Model comparison
GPT-5.5 vs Llama 4 Scout
GPT-5.5 is the stronger model overall, scoring 63.4 to 27.7 on the Noometry Index. Llama 4 Scout costs 75× less per token, which makes it the better buy when GPT-5.5's lead doesn't matter for your workload.
Last verified . 32 shared benchmarks.
MetaSummary
- They share 32 benchmarks with published results for both. GPT-5.5 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-5.5 leads 72.8 to 9.1.
- The biggest single-benchmark swing is ARC-AGI-1: 95% for GPT-5.5 and 0.5% for Llama 4 Scout.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $5 / $30 for GPT-5.5.
- GPT-5.5 accepts more context: 1.05M tokens versus 128K.
- Llama 4 Scout has downloadable open weights; the other is API-only.
Side by side
| GPT-5.5 | Llama 4 Scout | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 63.4 | 27.7 |
| Released | 2026-04-23 | 2025-04-05 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 128K |
| Max output | 128K | 4K |
| Input $ / M tokens | $5 | $0.10 |
| Output $ / M tokens | $30 | $0.30 |
| Results tracked | 71 | 43 |
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Category by category
Coding GPT-5.5 leads
GPT-5.5: 58.2 (#17), Llama 4 Scout: 20.2 (#339)
| Benchmark | GPT-5.5 | Llama 4 Scout |
|---|---|---|
| SciCode | 56.1% | 17% |
| LMArena Coding | 1494 | 1286 |
| SWE-bench Verified | 80.6% | — |
| DeepSWE | 67% | — |
| FrontierCode | 43% | — |
| SWE-bench Verified (bash only) | — | 9.1% |
| LMArena WebDev | 1513 | — |
| GSO | 40.2% | — |
| WeirdML | 84.9% | — |
| MirrorCode | 10% | — |
| BigCodeBench Complete | — | 43.1% |
| ALE-Bench | 1,943 | — |
Agentic & Tool Use GPT-5.5 leads
GPT-5.5: 50.7 (#6), Llama 4 Scout: 24.6 (#119)
| Benchmark | GPT-5.5 | Llama 4 Scout |
|---|---|---|
| Terminal-Bench | 84.7% | — |
| APEX-Agents | 55.1% | — |
| Berkeley Function Calling Leaderboard | — | 28.1% |
| OSWorld 2.0 | 13% | — |
| Remote Labor Index | 6.3% | — |
| τ²-bench Banking | 44.6% | — |
| DeepResearch Bench | 54% | — |
| PostTrainBench | 27.2% | — |
| ExploitBench | 47.4% | — |
| GBAEval | 53.2% | — |
| GDP.pdf | 26% | — |
| LMArena Search | 1242 | — |
| Vending-Bench 2 | 7,524 | — |
Reasoning GPT-5.5 leads
GPT-5.5: 72.8 (#11), Llama 4 Scout: 9.1 (#345)
| Benchmark | GPT-5.5 | Llama 4 Scout |
|---|---|---|
| ARC-AGI-2 | 85% | 0% |
| Kagi LLM Benchmark | 88.8% | 36.9% |
| ARC-AGI-1 | 95% | 0.5% |
| CritPt | 27.1% | 0% |
| LMArena Hard Prompts | 1489 | 1266 |
| DTBench | 96% | 57.9% |
| LMCA | 54.3% | 12% |
| Epoch Capabilities Index | 159.1 | 129.64 |
| ForecastBench | 60.6 | 57.5 |
| SimpleBench | 69% | — |
| NYT Connections (extended) | 96.2% | — |
| Chess Puzzles | 54% | — |
| EBR-Bench | 34.3% | — |
| Mystery Game Puzzles | 56% | — |
| Surface Evolver Bench | 88.1% | — |
| Bench to the Future 3 | 0.14 | — |
Math GPT-5.5 leads
GPT-5.5: 81.7 (#11), Llama 4 Scout: 19.6 (#286)
| Benchmark | GPT-5.5 | Llama 4 Scout |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 100% | 7.8% |
