Model comparison
GPT-6 Astra vs Llama 4 Scout
GPT-6 Astra is the stronger model overall, scoring 70.8 to 27.7 on the Noometry Index. Llama 4 Scout costs 133× less per token, which makes it the better buy when GPT-6 Astra's lead doesn't matter for your workload.
Last verified . 29 shared benchmarks.
MetaSummary
- They share 29 benchmarks with published results for both. GPT-6 Astra 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 Astra leads 85.1 to 9.1.
- The biggest single-benchmark swing is ARC-AGI-1: 98.5% for GPT-6 Astra and 0.5% for Llama 4 Scout.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $10 / $50 for GPT-6 Astra.
- GPT-6 Astra 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 Astra | Llama 4 Scout | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 70.8 | 27.7 |
| Released | 2026-09-03 | 2025-04-05 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 128K |
| Max output | 128K | 4K |
| Input $ / M tokens | $10 | $0.10 |
| Output $ / M tokens | $50 | $0.30 |
| Results tracked | 56 | 43 |
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Category by category
Coding GPT-6 Astra leads
GPT-6 Astra: 73.7 (#2), Llama 4 Scout: 20.2 (#339)
| Benchmark | GPT-6 Astra | Llama 4 Scout |
|---|---|---|
| SciCode | 56.5% | 17% |
| LMArena Coding | 1487 | 1286 |
| DeepSWE | 74.1% | — |
| FrontierCode | 53.3% | — |
| SWE-bench Verified (bash only) | — | 9.1% |
| LMArena WebDev | 1786 | — |
| FrontierSWE | 65.5% | — |
| GSO | 79.4% | — |
| WeirdML | 93.6% | — |
| MirrorCode | 46.7% | — |
| BigCodeBench Complete | — | 43.1% |
| ALE-Bench | 2,951 | — |
Agentic & Tool Use GPT-6 Astra leads
GPT-6 Astra: 52.9 (#3), Llama 4 Scout: 24.6 (#119)
| Benchmark | GPT-6 Astra | Llama 4 Scout |
|---|---|---|
| APEX-Agents | 64.7% | — |
| Berkeley Function Calling Leaderboard | — | 28.1% |
| Remote Labor Index | 20.8% | — |
| BALROG | 68.3% | — |
| GDP.pdf | 34.2% | — |
| Vending-Bench 2 | 15,515 | — |
Reasoning GPT-6 Astra leads
GPT-6 Astra: 85.1 (#1), Llama 4 Scout: 9.1 (#345)
| Benchmark | GPT-6 Astra | Llama 4 Scout |
|---|---|---|
| ARC-AGI-2 | 95% | 0% |
| ARC-AGI-1 | 98.5% | 0.5% |
| CritPt | 31.7% | 0% |
| LMArena Hard Prompts | 1462 | 1266 |
| DTBench | 97.3% | 57.9% |
| LMCA | 64.4% | 12% |
| Epoch Capabilities Index | 166.45 | 129.64 |
| Kagi LLM Benchmark | — | 36.9% |
| NYT Connections (extended) | 98.1% | — |
| Chess Puzzles | 72% | — |
| EBR-Bench | 76.2% | — |
| Mystery Game Puzzles | 84% | — |
| Bench to the Future 3 | 0.14 | — |
| ForecastBench | — | 57.5 |
Math GPT-6 Astra leads
GPT-6 Astra: 93.5 (#2), Llama 4 Scout: 19.6 (#286)
| Benchmark | GPT-6 Astra | Llama 4 Scout |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 100% | 7.8% |
| LMArena Math | 1465 | 1287 |
| FrontierMath (Tiers 1-3) | 93.7% | — |
| FrontierMath Tier 4 | 97.6% | — |
| ProofBench | 99% | — |
| Omni-MATH | — | 37.3% |
| MATH Level 5 | — | 62.3% |
| FrontierMath (Feb 2025 set) | — | 0% |
| FrontierMath Erdős | 2.9% | — |
Knowledge GPT-6 Astra leads
GPT-6 Astra: 75.3 (#1), Llama 4 Scout: 31.9 (#217)
| Benchmark | GPT-6 Astra | Llama 4 Scout |
|---|---|---|
| GPQA Diamond | 95.8% | 51.8% |
| Vectara Hallucination Rate | 8.7% | 7.7% |
| LMArena Expert | 1483 | 1235 |
| Humanity's Last Exam | 54.8% | — |
| SimpleQA Verified | 75.6% | — |
| MMLU-Pro | — | 74.2% |
| GPQA (HELM) | — | 50.7% |
Multimodal GPT-6 Astra leads
GPT-6 Astra: 55.0 (#3), Llama 4 Scout: 32.2 (#102)
| Benchmark | GPT-6 Astra | Llama 4 Scout |
|---|---|---|
| LMArena Vision | 1281 | 1118 |
| Blueprint-Bench 2 | 49.7% | — |
| Furniture Assembly | 80% | — |
| LMArena Document | 1468 | — |
| SpatialViz-Bench | — | 34.2% |
Multilingual GPT-6 Astra leads
GPT-6 Astra: 53.7 (#61), Llama 4 Scout: 41.0 (#212)
| Benchmark | GPT-6 Astra | Llama 4 Scout |
|---|---|---|
| LMArena Non-English | 1430 | 1252 |
| LMArena Chinese | 1484 | 1255 |
| LMArena French | 1456 | 1282 |
| LMArena German | 1440 | 1272 |
| LMArena Japanese | 1379 | 1206 |
| LMArena Korean | 1426 | 1207 |
| LMArena Russian | 1436 | 1263 |
| LMArena Spanish | 1407 | 1278 |
Instruction Following GPT-6 Astra leads
GPT-6 Astra: 76.3 (#44), Llama 4 Scout: 65.8 (#217)
| Benchmark | GPT-6 Astra | Llama 4 Scout |
|---|---|---|
| LMArena Instruction Following | 1450 | 1248 |
| IFEval | — | 81.8% |
Long Context GPT-6 Astra leads
GPT-6 Astra: 44.5 (#62), Llama 4 Scout: 27.5 (#294)
| Benchmark | GPT-6 Astra | Llama 4 Scout |
|---|---|---|
| LMArena Longer Query | 1456 | 1265 |
| Fiction.LiveBench | — | 36% |
Writing & Preference GPT-6 Astra leads
GPT-6 Astra: 75.3 (#7), Llama 4 Scout: 37.0 (#261)
| Benchmark | GPT-6 Astra | Llama 4 Scout |
|---|---|---|
| LMArena Text | 1441 | 1279 |
| LMArena Creative Writing | 1418 | 1249 |
| EQ-Bench Creative Writing | 2173 | 783 |
| LMArena Multi-Turn | 1448 | 1280 |
| WildBench | — | 78% |
Frequently asked questions
Is GPT-6 Astra better than Llama 4 Scout?
GPT-6 Astra is the stronger model overall, scoring 70.8 to 27.7 on the Noometry Index. Llama 4 Scout costs 133× less per token, which makes it the better buy when GPT-6 Astra's lead doesn't matter for your workload.
Which is cheaper, GPT-6 Astra 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 Astra lists at $10 and $50.
Is GPT-6 Astra or Llama 4 Scout better for coding?
GPT-6 Astra scores higher on coding benchmarks: 73.7 versus 20.2 in the Noometry coding category.
Which has the bigger context window?
GPT-6 Astra does, with 1.05M tokens against 128K.
How many benchmarks do GPT-6 Astra and Llama 4 Scout share?
29 benchmarks have published results for both models. GPT-6 Astra has 56 scored results on Noometry and Llama 4 Scout has 43.