Model comparison
GPT-5.5 Pro vs Llama 4 Scout
GPT-5.5 Pro is the stronger model overall, scoring 64.3 to 27.4 on the Noometry Index. Llama 4 Scout costs 450× less per token, which makes it the better buy when GPT-5.5 Pro's lead doesn't matter for your workload.
Last verified . 9 shared benchmarks.
MetaSummary
- They share 9 benchmarks with published results for both. GPT-5.5 Pro scores higher in 3 categories and Llama 4 Scout in 0 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.5 Pro leads 83.8 to 18.4.
- The biggest single-benchmark swing is ARC-AGI-1: 96.5% for GPT-5.5 Pro and 0.5% for Llama 4 Scout.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $30 / $180 for GPT-5.5 Pro.
- GPT-5.5 Pro 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 Pro | Llama 4 Scout | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 64.3 | 27.4 |
| Released | 2026-04-23 | 2025-04-05 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 128K |
| Max output | 128K | 4K |
| Input $ / M tokens | $30 | $0.10 |
| Output $ / M tokens | $180 | $0.30 |
| Results tracked | 14 | 43 |
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Category by category
Coding Not comparable
GPT-5.5 Pro: —, Llama 4 Scout: 19.9 (#334)
| Benchmark | GPT-5.5 Pro | Llama 4 Scout |
|---|---|---|
| SWE-bench Verified (bash only) | — | 9.1% |
| SciCode | — | 17% |
| LMArena Coding | — | 1286 |
| BigCodeBench Complete | — | 43.1% |
Agentic & Tool Use Not comparable
GPT-5.5 Pro: —, Llama 4 Scout: 24.4 (#109)
| Benchmark | GPT-5.5 Pro | Llama 4 Scout |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 28.1% |
Reasoning GPT-5.5 Pro leads
GPT-5.5 Pro: 73.6 (#7), Llama 4 Scout: 9.2 (#339)
| Benchmark | GPT-5.5 Pro | Llama 4 Scout |
|---|---|---|
| ARC-AGI-2 | 84.6% | 0% |
| ARC-AGI-1 | 96.5% | 0.5% |
| CritPt | 30.6% | 0% |
| DTBench | 96% | 57.9% |
| LMCA | 53.9% | 12% |
| Epoch Capabilities Index | 162.45 | 129.64 |
| SimpleBench | 76.9% | — |
| Kagi LLM Benchmark | — | 36.9% |
| Chess Puzzles | 64% | — |
| LMArena Hard Prompts | — | 1266 |
| ForecastBench | — | 57.5 |
Math GPT-5.5 Pro leads
GPT-5.5 Pro: 83.8 (#8), Llama 4 Scout: 18.4 (#280)
| Benchmark | GPT-5.5 Pro | Llama 4 Scout |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 100% | 7.8% |
| FrontierMath (Feb 2025 set) | 52.4% | 0% |
| FrontierMath (Tiers 1-3) | 87.7% | — |
| FrontierMath Tier 4 | 78% | — |
| Omni-MATH | — | 37.3% |
| LMArena Math | — | 1286 |
| MATH Level 5 | — | 62.3% |
| FrontierMath Tier 4 (v1) | 39.6% | — |
Knowledge GPT-5.5 Pro leads
GPT-5.5 Pro: 64.2 (#17), Llama 4 Scout: 31.8 (#208)
| Benchmark | GPT-5.5 Pro | Llama 4 Scout |
|---|---|---|
| GPQA Diamond | 93.9% | 51.8% |
| MMLU-Pro | — | 74.2% |
| Vectara Hallucination Rate | — | 7.7% |
| GPQA (HELM) | — | 50.7% |
| LMArena Expert | — | 1236 |
Multimodal Not comparable
GPT-5.5 Pro: —, Llama 4 Scout: 31.6 (#98)
| Benchmark | GPT-5.5 Pro | Llama 4 Scout |
|---|---|---|
| LMArena Vision | — | 1118 |
| SpatialViz-Bench | — | 34.2% |
Multilingual Not comparable
GPT-5.5 Pro: —, Llama 4 Scout: 41.3 (#207)
| Benchmark | GPT-5.5 Pro | Llama 4 Scout |
|---|---|---|
| LMArena Non-English | — | 1253 |
| LMArena Chinese | — | 1256 |
| LMArena French | — | 1283 |
| LMArena German | — | 1274 |
| LMArena Japanese | — | 1209 |
| LMArena Korean | — | 1207 |
| LMArena Russian | — | 1263 |
| LMArena Spanish | — | 1277 |
Instruction Following Not comparable
GPT-5.5 Pro: —, Llama 4 Scout: 66.0 (#212)
| Benchmark | GPT-5.5 Pro | Llama 4 Scout |
|---|---|---|
| IFEval | — | 81.8% |
| LMArena Instruction Following | — | 1248 |
Long Context Not comparable
GPT-5.5 Pro: —, Llama 4 Scout: 27.5 (#289)
| Benchmark | GPT-5.5 Pro | Llama 4 Scout |
|---|---|---|
| Fiction.LiveBench | — | 36% |
| LMArena Longer Query | — | 1265 |
Writing & Preference Not comparable
GPT-5.5 Pro: —, Llama 4 Scout: 37.1 (#256)
| Benchmark | GPT-5.5 Pro | Llama 4 Scout |
|---|---|---|
| LMArena Text | — | 1279 |
| LMArena Creative Writing | — | 1249 |
| EQ-Bench Creative Writing | — | 783 |
| WildBench | — | 78% |
| LMArena Multi-Turn | — | 1281 |
Frequently asked questions
Is GPT-5.5 Pro better than Llama 4 Scout?
GPT-5.5 Pro is the stronger model overall, scoring 64.3 to 27.4 on the Noometry Index. Llama 4 Scout costs 450× less per token, which makes it the better buy when GPT-5.5 Pro's lead doesn't matter for your workload.
Which is cheaper, GPT-5.5 Pro 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 Pro lists at $30 and $180.
Which has the bigger context window?
GPT-5.5 Pro does, with 1.05M tokens against 128K.
How many benchmarks do GPT-5.5 Pro and Llama 4 Scout share?
9 benchmarks have published results for both models. GPT-5.5 Pro has 14 scored results on Noometry and Llama 4 Scout has 43.