Model comparison
Llama 3-8B vs Llama 4 Scout
Llama 4 Scout is the stronger model overall, scoring 27.7 to 25.5 on the Noometry Index.
Last verified . 24 shared benchmarks.
MetaSummary
- They share 24 benchmarks with published results for both. Llama 3-8B scores higher in 4 categories and Llama 4 Scout in 4 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Llama 4 Scout leads 31.9 to 7.8.
- The biggest single-benchmark swing is MATH Level 5: 6.1% for Llama 3-8B and 62.3% for Llama 4 Scout.
Side by side
| Llama 3-8B | Llama 4 Scout | |
|---|---|---|
| Provider | Meta | Meta |
| Noometry Index | 25.5 | 27.7 |
| Released | 2024-04-18 | 2025-04-05 |
| Weights | Open | Open |
| Context window | — | 128K |
| Max output | — | 4K |
| Input $ / M tokens | — | $0.10 |
| Output $ / M tokens | — | $0.30 |
| Results tracked | 34 | 43 |
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Category by category
Coding Llama 3-8B leads
Llama 3-8B: 31.0 (#289), Llama 4 Scout: 20.2 (#339)
| Benchmark | Llama 3-8B | Llama 4 Scout |
|---|---|---|
| LMArena Coding | 1152 | 1286 |
| BigCodeBench Complete | 36.9% | 43.1% |
| SWE-bench Verified (bash only) | — | 9.1% |
| SciCode | — | 17% |
| BigCodeBench Instruct | 31.9% | — |
| HumanEval+ | 56.7% | — |
| MBPP+ | 54.8% | — |
Agentic & Tool Use Not comparable
Llama 3-8B: —, Llama 4 Scout: 24.6 (#119)
| Benchmark | Llama 3-8B | Llama 4 Scout |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 28.1% |
Reasoning Llama 3-8B leads
Llama 3-8B: 14.3 (#326), Llama 4 Scout: 9.1 (#345)
| Benchmark | Llama 3-8B | Llama 4 Scout |
|---|---|---|
| LMArena Hard Prompts | 1133 | 1266 |
| DTBench | 43.9% | 57.9% |
| Epoch Capabilities Index | 116.45 | 129.64 |
| ForecastBench | 58.6 | 57.5 |
| ARC-AGI-2 | — | 0% |
| Kagi LLM Benchmark | — | 36.9% |
| ARC-AGI-1 | — | 0.5% |
| CritPt | — | 0% |
| Chess Puzzles | 0% | — |
| LMCA | — | 12% |
| Adversarial NLI | 57.3% | — |
| WinoGrande | 75.7% | — |
Math Llama 4 Scout leads
Llama 3-8B: 8.8 (#323), Llama 4 Scout: 19.6 (#286)
| Benchmark | Llama 3-8B | Llama 4 Scout |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 1.9% | 7.8% |
| LMArena Math | 1151 | 1287 |
| MATH Level 5 | 6.1% | 62.3% |
| Omni-MATH | — | 37.3% |
| FrontierMath (Feb 2025 set) | — | 0% |
Knowledge Llama 4 Scout leads
Llama 3-8B: 7.8 (#308), Llama 4 Scout: 31.9 (#217)
| Benchmark | Llama 3-8B | Llama 4 Scout |
|---|---|---|
| GPQA Diamond | 26.1% | 51.8% |
| LMArena Expert | 1113 | 1235 |
| MMLU-Pro | — | 74.2% |
| Vectara Hallucination Rate | — | 7.7% |
| GPQA (HELM) | — | 50.7% |
| ARC (AI2) Challenge | 82.8% | — |
| MMLU | 68.8% | — |
| OpenBookQA | 82.6% | — |
| TriviaQA | 67.7% | — |
Multimodal Not comparable
Llama 3-8B: —, Llama 4 Scout: 32.2 (#102)
| Benchmark | Llama 3-8B | Llama 4 Scout |
|---|---|---|
| LMArena Vision | — | 1118 |
| SpatialViz-Bench | — | 34.2% |
Multilingual Llama 4 Scout leads
Llama 3-8B: 30.8 (#261), Llama 4 Scout: 41.0 (#212)
| Benchmark | Llama 3-8B | Llama 4 Scout |
|---|---|---|
| LMArena Non-English | 1098 | 1252 |
| LMArena Chinese | 1076 | 1255 |
| LMArena French | 1159 | 1282 |
| LMArena German | 1104 | 1272 |
| LMArena Japanese | 967 | 1206 |
| LMArena Korean | 1004 | 1207 |
| LMArena Russian | 1109 | 1263 |
| LMArena Spanish | 1173 | 1278 |
Instruction Following Llama 4 Scout leads
Llama 3-8B: 58.4 (#260), Llama 4 Scout: 65.8 (#217)
| Benchmark | Llama 3-8B | Llama 4 Scout |
|---|---|---|
| LMArena Instruction Following | 1127 | 1248 |
| IFEval | — | 81.8% |
Long Context Llama 3-8B leads
Llama 3-8B: 34.2 (#251), Llama 4 Scout: 27.5 (#294)
| Benchmark | Llama 3-8B | Llama 4 Scout |
|---|---|---|
| LMArena Longer Query | 1128 | 1265 |
| Fiction.LiveBench | — | 36% |
Writing & Preference Too close to call
Llama 3-8B: 37.5 (#256), Llama 4 Scout: 37.0 (#261)
| Benchmark | Llama 3-8B | Llama 4 Scout |
|---|---|---|
| LMArena Text | 1166 | 1279 |
| LMArena Creative Writing | 1150 | 1249 |
| LMArena Multi-Turn | 1152 | 1280 |
| EQ-Bench Creative Writing | — | 783 |
| WildBench | — | 78% |
Frequently asked questions
Is Llama 3-8B better than Llama 4 Scout?
Llama 4 Scout is the stronger model overall, scoring 27.7 to 25.5 on the Noometry Index.
Is Llama 3-8B or Llama 4 Scout better for coding?
Llama 3-8B scores higher on coding benchmarks: 31.0 versus 20.2 in the Noometry coding category.
How many benchmarks do Llama 3-8B and Llama 4 Scout share?
24 benchmarks have published results for both models. Llama 3-8B has 34 scored results on Noometry and Llama 4 Scout has 43.