Model comparison
GPT-4.1 vs Llama 4 Scout
GPT-4.1 is the stronger model overall, scoring 35.9 to 27.7 on the Noometry Index. Llama 4 Scout costs 23× less per token, which makes it the better buy when GPT-4.1's lead doesn't matter for your workload.
Last verified . 39 shared benchmarks.
MetaSummary
- They share 39 benchmarks with published results for both. GPT-4.1 scores higher in 10 categories and Llama 4 Scout in 0 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GPT-4.1 leads 57.6 to 37.0.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 38.3% for GPT-4.1 and 7.8% for Llama 4 Scout.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $2 / $8 for GPT-4.1.
- GPT-4.1 accepts more context: 1.05M tokens versus 128K.
- Llama 4 Scout has downloadable open weights; the other is API-only.
Side by side
| GPT-4.1 | Llama 4 Scout | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 35.9 | 27.7 |
| Released | 2025-04-14 | 2025-04-05 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 128K |
| Max output | 33K | 4K |
| Input $ / M tokens | $2 | $0.10 |
| Output $ / M tokens | $8 | $0.30 |
| Results tracked | 52 | 43 |
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Category by category
Coding GPT-4.1 leads
GPT-4.1: 34.4 (#238), Llama 4 Scout: 20.2 (#339)
| Benchmark | GPT-4.1 | Llama 4 Scout |
|---|---|---|
| SWE-bench Verified (bash only) | 39.6% | 9.1% |
| LMArena Coding | 1391 | 1286 |
| SWE-bench Verified | 48.5% | — |
| Aider Polyglot | 52.4% | — |
| SciCode | — | 17% |
| WeirdML | 39% | — |
| BigCodeBench Complete | — | 43.1% |
| CadEval | 42% | — |
| ALE-Bench | 558.1 | — |
Agentic & Tool Use GPT-4.1 leads
GPT-4.1: 34.7 (#43), Llama 4 Scout: 24.6 (#119)
| Benchmark | GPT-4.1 | Llama 4 Scout |
|---|---|---|
| Berkeley Function Calling Leaderboard | 54% | 28.1% |
Reasoning GPT-4.1 leads
GPT-4.1: 11.7 (#339), Llama 4 Scout: 9.1 (#345)
| Benchmark | GPT-4.1 | Llama 4 Scout |
|---|---|---|
| ARC-AGI-2 | 0.4% | 0% |
| Kagi LLM Benchmark | 52.3% | 36.9% |
| ARC-AGI-1 | 5.5% | 0.5% |
| LMArena Hard Prompts | 1384 | 1266 |
| DTBench | 68.3% | 57.9% |
| LMCA | 25.6% | 12% |
| Epoch Capabilities Index | 136.78 | 129.64 |
| ForecastBench | 61.5 | 57.5 |
| SimpleBench | 27% | — |
| CritPt | — | 0% |
| Chess Puzzles | 6% | — |
| EnigmaEval | 2.2% | — |
Math GPT-4.1 leads
GPT-4.1: 22.3 (#280), Llama 4 Scout: 19.6 (#286)
| Benchmark | GPT-4.1 | Llama 4 Scout |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 38.3% | 7.8% |
| Omni-MATH | 47.1% | 37.3% |
| LMArena Math | 1370 | 1287 |
| MATH Level 5 | 83% | 62.3% |
| FrontierMath (Feb 2025 set) | 5.5% | 0% |
| FrontierMath (Tiers 1-3) | 6% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
Knowledge GPT-4.1 leads
GPT-4.1: 37.1 (#160), Llama 4 Scout: 31.9 (#217)
| Benchmark | GPT-4.1 | Llama 4 Scout |
|---|---|---|
| GPQA Diamond | 66.9% | 51.8% |
| MMLU-Pro | 81.1% | 74.2% |
| Vectara Hallucination Rate | 5.6% | 7.7% |
| GPQA (HELM) | 65.9% | 50.7% |
| LMArena Expert | 1364 | 1235 |
| Humanity's Last Exam | 5.4% | — |
| SimpleQA Verified | 31.1% | — |
Multimodal GPT-4.1 leads
GPT-4.1: 38.2 (#67), Llama 4 Scout: 32.2 (#102)
| Benchmark | GPT-4.1 | Llama 4 Scout |
|---|---|---|
| LMArena Vision | 1211 | 1118 |
| GeoBench | 72% | — |
| SpatialViz-Bench | — | 34.2% |
Multilingual GPT-4.1 leads
GPT-4.1: 49.4 (#133), Llama 4 Scout: 41.0 (#212)
| Benchmark | GPT-4.1 | Llama 4 Scout |
|---|---|---|
| LMArena Non-English | 1370 | 1252 |
| LMArena Chinese | 1382 | 1255 |
| LMArena French | 1382 | 1282 |
| LMArena German | 1381 | 1272 |
| LMArena Japanese | 1319 | 1206 |
| LMArena Korean | 1339 | 1207 |
| LMArena Russian | 1377 | 1263 |
| LMArena Spanish | 1376 | 1278 |
Instruction Following GPT-4.1 leads
GPT-4.1: 71.3 (#153), Llama 4 Scout: 65.8 (#217)
| Benchmark | GPT-4.1 | Llama 4 Scout |
|---|---|---|
| IFEval | 83.8% | 81.8% |
| LMArena Instruction Following | 1367 | 1248 |
Long Context GPT-4.1 leads
GPT-4.1: 40.0 (#163), Llama 4 Scout: 27.5 (#294)
| Benchmark | GPT-4.1 | Llama 4 Scout |
|---|---|---|
| Fiction.LiveBench | 63.9% | 36% |
| LMArena Longer Query | 1385 | 1265 |
Writing & Preference GPT-4.1 leads
GPT-4.1: 57.6 (#125), Llama 4 Scout: 37.0 (#261)
| Benchmark | GPT-4.1 | Llama 4 Scout |
|---|---|---|
| LMArena Text | 1383 | 1279 |
| LMArena Creative Writing | 1363 | 1249 |
| EQ-Bench Creative Writing | 1420 | 783 |
| WildBench | 85.4% | 78% |
| LMArena Multi-Turn | 1398 | 1280 |
Frequently asked questions
Is GPT-4.1 better than Llama 4 Scout?
GPT-4.1 is the stronger model overall, scoring 35.9 to 27.7 on the Noometry Index. Llama 4 Scout costs 23× less per token, which makes it the better buy when GPT-4.1's lead doesn't matter for your workload.
Which is cheaper, GPT-4.1 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; GPT-4.1 lists at $2 and $8.
Is GPT-4.1 or Llama 4 Scout better for coding?
GPT-4.1 scores higher on coding benchmarks: 34.4 versus 20.2 in the Noometry coding category.
Which has the bigger context window?
GPT-4.1 does, with 1.05M tokens against 128K.
How many benchmarks do GPT-4.1 and Llama 4 Scout share?
39 benchmarks have published results for both models. GPT-4.1 has 52 scored results on Noometry and Llama 4 Scout has 43.