Model comparison
Llama 3.1-70B vs Llama 4 Scout
Llama 3.1-70B is the stronger model overall, scoring 29.6 to 27.7 on the Noometry Index. Llama 4 Scout costs 2.7× less per token, which makes it the better buy when Llama 3.1-70B's lead doesn't matter for your workload.
Last verified . 30 shared benchmarks.
MetaSummary
- They share 30 benchmarks with published results for both. Llama 3.1-70B scores higher in 4 categories and Llama 4 Scout in 5 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Llama 3.1-70B leads 21.6 to 9.1.
- The biggest single-benchmark swing is MATH Level 5: 36.7% for Llama 3.1-70B and 62.3% for Llama 4 Scout.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $0.40 / $0.40 for Llama 3.1-70B.
Side by side
| Llama 3.1-70B | Llama 4 Scout | |
|---|---|---|
| Provider | Meta | Meta |
| Noometry Index | 29.6 | 27.7 |
| Released | 2024-07-23 | 2025-04-05 |
| Weights | Open | Open |
| Context window | 128K | 128K |
| Max output | 4K | 4K |
| Input $ / M tokens | $0.40 | $0.10 |
| Output $ / M tokens | $0.40 | $0.30 |
| Results tracked | 35 | 43 |
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Category by category
Coding Llama 3.1-70B leads
Llama 3.1-70B: 30.3 (#296), Llama 4 Scout: 20.2 (#339)
| Benchmark | Llama 3.1-70B | Llama 4 Scout |
|---|---|---|
| LMArena Coding | 1260 | 1286 |
| BigCodeBench Complete | 54.8% | 43.1% |
| SWE-bench Verified (bash only) | — | 9.1% |
| SciCode | — | 17% |
| WeirdML | 9% | — |
| BigCodeBench Instruct | 46.1% | — |
Agentic & Tool Use Too close to call
Llama 3.1-70B: 25.1 (#112), Llama 4 Scout: 24.6 (#119)
| Benchmark | Llama 3.1-70B | Llama 4 Scout |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 28.1% |
| TheAgentCompany | 6.9% | — |
| BALROG | 27.9% | — |
Reasoning Llama 3.1-70B leads
Llama 3.1-70B: 21.6 (#220), Llama 4 Scout: 9.1 (#345)
| Benchmark | Llama 3.1-70B | Llama 4 Scout |
|---|---|---|
| LMArena Hard Prompts | 1241 | 1266 |
| DTBench | 60% | 57.9% |
| LMCA | 14.8% | 12% |
| Epoch Capabilities Index | 125.92 | 129.64 |
| ARC-AGI-2 | — | 0% |
| Kagi LLM Benchmark | — | 36.9% |
| ARC-AGI-1 | — | 0.5% |
| CritPt | — | 0% |
| ForecastBench | — | 57.5 |
Math Llama 4 Scout leads
Llama 3.1-70B: 13.5 (#304), Llama 4 Scout: 19.6 (#286)
| Benchmark | Llama 3.1-70B | Llama 4 Scout |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 3.6% | 7.8% |
| Omni-MATH | 21% | 37.3% |
| LMArena Math | 1252 | 1287 |
| MATH Level 5 | 36.7% | 62.3% |
| FrontierMath (Feb 2025 set) | — | 0% |
Knowledge Llama 4 Scout leads
Llama 3.1-70B: 24.2 (#269), Llama 4 Scout: 31.9 (#217)
| Benchmark | Llama 3.1-70B | Llama 4 Scout |
|---|---|---|
| GPQA Diamond | 44.2% | 51.8% |
| MMLU-Pro | 65.3% | 74.2% |
| GPQA (HELM) | 42.6% | 50.7% |
| LMArena Expert | 1209 | 1235 |
| Vectara Hallucination Rate | — | 7.7% |
| MMLU | 80.1% | — |
Multimodal Not comparable
Llama 3.1-70B: —, Llama 4 Scout: 32.2 (#102)
| Benchmark | Llama 3.1-70B | Llama 4 Scout |
|---|---|---|
| LMArena Vision | — | 1118 |
| SpatialViz-Bench | — | 34.2% |
Multilingual Llama 4 Scout leads
Llama 3.1-70B: 38.8 (#225), Llama 4 Scout: 41.0 (#212)
| Benchmark | Llama 3.1-70B | Llama 4 Scout |
|---|---|---|
| LMArena Non-English | 1219 | 1252 |
| LMArena Chinese | 1215 | 1255 |
| LMArena French | 1261 | 1282 |
| LMArena German | 1222 | 1272 |
| LMArena Japanese | 1132 | 1206 |
| LMArena Korean | 1140 | 1207 |
| LMArena Russian | 1234 | 1263 |
| LMArena Spanish | 1253 | 1278 |
Instruction Following Too close to call
Llama 3.1-70B: 65.3 (#223), Llama 4 Scout: 65.8 (#217)
| Benchmark | Llama 3.1-70B | Llama 4 Scout |
|---|---|---|
| IFEval | 82.1% | 81.8% |
| LMArena Instruction Following | 1231 | 1248 |
Long Context Llama 3.1-70B leads
Llama 3.1-70B: 37.6 (#214), Llama 4 Scout: 27.5 (#294)
| Benchmark | Llama 3.1-70B | Llama 4 Scout |
|---|---|---|
| LMArena Longer Query | 1241 | 1265 |
| Fiction.LiveBench | — | 36% |
Writing & Preference Llama 4 Scout leads
Llama 3.1-70B: 35.4 (#267), Llama 4 Scout: 37.0 (#261)
| Benchmark | Llama 3.1-70B | Llama 4 Scout |
|---|---|---|
| LMArena Text | 1261 | 1279 |
| LMArena Creative Writing | 1232 | 1249 |
| EQ-Bench Creative Writing | 784 | 783 |
| WildBench | 75.8% | 78% |
| LMArena Multi-Turn | 1256 | 1280 |
Frequently asked questions
Is Llama 3.1-70B better than Llama 4 Scout?
Llama 3.1-70B is the stronger model overall, scoring 29.6 to 27.7 on the Noometry Index. Llama 4 Scout costs 2.7× less per token, which makes it the better buy when Llama 3.1-70B's lead doesn't matter for your workload.
Which is cheaper, Llama 3.1-70B or Llama 4 Scout?
Llama 4 Scout is cheaper. It lists at $0.10 per million input tokens and $0.30 per million output tokens; Llama 3.1-70B lists at $0.40 and $0.40.
Is Llama 3.1-70B or Llama 4 Scout better for coding?
Llama 3.1-70B scores higher on coding benchmarks: 30.3 versus 20.2 in the Noometry coding category.
Which has the bigger context window?
Both accept 128K tokens.
How many benchmarks do Llama 3.1-70B and Llama 4 Scout share?
30 benchmarks have published results for both models. Llama 3.1-70B has 35 scored results on Noometry and Llama 4 Scout has 43.