Model comparison
Llama 3.1-8B vs Llama 4 Scout
Llama 4 Scout is the stronger model overall, scoring 27.7 to 23.0 on the Noometry Index. Llama 3.1-8B costs 2.6× less per token, which makes it the better buy when Llama 4 Scout's lead doesn't matter for your workload.
Last verified . 33 shared benchmarks.
MetaSummary
- They share 33 benchmarks with published results for both. Llama 3.1-8B scores higher in 2 categories and Llama 4 Scout in 7 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Llama 4 Scout leads 31.9 to 8.0.
- The biggest single-benchmark swing is MATH Level 5: 22.9% for Llama 3.1-8B and 62.3% for Llama 4 Scout.
- Llama 3.1-8B is cheaper at $0.05 / $0.08 per million input/output tokens, against $0.10 / $0.30 for Llama 4 Scout.
Side by side
| Llama 3.1-8B | Llama 4 Scout | |
|---|---|---|
| Provider | Meta | Meta |
| Noometry Index | 23.0 | 27.7 |
| Released | 2024-07-23 | 2025-04-05 |
| Weights | Open | Open |
| Context window | 128K | 128K |
| Max output | 4K | 4K |
| Input $ / M tokens | $0.05 | $0.10 |
| Output $ / M tokens | $0.08 | $0.30 |
| Results tracked | 43 | 43 |
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Category by category
Coding Too close to call
Llama 3.1-8B: 20.2 (#340), Llama 4 Scout: 20.2 (#339)
| Benchmark | Llama 3.1-8B | Llama 4 Scout |
|---|---|---|
| SciCode | 13.2% | 17% |
| LMArena Coding | 1195 | 1286 |
| BigCodeBench Complete | 40.5% | 43.1% |
| SWE-bench Verified (bash only) | — | 9.1% |
| WeirdML | 1.7% | — |
| BigCodeBench Instruct | 32.8% | — |
| HumanEval+ | 62.8% | — |
| MBPP+ | 55.6% | — |
Agentic & Tool Use Llama 4 Scout leads
Llama 3.1-8B: 22.5 (#131), Llama 4 Scout: 24.6 (#119)
| Benchmark | Llama 3.1-8B | Llama 4 Scout |
|---|---|---|
| Berkeley Function Calling Leaderboard | 25.8% | 28.1% |
| BALROG | 15.1% | — |
Reasoning Llama 3.1-8B leads
Llama 3.1-8B: 14.9 (#321), Llama 4 Scout: 9.1 (#345)
| Benchmark | Llama 3.1-8B | Llama 4 Scout |
|---|---|---|
| CritPt | 0% | 0% |
| LMArena Hard Prompts | 1175 | 1266 |
| DTBench | 50.9% | 57.9% |
| LMCA | 5.4% | 12% |
| Epoch Capabilities Index | 116.57 | 129.64 |
| ARC-AGI-2 | — | 0% |
| Kagi LLM Benchmark | — | 36.9% |
| ARC-AGI-1 | — | 0.5% |
| Chess Puzzles | 0% | — |
| ForecastBench | — | 57.5 |
| PIQA | 81.2% | — |
Math Llama 4 Scout leads
Llama 3.1-8B: 10.2 (#317), Llama 4 Scout: 19.6 (#286)
| Benchmark | Llama 3.1-8B | Llama 4 Scout |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 1.7% | 7.8% |
| Omni-MATH | 13.7% | 37.3% |
| LMArena Math | 1179 | 1287 |
| MATH Level 5 | 22.9% | 62.3% |
| FrontierMath (Feb 2025 set) | — | 0% |
| GSM8K | 82.4% | — |
Knowledge Llama 4 Scout leads
Llama 3.1-8B: 8.0 (#307), Llama 4 Scout: 31.9 (#217)
| Benchmark | Llama 3.1-8B | Llama 4 Scout |
|---|---|---|
| GPQA Diamond | 27% | 51.8% |
| MMLU-Pro | 40.6% | 74.2% |
| GPQA (HELM) | 24.7% | 50.7% |
| LMArena Expert | 1144 | 1235 |
| Vectara Hallucination Rate | — | 7.7% |
| BoolQ | 82.8% | — |
| MMLU | 56.1% | — |
Multimodal Not comparable
Llama 3.1-8B: —, Llama 4 Scout: 32.2 (#102)
| Benchmark | Llama 3.1-8B | Llama 4 Scout |
|---|---|---|
| LMArena Vision | — | 1118 |
| SpatialViz-Bench | — | 34.2% |
Multilingual Llama 4 Scout leads
Llama 3.1-8B: 34.0 (#249), Llama 4 Scout: 41.0 (#212)
| Benchmark | Llama 3.1-8B | Llama 4 Scout |
|---|---|---|
| LMArena Non-English | 1148 | 1252 |
| LMArena Chinese | 1151 | 1255 |
| LMArena French | 1177 | 1282 |
| LMArena German | 1144 | 1272 |
| LMArena Japanese | 1061 | 1206 |
| LMArena Korean | 1053 | 1207 |
| LMArena Russian | 1158 | 1263 |
| LMArena Spanish | 1169 | 1278 |
Instruction Following Llama 4 Scout leads
Llama 3.1-8B: 58.9 (#258), Llama 4 Scout: 65.8 (#217)
| Benchmark | Llama 3.1-8B | Llama 4 Scout |
|---|---|---|
| IFEval | 74.3% | 81.8% |
| LMArena Instruction Following | 1159 | 1248 |
Long Context Llama 3.1-8B leads
Llama 3.1-8B: 35.8 (#238), Llama 4 Scout: 27.5 (#294)
| Benchmark | Llama 3.1-8B | Llama 4 Scout |
|---|---|---|
| LMArena Longer Query | 1182 | 1265 |
| Fiction.LiveBench | — | 36% |
Writing & Preference Llama 4 Scout leads
Llama 3.1-8B: 29.7 (#290), Llama 4 Scout: 37.0 (#261)
| Benchmark | Llama 3.1-8B | Llama 4 Scout |
|---|---|---|
| LMArena Text | 1187 | 1279 |
| LMArena Creative Writing | 1154 | 1249 |
| EQ-Bench Creative Writing | 713 | 783 |
| WildBench | 68.7% | 78% |
| LMArena Multi-Turn | 1172 | 1280 |
Frequently asked questions
Is Llama 3.1-8B better than Llama 4 Scout?
Llama 4 Scout is the stronger model overall, scoring 27.7 to 23.0 on the Noometry Index. Llama 3.1-8B costs 2.6× less per token, which makes it the better buy when Llama 4 Scout's lead doesn't matter for your workload.
Which is cheaper, Llama 3.1-8B or Llama 4 Scout?
Llama 3.1-8B is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; Llama 4 Scout lists at $0.10 and $0.30.
Is Llama 3.1-8B or Llama 4 Scout better for coding?
They score almost the same on coding (20.2 vs 20.2); test both on your own repository before choosing.
Which has the bigger context window?
Both accept 128K tokens.
How many benchmarks do Llama 3.1-8B and Llama 4 Scout share?
33 benchmarks have published results for both models. Llama 3.1-8B has 43 scored results on Noometry and Llama 4 Scout has 43.