-mini: 29.3 (#79)
| Benchmark | Llama 4 Scout | o3-mini |
|---|---|---|
| Berkeley Function Calling Leaderboard | 28.1% | — |
| Cybench | — | 22.5% |
Reasoning o3-mini leads
Llama 4 Scout: 9.2 (#339), o3-mini: 16.3 (#295)
| Benchmark | Llama 4 Scout | o3-mini |
|---|---|---|
| ARC-AGI-2 | 0% | 3% |
| ARC-AGI-1 | 0.5% | 34.5% |
| CritPt | 0% | 0.3% |
| LMArena Hard Prompts | 1266 | 1366 |
| DTBench | 57.9% | 68.8% |
| LMCA | 12% | 19% |
| Epoch Capabilities Index | 129.64 | 140.35 |
| ForecastBench | 57.5 | 59.6 |
| SimpleBench | — | 22.8% |
| Kagi LLM Benchmark | 36.9% | — |
| Chess Puzzles | — | 17% |
| LiveBench Reasoning | — | 89.6% |
| Mystery Game Puzzles | — | 7% |
| LiveBench Data Analysis | — | 70.6% |
| LiveBench | — | 75.9% |
Math o3-mini leads
Llama 4 Scout: 18.4 (#280), o3-mini: 27.6 (#236)
| Benchmark | Llama 4 Scout | o3-mini |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 7.8% | 76.9% |
| LMArena Math | 1286 | 1396 |
| MATH Level 5 | 62.3% | 96.5% |
| FrontierMath (Feb 2025 set) | 0% | 12.4% |
| FrontierMath (Tiers 1-3) | — | 18.6% |
| FrontierMath Tier 4 | — | 0% |
| Omni-MATH | 37.3% | — |
| LiveBench Math | — | 77.3% |
| FrontierMath Tier 4 (v1) | — | 4.2% |
Knowledge o3-mini leads
Llama 4 Scout: 31.8 (#208), o3-mini: 38.3 (#135)
| Benchmark | Llama 4 Scout | o3-mini |
|---|---|---|
| GPQA Diamond | 51.8% | 77% |
| LMArena Expert | 1236 | 1364 |
| SimpleQA Verified | — | 15.3% |
| MMLU-Pro | 74.2% | — |
| Confabulations | — | 17.9% |
| Vectara Hallucination Rate | 7.7% | — |
| GPQA (HELM) | 50.7% | — |
Multimodal Not comparable
Llama 4 Scout: 31.6 (#98), o3-mini: —
| Benchmark | Llama 4 Scout | o3-mini |
|---|---|---|
| LMArena Vision | 1118 | — |
| SpatialViz-Bench | 34.2% | — |
Multilingual o3-mini leads
Llama 4 Scout: 41.3 (#207), o3-mini: 45.9 (#159)
| Benchmark | Llama 4 Scout | o3-mini |
|---|---|---|
| LMArena Non-English | 1253 | 1319 |
| LMArena Chinese | 1256 | 1379 |
| LMArena French | 1283 | 1333 |
| LMArena German | 1274 | 1303 |
| LMArena Japanese | 1209 | 1286 |
| LMArena Korean | 1207 | 1314 |
| LMArena Russian | 1263 | 1304 |
| LMArena Spanish | 1277 | 1321 |
Instruction Following o3-mini leads
Llama 4 Scout: 66.0 (#212), o3-mini: 75.3 (#67)
| Benchmark | Llama 4 Scout | o3-mini |
|---|---|---|
| LMArena Instruction Following | 1248 | 1338 |
| LiveBench Instruction Following | — | 84.4% |
| IFEval | 81.8% | — |
Long Context o3-mini leads
Llama 4 Scout: 27.5 (#289), o3-mini: 33.8 (#251)
| Benchmark | Llama 4 Scout | o3-mini |
|---|---|---|
| Fiction.LiveBench | 36% | 50% |
| LMArena Longer Query | 1265 | 1343 |
Writing & Preference o3-mini leads
Llama 4 Scout: 37.1 (#256), o3-mini: 50.4 (#177)
| Benchmark | Llama 4 Scout | o3-mini |
|---|---|---|
| LMArena Text | 1279 | 1337 |
| LMArena Creative Writing | 1249 | 1286 |
| LMArena Multi-Turn | 1281 | 1320 |
| Short-Story Creative Writing | — | 61.7% |
| EQ-Bench Creative Writing | 783 | — |
| WildBench | 78% | — |
| LiveBench Language | — | 50.7% |
Frequently asked questions
Is Llama 4 Scout better than o3-mini?
o3-mini is the stronger model overall, scoring 36.7 to 27.4 on the Noometry Index. Llama 4 Scout costs 13× less per token, which makes it the better buy when o3-mini's lead doesn't matter for your workload.
Which is cheaper, Llama 4 Scout or o3-mini?
Llama 4 Scout is cheaper. It lists at $0.10 per million input tokens and $0.30 per million output tokens; o3-mini lists at $1.10 and $4.40.
Is Llama 4 Scout or o3-mini better for coding?
o3-mini scores higher on coding benchmarks: 41.4 versus 19.9 in the Noometry coding category.
Which has the bigger context window?
o3-mini does, with 200K tokens against 128K.
How many benchmarks do Llama 4 Scout and o3-mini share?
30 benchmarks have published results for both models. Llama 4 Scout has 43 scored results on Noometry and o3-mini has 51.