Model comparison
Llama 4 Scout vs o4-mini
o4-mini is the stronger model overall, scoring 41.6 to 27.7 on the Noometry Index. Llama 4 Scout costs 13× less per token, which makes it the better buy when o4-mini's lead doesn't matter for your workload.
Last verified . 39 shared benchmarks.
MetaSummary
- They share 39 benchmarks with published results for both. Llama 4 Scout scores higher in 0 categories and o4-mini in 10 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in math, where o4-mini leads 40.8 to 19.6.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 7.8% for Llama 4 Scout and 81.7% for o4-mini.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $1.10 / $4.40 for o4-mini.
- o4-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 | o4-mini | |
|---|---|---|
| Provider | Meta | OpenAI |
| Noometry Index | 27.7 | 41.6 |
| Released | 2025-04-05 | 2025-04-16 |
| 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 | 60 |
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Category by category
Coding o4-mini leads
Llama 4 Scout: 20.2 (#339), o4-mini: 40.9 (#127)
| Benchmark | Llama 4 Scout | o4-mini |
|---|---|---|
| SWE-bench Verified (bash only) | 9.1% | 45% |
| LMArena Coding | 1286 | 1368 |
| Aider Polyglot | — | 72% |
| SciCode | 17% | — |
| GSO | — | 3.6% |
| WeirdML | — | 52.6% |
| BigCodeBench Complete | 43.1% | — |
| CadEval | — | 62% |
| ALE-Bench | — | 826.17 |
| AlgoTune | — | 1.72 |
Agentic & Tool Use o4-mini leads
Llama 4 Scout: 24.6 (#119), o4-mini: 32.6 (#61)
| Benchmark | Llama 4 Scout | o4-mini |
|---|---|---|
| Berkeley Function Calling Leaderboard | 28.1% | 53.2% |
| GDPval | — | 25.3% |
| METR Time Horizons | — | 63.9% |
Reasoning o4-mini leads
Llama 4 Scout: 9.1 (#345), o4-mini: 24.6 (#162)
| Benchmark | Llama 4 Scout | o4-mini |
|---|---|---|
| ARC-AGI-2 | 0% | 6.1% |
| Kagi LLM Benchmark | 36.9% | 67.6% |
| ARC-AGI-1 | 0.5% | 58.7% |
| CritPt | 0% | 0.6% |
| LMArena Hard Prompts | 1266 | 1351 |
| DTBench | 57.9% | 77.6% |
| LMCA | 12% | 26.5% |
| Epoch Capabilities Index | 129.64 | 145.64 |
| ForecastBench | 57.5 | 61.8 |
| SimpleBench | — | 38.7% |
| Chess Puzzles | — | 26% |
| EnigmaEval | — | 9.2% |
| Mystery Game Puzzles | — | 5% |
Math o4-mini leads
Llama 4 Scout: 19.6 (#286), o4-mini: 40.8 (#89)
| Benchmark | Llama 4 Scout | o4-mini |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 7.8% | 81.7% |
| Omni-MATH | 37.3% | 72% |
| LMArena Math | 1287 | 1389 |
| MATH Level 5 | 62.3% | 97.8% |
| FrontierMath (Feb 2025 set) | 0% | 24.8% |
| FrontierMath (Tiers 1-3) | — | 36.1% |
| FrontierMath Tier 4 | — | 4.9% |
| FrontierMath Tier 4 (v1) | — | 6.3% |
Knowledge o4-mini leads
Llama 4 Scout: 31.9 (#217), o4-mini: 43.6 (#91)
| Benchmark | Llama 4 Scout | o4-mini |
|---|---|---|
| GPQA Diamond | 51.8% | 79.6% |
| MMLU-Pro | 74.2% | 82% |
| Vectara Hallucination Rate | 7.7% | 18.6% |
| GPQA (HELM) | 50.7% | 73.5% |
| LMArena Expert | 1235 | 1343 |
| Humanity's Last Exam | — | 18.1% |
| SimpleQA Verified | — | 19.6% |
| Confabulations | — | 15.8% |
Multimodal o4-mini leads
Llama 4 Scout: 32.2 (#102), o4-mini: 40.2 (#49)
| Benchmark | Llama 4 Scout | o4-mini |
|---|---|---|
| LMArena Vision | 1118 | 1194 |
| GeoBench | — | 64% |
| VPCT | — | 57.5% |
| SpatialViz-Bench | 34.2% | — |
Multilingual o4-mini leads
Llama 4 Scout: 41.0 (#212), o4-mini: 47.0 (#154)
| Benchmark | Llama 4 Scout | o4-mini |
|---|---|---|
| LMArena Non-English | 1252 | 1337 |
| LMArena Chinese | 1255 | 1354 |
| LMArena French | 1282 | 1364 |
| LMArena German | 1272 | 1336 |
| LMArena Japanese | 1206 | 1308 |
| LMArena Korean | 1207 | 1312 |
| LMArena Russian | 1263 | 1334 |
| LMArena Spanish | 1278 | 1347 |
Instruction Following o4-mini leads
Llama 4 Scout: 65.8 (#217), o4-mini: 75.2 (#68)
| Benchmark | Llama 4 Scout | o4-mini |
|---|---|---|
| IFEval | 81.8% | 92.8% |
| LMArena Instruction Following | 1248 | 1321 |
Long Context o4-mini leads
Llama 4 Scout: 27.5 (#294), o4-mini: 45.5 (#33)
| Benchmark | Llama 4 Scout | o4-mini |
|---|---|---|
| Fiction.LiveBench | 36% | 77.8% |
| LMArena Longer Query | 1265 | 1315 |
Writing & Preference o4-mini leads
Llama 4 Scout: 37.0 (#261), o4-mini: 54.0 (#152)
| Benchmark | Llama 4 Scout | o4-mini |
|---|---|---|
| LMArena Text | 1279 | 1353 |
| LMArena Creative Writing | 1249 | 1294 |
| WildBench | 78% | 85.4% |
| LMArena Multi-Turn | 1280 | 1350 |
| Short-Story Creative Writing | — | 75% |
| EQ-Bench Creative Writing | 783 | — |
Frequently asked questions
Is Llama 4 Scout better than o4-mini?
o4-mini is the stronger model overall, scoring 41.6 to 27.7 on the Noometry Index. Llama 4 Scout costs 13× less per token, which makes it the better buy when o4-mini's lead doesn't matter for your workload.
Which is cheaper, Llama 4 Scout or o4-mini?
Llama 4 Scout is cheaper. It lists at $0.10 per million input tokens and $0.30 per million output tokens; o4-mini lists at $1.10 and $4.40.
Is Llama 4 Scout or o4-mini better for coding?
o4-mini scores higher on coding benchmarks: 40.9 versus 20.2 in the Noometry coding category.
Which has the bigger context window?
o4-mini does, with 200K tokens against 128K.
How many benchmarks do Llama 4 Scout and o4-mini share?
39 benchmarks have published results for both models. Llama 4 Scout has 43 scored results on Noometry and o4-mini has 60.