Model comparison
GPT-6 Sol vs Llama 4 Scout
GPT-6 Sol is the stronger model overall, scoring 49.2 to 27.4 on the Noometry Index. Llama 4 Scout costs 27× less per token, which makes it the better buy when GPT-6 Sol's lead doesn't matter for your workload.
Last verified . 15 shared benchmarks.
MetaSummary
- They share 15 benchmarks with published results for both. GPT-6 Sol scores higher in 10 categories and Llama 4 Scout in 0 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-6 Sol leads 49.3 to 9.2.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $2 / $10 for GPT-6 Sol.
- GPT-6 Sol accepts more context: 1.05M tokens versus 128K.
- Llama 4 Scout has downloadable open weights; the other is API-only.
Side by side
| GPT-6 Sol | Llama 4 Scout | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 49.2 | 27.4 |
| Released | 2026-09-22 | 2025-04-05 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 128K |
| Max output | 128K | 4K |
| Input $ / M tokens | $2 | $0.10 |
| Output $ / M tokens | $10 | $0.30 |
| Results tracked | 22 | 43 |
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Category by category
Coding GPT-6 Sol leads
GPT-6 Sol: 59.6 (#11), Llama 4 Scout: 19.9 (#334)
| Benchmark | GPT-6 Sol | Llama 4 Scout |
|---|---|---|
| LMArena Coding | 1453 | 1286 |
| DeepSWE | 68.8% | — |
| FrontierCode | 49.3% | — |
| SWE-bench Verified (bash only) | — | 9.1% |
| LMArena WebDev | 1692 | — |
| SciCode | — | 17% |
| BigCodeBench Complete | — | 43.1% |
Agentic & Tool Use GPT-6 Sol leads
GPT-6 Sol: 33.5 (#49), Llama 4 Scout: 24.4 (#109)
| Benchmark | GPT-6 Sol | Llama 4 Scout |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 28.1% |
| GDP.pdf | 28% | — |
Reasoning GPT-6 Sol leads
GPT-6 Sol: 49.3 (#34), Llama 4 Scout: 9.2 (#339)
| Benchmark | GPT-6 Sol | Llama 4 Scout |
|---|---|---|
| LMArena Hard Prompts | 1420 | 1266 |
| ARC-AGI-2 | — | 0% |
| Kagi LLM Benchmark | — | 36.9% |
| NYT Connections (extended) | 90.1% | — |
| ARC-AGI-1 | — | 0.5% |
| CritPt | — | 0% |
| EBR-Bench | 53.3% | — |
| DTBench | — | 57.9% |
| LMCA | — | 12% |
| Epoch Capabilities Index | — | 129.64 |
| ForecastBench | — | 57.5 |
Math GPT-6 Sol leads
GPT-6 Sol: 35.8 (#135), Llama 4 Scout: 18.4 (#280)
| Benchmark | GPT-6 Sol | Llama 4 Scout |
|---|---|---|
| LMArena Math | 1404 | 1286 |
| OTIS Mock AIME 2024-2025 | — | 7.8% |
| Omni-MATH | — | 37.3% |
| MATH Level 5 | — | 62.3% |
| FrontierMath (Feb 2025 set) | — | 0% |
Knowledge GPT-6 Sol leads
GPT-6 Sol: 43.0 (#87), Llama 4 Scout: 31.8 (#208)
| Benchmark | GPT-6 Sol | Llama 4 Scout |
|---|---|---|
| Vectara Hallucination Rate | 6.5% | 7.7% |
| LMArena Expert | 1449 | 1236 |
| GPQA Diamond | — | 51.8% |
| MMLU-Pro | — | 74.2% |
| GPQA (HELM) | — | 50.7% |
Multimodal GPT-6 Sol leads
GPT-6 Sol: 47.6 (#9), Llama 4 Scout: 31.6 (#98)
| Benchmark | GPT-6 Sol | Llama 4 Scout |
|---|---|---|
| LMArena Vision | — | 1118 |
| Furniture Assembly | 58.3% | — |
| SpatialViz-Bench | — | 34.2% |
Multilingual GPT-6 Sol leads
GPT-6 Sol: 51.0 (#113), Llama 4 Scout: 41.3 (#207)
| Benchmark | GPT-6 Sol | Llama 4 Scout |
|---|---|---|
| LMArena Non-English | 1389 | 1253 |
| LMArena Chinese | 1416 | 1256 |
| LMArena Russian | 1407 | 1263 |
| LMArena Spanish | 1377 | 1277 |
| LMArena French | — | 1283 |
| LMArena German | — | 1274 |
| LMArena Japanese | — | 1209 |
| LMArena Korean | — | 1207 |
Instruction Following GPT-6 Sol leads
GPT-6 Sol: 74.4 (#97), Llama 4 Scout: 66.0 (#212)
| Benchmark | GPT-6 Sol | Llama 4 Scout |
|---|---|---|
| LMArena Instruction Following | 1406 | 1248 |
| IFEval | — | 81.8% |
Long Context GPT-6 Sol leads
GPT-6 Sol: 43.2 (#100), Llama 4 Scout: 27.5 (#289)
| Benchmark | GPT-6 Sol | Llama 4 Scout |
|---|---|---|
| LMArena Longer Query | 1415 | 1265 |
| Fiction.LiveBench | — | 36% |
Writing & Preference GPT-6 Sol leads
GPT-6 Sol: 72.3 (#15), Llama 4 Scout: 37.1 (#256)
| Benchmark | GPT-6 Sol | Llama 4 Scout |
|---|---|---|
| LMArena Text | 1396 | 1279 |
| LMArena Creative Writing | 1392 | 1249 |
| EQ-Bench Creative Writing | 2125 | 783 |
| LMArena Multi-Turn | 1405 | 1281 |
| WildBench | — | 78% |
Frequently asked questions
Is GPT-6 Sol better than Llama 4 Scout?
GPT-6 Sol is the stronger model overall, scoring 49.2 to 27.4 on the Noometry Index. Llama 4 Scout costs 27× less per token, which makes it the better buy when GPT-6 Sol's lead doesn't matter for your workload.
Which is cheaper, GPT-6 Sol 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-6 Sol lists at $2 and $10.
Is GPT-6 Sol or Llama 4 Scout better for coding?
GPT-6 Sol scores higher on coding benchmarks: 59.6 versus 19.9 in the Noometry coding category.
Which has the bigger context window?
GPT-6 Sol does, with 1.05M tokens against 128K.
How many benchmarks do GPT-6 Sol and Llama 4 Scout share?
15 benchmarks have published results for both models. GPT-6 Sol has 22 scored results on Noometry and Llama 4 Scout has 43.