# GPT-5.2 Pro vs Llama 4 Scout

> GPT-5.2 Pro is the stronger model overall, scoring 52.2 to 27.4 on the Noometry Index. Llama 4 Scout costs 385× less per token, which makes it the better buy when GPT-5.2 Pro's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/gpt-5-2-pro-vs-llama-4-scout
- Last updated: 2026-10-10
- Shared benchmarks: 3

## Summary

- They share 3 benchmarks with published results for both. GPT-5.2 Pro scores higher in 2 categories and Llama 4 Scout in 0 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.2 Pro leads 64.8 to 18.4.
- The biggest single-benchmark swing is ARC-AGI-1: 90.5% for GPT-5.2 Pro and 0.5% for Llama 4 Scout.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $21 / $168 for GPT-5.2 Pro.
- GPT-5.2 Pro accepts more context: 400K tokens versus 128K.
- Llama 4 Scout has downloadable open weights; the other is API-only.

## Snapshot

| | GPT-5.2 Pro | Llama 4 Scout |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 52.2 | 27.4 |
| Rank | 34 | 323 |
| Context | 400K | 128K |
| Input $/M | $21 | $0.10 |
| Output $/M | $168 | $0.30 |
| Weights | Proprietary | Open |

## Coding

- GPT-5.2 Pro: —
- Llama 4 Scout: 19.9 (#334)

| Benchmark | GPT-5.2 Pro | Llama 4 Scout |
|---|---|---|
| SWE-bench Verified (bash only) | — | 9.1% |
| SciCode | — | 17% |
| LMArena Coding | — | 1286 |
| BigCodeBench Complete | — | 43.1% |

## Agentic & Tool Use

- GPT-5.2 Pro: —
- Llama 4 Scout: 24.4 (#109)

| Benchmark | GPT-5.2 Pro | Llama 4 Scout |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 28.1% |

## Reasoning

- GPT-5.2 Pro: 51.8 (#31)
- Llama 4 Scout: 9.2 (#339)

| Benchmark | GPT-5.2 Pro | Llama 4 Scout |
|---|---|---|
| ARC-AGI-2 | 54.2% | 0% |
| ARC-AGI-1 | 90.5% | 0.5% |
| Epoch Capabilities Index | 155.41 | 129.64 |
| SimpleBench | 57.4% | — |
| Kagi LLM Benchmark | — | 36.9% |
| NYT Connections (extended) | 79.3% | — |
| CritPt | — | 0% |
| LMArena Hard Prompts | — | 1266 |
| DTBench | — | 57.9% |
| LMCA | — | 12% |
| ForecastBench | — | 57.5 |

## Math

- GPT-5.2 Pro: 64.8 (#23)
- Llama 4 Scout: 18.4 (#280)

| Benchmark | GPT-5.2 Pro | Llama 4 Scout |
|---|---|---|
| FrontierMath (Tiers 1-3) | 74% | — |
| FrontierMath Tier 4 | 46% | — |
| OTIS Mock AIME 2024-2025 | — | 7.8% |
| Omni-MATH | — | 37.3% |
| LMArena Math | — | 1286 |
| MATH Level 5 | — | 62.3% |
| FrontierMath (Feb 2025 set) | — | 0% |
| FrontierMath Tier 4 (v1) | 31.3% | — |

## Knowledge

- GPT-5.2 Pro: —
- Llama 4 Scout: 31.8 (#208)

| Benchmark | GPT-5.2 Pro | Llama 4 Scout |
|---|---|---|
| GPQA Diamond | — | 51.8% |
| MMLU-Pro | — | 74.2% |
| Vectara Hallucination Rate | — | 7.7% |
| GPQA (HELM) | — | 50.7% |
| LMArena Expert | — | 1236 |

## Multimodal

- GPT-5.2 Pro: —
- Llama 4 Scout: 31.6 (#98)

| Benchmark | GPT-5.2 Pro | Llama 4 Scout |
|---|---|---|
| LMArena Vision | — | 1118 |
| SpatialViz-Bench | — | 34.2% |

## Multilingual

- GPT-5.2 Pro: —
- Llama 4 Scout: 41.3 (#207)

| Benchmark | GPT-5.2 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

- GPT-5.2 Pro: —
- Llama 4 Scout: 66.0 (#212)

| Benchmark | GPT-5.2 Pro | Llama 4 Scout |
|---|---|---|
| IFEval | — | 81.8% |
| LMArena Instruction Following | — | 1248 |

## Long Context

- GPT-5.2 Pro: —
- Llama 4 Scout: 27.5 (#289)

| Benchmark | GPT-5.2 Pro | Llama 4 Scout |
|---|---|---|
| Fiction.LiveBench | — | 36% |
| LMArena Longer Query | — | 1265 |

## Writing & Preference

- GPT-5.2 Pro: —
- Llama 4 Scout: 37.1 (#256)

| Benchmark | GPT-5.2 Pro | Llama 4 Scout |
|---|---|---|
| LMArena Text | — | 1279 |
| LMArena Creative Writing | — | 1249 |
| EQ-Bench Creative Writing | — | 783 |
| WildBench | — | 78% |
| LMArena Multi-Turn | — | 1281 |

## FAQ

### Is GPT-5.2 Pro better than Llama 4 Scout?

GPT-5.2 Pro is the stronger model overall, scoring 52.2 to 27.4 on the Noometry Index. Llama 4 Scout costs 385× less per token, which makes it the better buy when GPT-5.2 Pro's lead doesn't matter for your workload.

### Which is cheaper, GPT-5.2 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-5.2 Pro lists at $21 and $168.

### Which has the bigger context window?

GPT-5.2 Pro does, with 400K tokens against 128K.

### How many benchmarks do GPT-5.2 Pro and Llama 4 Scout share?

3 benchmarks have published results for both models. GPT-5.2 Pro has 8 scored results on Noometry and Llama 4 Scout has 43.