Model comparison
GPT-4 vs Llama 4 Scout
GPT-4 is the stronger model overall, scoring 29.1 to 27.7 on the Noometry Index. Llama 4 Scout costs 250× less per token, which makes it the better buy when GPT-4's lead doesn't matter for your workload.
Last verified . 26 shared benchmarks.
MetaSummary
- They share 26 benchmarks with published results for both. GPT-4 scores higher in 3 categories and Llama 4 Scout in 5 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Llama 4 Scout leads 31.9 to 18.4.
- The biggest single-benchmark swing is MATH Level 5: 23% for GPT-4 and 62.3% for Llama 4 Scout.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $30 / $60 for GPT-4.
- Llama 4 Scout accepts more context: 128K tokens versus 8K.
- Llama 4 Scout has downloadable open weights; the other is API-only.
Side by side
| GPT-4 | Llama 4 Scout | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 29.1 | 27.7 |
| Released | 2023-03-14 | 2025-04-05 |
| Weights | Proprietary | Open |
| Context window | 8K | 128K |
| Max output | 8K | 4K |
| Input $ / M tokens | $30 | $0.10 |
| Output $ / M tokens | $60 | $0.30 |
| Results tracked | 38 | 43 |
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Category by category
Coding GPT-4 leads
GPT-4: 31.6 (#283), Llama 4 Scout: 20.2 (#339)
| Benchmark | GPT-4 | Llama 4 Scout |
|---|---|---|
| LMArena Coding | 1254 | 1286 |
| BigCodeBench Complete | 57.2% | 43.1% |
| SWE-bench Verified (bash only) | — | 9.1% |
| SciCode | — | 17% |
| WeirdML | 12.4% | — |
| BigCodeBench Instruct | 46% | — |
| HumanEval+ | 79.3% | — |
Agentic & Tool Use Not comparable
GPT-4: —, Llama 4 Scout: 24.6 (#119)
| Benchmark | GPT-4 | Llama 4 Scout |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 28.1% |
| METR Time Horizons | 36.1% | — |
Reasoning GPT-4 leads
GPT-4: 17.8 (#289), Llama 4 Scout: 9.1 (#345)
| Benchmark | GPT-4 | Llama 4 Scout |
|---|---|---|
| LMArena Hard Prompts | 1241 | 1266 |
| DTBench | 62.7% | 57.9% |
| LMCA | 17.1% | 12% |
| Epoch Capabilities Index | 125.89 | 129.64 |
| ForecastBench | 57.8 | 57.5 |
| ARC-AGI-2 | — | 0% |
| Kagi LLM Benchmark | — | 36.9% |
| ARC-AGI-1 | — | 0.5% |
| CritPt | — | 0% |
| Chess Puzzles | 4% | — |
| Mystery Game Puzzles | 12% | — |
| BIG-Bench Hard | 75.1% | — |
| HellaSwag | 95.3% | — |
| WinoGrande | 87.5% | — |
Math Llama 4 Scout leads
GPT-4: 10.8 (#309), Llama 4 Scout: 19.6 (#286)
| Benchmark | GPT-4 | Llama 4 Scout |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 1.1% | 7.8% |
| LMArena Math | 1269 | 1287 |
| MATH Level 5 | 23% | 62.3% |
| Omni-MATH | — | 37.3% |
| FrontierMath (Feb 2025 set) | — | 0% |
| GSM8K | 92% | — |
Knowledge Llama 4 Scout leads
GPT-4: 18.4 (#282), Llama 4 Scout: 31.9 (#217)
| Benchmark | GPT-4 | Llama 4 Scout |
|---|---|---|
| GPQA Diamond | 35.7% | 51.8% |
| LMArena Expert | 1211 | 1235 |
| MMLU-Pro | — | 74.2% |
| Vectara Hallucination Rate | — | 7.7% |
| GPQA (HELM) | — | 50.7% |
| MMLU | 86.4% | — |
| TriviaQA | 84.8% | — |
Multimodal Not comparable
GPT-4: —, Llama 4 Scout: 32.2 (#102)
| Benchmark | GPT-4 | Llama 4 Scout |
|---|---|---|
| LMArena Vision | — | 1118 |
| SpatialViz-Bench | — | 34.2% |
Multilingual Too close to call
GPT-4: 40.6 (#215), Llama 4 Scout: 41.0 (#212)
| Benchmark | GPT-4 | Llama 4 Scout |
|---|---|---|
| LMArena Non-English | 1246 | 1252 |
| LMArena Chinese | 1242 | 1255 |
| LMArena French | 1283 | 1282 |
| LMArena German | 1251 | 1272 |
| LMArena Japanese | 1209 | 1206 |
| LMArena Korean | 1184 | 1207 |
| LMArena Russian | 1251 | 1263 |
| LMArena Spanish | 1261 | 1278 |
Instruction Following Too close to call
GPT-4: 65.3 (#222), Llama 4 Scout: 65.8 (#217)
| Benchmark | GPT-4 | Llama 4 Scout |
|---|---|---|
| LMArena Instruction Following | 1241 | 1248 |
| IFEval | — | 81.8% |
Long Context GPT-4 leads
GPT-4: 37.7 (#212), Llama 4 Scout: 27.5 (#294)
| Benchmark | GPT-4 | Llama 4 Scout |
|---|---|---|
| LMArena Longer Query | 1244 | 1265 |
| Fiction.LiveBench | — | 36% |
Writing & Preference Llama 4 Scout leads
GPT-4: 34.9 (#268), Llama 4 Scout: 37.0 (#261)
| Benchmark | GPT-4 | Llama 4 Scout |
|---|---|---|
| LMArena Text | 1263 | 1279 |
| LMArena Creative Writing | 1244 | 1249 |
| EQ-Bench Creative Writing | 752 | 783 |
| LMArena Multi-Turn | 1257 | 1280 |
| WildBench | — | 78% |
Frequently asked questions
Is GPT-4 better than Llama 4 Scout?
GPT-4 is the stronger model overall, scoring 29.1 to 27.7 on the Noometry Index. Llama 4 Scout costs 250× less per token, which makes it the better buy when GPT-4's lead doesn't matter for your workload.
Which is cheaper, GPT-4 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 lists at $30 and $60.
Is GPT-4 or Llama 4 Scout better for coding?
GPT-4 scores higher on coding benchmarks: 31.6 versus 20.2 in the Noometry coding category.
Which has the bigger context window?
Llama 4 Scout does, with 128K tokens against 8K.
How many benchmarks do GPT-4 and Llama 4 Scout share?
26 benchmarks have published results for both models. GPT-4 has 38 scored results on Noometry and Llama 4 Scout has 43.