Model comparison
Llama 4 Scout vs o3-pro
o3-pro is the stronger model overall, scoring 42.9 to 27.7 on the Noometry Index. Llama 4 Scout costs 233× less per token, which makes it the better buy when o3-pro's lead doesn't matter for your workload.
Last verified . 8 shared benchmarks.
MetaSummary
- They share 8 benchmarks with published results for both. Llama 4 Scout scores higher in 1 category and o3-pro in 4 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in long context, where o3-pro leads 72.2 to 27.5.
- The biggest single-benchmark swing is Fiction.LiveBench: 36% for Llama 4 Scout and 97.2% for o3-pro.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $20 / $80 for o3-pro.
- o3-pro 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-pro | |
|---|---|---|
| Provider | Meta | OpenAI |
| Noometry Index | 27.7 | 42.9 |
| Released | 2025-04-05 | 2025-06-10 |
| Weights | Open | Proprietary |
| Context window | 128K | 200K |
| Max output | 4K | 100K |
| Input $ / M tokens | $0.10 | $20 |
| Output $ / M tokens | $0.30 | $80 |
| Results tracked | 43 | 12 |
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Category by category
Coding o3-pro leads
Llama 4 Scout: 20.2 (#339), o3-pro: 55.5 (#24)
| Benchmark | Llama 4 Scout | o3-pro |
|---|---|---|
| SWE-bench Verified (bash only) | 9.1% | — |
| Aider Polyglot | — | 84.9% |
| SciCode | 17% | — |
| WeirdML | — | 58.2% |
| LMArena Coding | 1286 | — |
| BigCodeBench Complete | 43.1% | — |
Agentic & Tool Use Not comparable
Llama 4 Scout: 24.6 (#119), o3-pro: —
| Benchmark | Llama 4 Scout | o3-pro |
|---|---|---|
| Berkeley Function Calling Leaderboard | 28.1% | — |
Reasoning o3-pro leads
Llama 4 Scout: 9.1 (#345), o3-pro: 23.8 (#171)
| Benchmark | Llama 4 Scout | o3-pro |
|---|---|---|
| ARC-AGI-2 | 0% | 4.9% |
| Kagi LLM Benchmark | 36.9% | 72.1% |
| ARC-AGI-1 | 0.5% | 59.3% |
| DTBench | 57.9% | 86.9% |
| LMCA | 12% | 38.5% |
| Epoch Capabilities Index | 129.64 | 147.42 |
| CritPt | 0% | — |
| LMArena Hard Prompts | 1266 | — |
| ForecastBench | 57.5 | — |
Math Not comparable
Llama 4 Scout: 19.6 (#286), o3-pro: —
| Benchmark | Llama 4 Scout | o3-pro |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 7.8% | — |
| Omni-MATH | 37.3% | — |
| LMArena Math | 1287 | — |
| MATH Level 5 | 62.3% | — |
| FrontierMath (Feb 2025 set) | 0% | — |
Knowledge Llama 4 Scout leads
Llama 4 Scout: 31.9 (#217), o3-pro: 29.5 (#238)
| Benchmark | Llama 4 Scout | o3-pro |
|---|---|---|
| Vectara Hallucination Rate | 7.7% | 23.3% |
| GPQA Diamond | 51.8% | — |
| MMLU-Pro | 74.2% | — |
| Confabulations | — | 14.2% |
| GPQA (HELM) | 50.7% | — |
| LMArena Expert | 1235 | — |
Multimodal Not comparable
Llama 4 Scout: 32.2 (#102), o3-pro: —
| Benchmark | Llama 4 Scout | o3-pro |
|---|---|---|
| LMArena Vision | 1118 | — |
| SpatialViz-Bench | 34.2% | — |
Multilingual Not comparable
Llama 4 Scout: 41.0 (#212), o3-pro: —
| Benchmark | Llama 4 Scout | o3-pro |
|---|---|---|
| LMArena Non-English | 1252 | — |
| LMArena Chinese | 1255 | — |
| LMArena French | 1282 | — |
| LMArena German | 1272 | — |
| LMArena Japanese | 1206 | — |
| LMArena Korean | 1207 | — |
| LMArena Russian | 1263 | — |
| LMArena Spanish | 1278 | — |
Instruction Following Not comparable
Llama 4 Scout: 65.8 (#217), o3-pro: —
| Benchmark | Llama 4 Scout | o3-pro |
|---|---|---|
| IFEval | 81.8% | — |
| LMArena Instruction Following | 1248 | — |
Long Context o3-pro leads
Llama 4 Scout: 27.5 (#294), o3-pro: 72.2 (#1)
| Benchmark | Llama 4 Scout | o3-pro |
|---|---|---|
| Fiction.LiveBench | 36% | 97.2% |
| LMArena Longer Query | 1265 | — |
Writing & Preference o3-pro leads
Llama 4 Scout: 37.0 (#261), o3-pro: 57.1 (#133)
| Benchmark | Llama 4 Scout | o3-pro |
|---|---|---|
| LMArena Text | 1279 | — |
| LMArena Creative Writing | 1249 | — |
| Short-Story Creative Writing | — | 84.4% |
| EQ-Bench Creative Writing | 783 | — |
| WildBench | 78% | — |
| LMArena Multi-Turn | 1280 | — |
Frequently asked questions
Is Llama 4 Scout better than o3-pro?
o3-pro is the stronger model overall, scoring 42.9 to 27.7 on the Noometry Index. Llama 4 Scout costs 233× less per token, which makes it the better buy when o3-pro's lead doesn't matter for your workload.
Which is cheaper, Llama 4 Scout or o3-pro?
Llama 4 Scout is cheaper. It lists at $0.10 per million input tokens and $0.30 per million output tokens; o3-pro lists at $20 and $80.
Is Llama 4 Scout or o3-pro better for coding?
o3-pro scores higher on coding benchmarks: 55.5 versus 20.2 in the Noometry coding category.
Which has the bigger context window?
o3-pro does, with 200K tokens against 128K.
How many benchmarks do Llama 4 Scout and o3-pro share?
8 benchmarks have published results for both models. Llama 4 Scout has 43 scored results on Noometry and o3-pro has 12.