| LMArena Math | 1486 | 1287 |
| FrontierMath (Feb 2025 set) | 51.7% | 0% |
| FrontierMath (Tiers 1-3) | 85.3% | — |
| FrontierMath Tier 4 | 72.5% | — |
| MathArena Final-Answer Competitions | 94.3% | — |
| ProofBench | 50% | — |
| Omni-MATH | — | 37.3% |
| MATH Level 5 | — | 62.3% |
| FrontierMath Erdős | 0% | — |
| FrontierMath Tier 4 (v1) | 35.4% | — |
Knowledge GPT-5.5 leads
GPT-5.5: 64.4 (#17), Llama 4 Scout: 31.9 (#217)
| Benchmark | GPT-5.5 | Llama 4 Scout |
|---|---|---|
| GPQA Diamond | 94% | 51.8% |
| Vectara Hallucination Rate | 9.3% | 7.7% |
| LMArena Expert | 1508 | 1235 |
| SimpleQA Verified | 63% | — |
| MMLU-Pro | — | 74.2% |
| GPQA (HELM) | — | 50.7% |
Multimodal GPT-5.5 leads
GPT-5.5: 46.9 (#12), Llama 4 Scout: 32.2 (#102)
| Benchmark | GPT-5.5 | Llama 4 Scout |
|---|---|---|
| LMArena Vision | 1297 | 1118 |
| Blueprint-Bench 2 | 36.2% | — |
| Furniture Assembly | 44.2% | — |
| LMArena Document | 1486 | — |
| SpatialViz-Bench | — | 34.2% |
Multilingual GPT-5.5 leads
GPT-5.5: 56.4 (#20), Llama 4 Scout: 41.0 (#212)
| Benchmark | GPT-5.5 | Llama 4 Scout |
|---|---|---|
| LMArena Non-English | 1467 | 1252 |
| LMArena Chinese | 1533 | 1255 |
| LMArena French | 1486 | 1282 |
| LMArena German | 1480 | 1272 |
| LMArena Japanese | 1498 | 1206 |
| LMArena Korean | 1460 | 1207 |
| LMArena Russian | 1473 | 1263 |
| LMArena Spanish | 1468 | 1278 |
Instruction Following GPT-5.5 leads
GPT-5.5: 77.5 (#18), Llama 4 Scout: 65.8 (#217)
| Benchmark | GPT-5.5 | Llama 4 Scout |
|---|---|---|
| LMArena Instruction Following | 1479 | 1248 |
| IFEval | — | 81.8% |
Long Context GPT-5.5 leads
GPT-5.5: 48.3 (#12), Llama 4 Scout: 27.5 (#294)
| Benchmark | GPT-5.5 | Llama 4 Scout |
|---|---|---|
| LMArena Longer Query | 1484 | 1265 |
| Fiction.LiveBench | — | 36% |
| CL-bench Life | 22.2% | — |
Writing & Preference GPT-5.5 leads
GPT-5.5: 72.7 (#13), Llama 4 Scout: 37.0 (#261)
| Benchmark | GPT-5.5 | Llama 4 Scout |
|---|---|---|
| LMArena Text | 1472 | 1279 |
| LMArena Creative Writing | 1455 | 1249 |
| EQ-Bench Creative Writing | 1844 | 783 |
| LMArena Multi-Turn | 1476 | 1280 |
| WildBench | — | 78% |
| EQ-Bench 4 | 1315 | — |
Frequently asked questions
Is GPT-5.5 better than Llama 4 Scout?
GPT-5.5 is the stronger model overall, scoring 63.4 to 27.7 on the Noometry Index. Llama 4 Scout costs 75× less per token, which makes it the better buy when GPT-5.5's lead doesn't matter for your workload.
Which is cheaper, GPT-5.5 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-5.5 lists at $5 and $30.
Is GPT-5.5 or Llama 4 Scout better for coding?
GPT-5.5 scores higher on coding benchmarks: 58.2 versus 20.2 in the Noometry coding category.
Which has the bigger context window?
GPT-5.5 does, with 1.05M tokens against 128K.
How many benchmarks do GPT-5.5 and Llama 4 Scout share?
32 benchmarks have published results for both models. GPT-5.5 has 71 scored results on Noometry and Llama 4 Scout has 43.