# DeepSeek-V3.2-Exp vs Llama 4 Scout

> DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.9 to 27.4 on the Noometry Index. Llama 4 Scout costs 1.9× less per token, which makes it the better buy when DeepSeek-V3.2-Exp's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-v3-2-exp-vs-llama-4-scout
- Last updated: 2026-10-10
- Shared benchmarks: 33

## Summary

- They share 33 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 9 categories and Llama 4 Scout in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in coding, where DeepSeek-V3.2-Exp leads 46.4 to 19.9.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 87.8% for DeepSeek-V3.2-Exp and 7.8% for Llama 4 Scout.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $0.26 / $0.38 for DeepSeek-V3.2-Exp.
- DeepSeek-V3.2-Exp accepts more context: 164K tokens versus 128K.

## Snapshot

| | DeepSeek-V3.2-Exp | Llama 4 Scout |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 44.9 | 27.4 |
| Rank | 68 | 323 |
| Context | 164K | 128K |
| Input $/M | $0.26 | $0.10 |
| Output $/M | $0.38 | $0.30 |
| Weights | Open | Open |

## Coding

- DeepSeek-V3.2-Exp: 46.4 (#61)
- Llama 4 Scout: 19.9 (#334)

| Benchmark | DeepSeek-V3.2-Exp | Llama 4 Scout |
|---|---|---|
| SWE-bench Verified (bash only) | 70% | 9.1% |
| SciCode | 38.9% | 17% |
| LMArena Coding | 1454 | 1286 |
| Aider Polyglot | 74.2% | — |
| LMArena WebDev | 1362 | — |
| SWE-bench Multilingual | 59% | — |
| WeirdML | 39.5% | — |
| BigCodeBench Complete | — | 43.1% |

## Agentic & Tool Use

- DeepSeek-V3.2-Exp: 37.9 (#31)
- Llama 4 Scout: 24.4 (#109)

| Benchmark | DeepSeek-V3.2-Exp | Llama 4 Scout |
|---|---|---|
| Berkeley Function Calling Leaderboard | 56.7% | 28.1% |
| Terminal-Bench | 39.6% | — |
| TheAgentCompany | 42.9% | — |
| Vending-Bench 2 | 1,034 | — |

## Reasoning

- DeepSeek-V3.2-Exp: 22.0 (#185)
- Llama 4 Scout: 9.2 (#339)

| Benchmark | DeepSeek-V3.2-Exp | Llama 4 Scout |
|---|---|---|
| ARC-AGI-2 | 4% | 0% |
| Kagi LLM Benchmark | 52.2% | 36.9% |
| ARC-AGI-1 | 57% | 0.5% |
| CritPt | 2.9% | 0% |
| LMArena Hard Prompts | 1433 | 1266 |
| DTBench | 87.7% | 57.9% |
| LMCA | 29.1% | 12% |
| Epoch Capabilities Index | 146.28 | 129.64 |
| NYT Connections (extended) | 36.7% | — |
| Chess Puzzles | 14% | — |
| Thematic Generalization | 65% | — |
| ForecastBench | — | 57.5 |

## Math

- DeepSeek-V3.2-Exp: 40.8 (#75)
- Llama 4 Scout: 18.4 (#280)

| Benchmark | DeepSeek-V3.2-Exp | Llama 4 Scout |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 87.8% | 7.8% |
| LMArena Math | 1435 | 1286 |
| FrontierMath (Feb 2025 set) | 22.1% | 0% |
| MathArena Final-Answer Competitions | 57.7% | — |
| ProofBench | 8% | — |
| Omni-MATH | — | 37.3% |
| MATH Level 5 | — | 62.3% |
| FrontierMath Tier 4 (v1) | 2.1% | — |

## Knowledge

- DeepSeek-V3.2-Exp: 51.6 (#61)
- Llama 4 Scout: 31.8 (#208)

| Benchmark | DeepSeek-V3.2-Exp | Llama 4 Scout |
|---|---|---|
| GPQA Diamond | 83.4% | 51.8% |
| Vectara Hallucination Rate | 5.3% | 7.7% |
| LMArena Expert | 1436 | 1236 |
| MMLU-Pro | — | 74.2% |
| GPQA (HELM) | — | 50.7% |

## Multimodal

- DeepSeek-V3.2-Exp: —
- Llama 4 Scout: 31.6 (#98)

| Benchmark | DeepSeek-V3.2-Exp | Llama 4 Scout |
|---|---|---|
| LMArena Vision | — | 1118 |
| SpatialViz-Bench | — | 34.2% |

## Multilingual

- DeepSeek-V3.2-Exp: 52.4 (#85)
- Llama 4 Scout: 41.3 (#207)

| Benchmark | DeepSeek-V3.2-Exp | Llama 4 Scout |
|---|---|---|
| LMArena Non-English | 1409 | 1253 |
| LMArena Chinese | 1461 | 1256 |
| LMArena French | 1435 | 1283 |
| LMArena German | 1440 | 1274 |
| LMArena Japanese | 1377 | 1209 |
| LMArena Korean | 1372 | 1207 |
| LMArena Russian | 1423 | 1263 |
| LMArena Spanish | 1441 | 1277 |

## Instruction Following

- DeepSeek-V3.2-Exp: 74.7 (#89)
- Llama 4 Scout: 66.0 (#212)

| Benchmark | DeepSeek-V3.2-Exp | Llama 4 Scout |
|---|---|---|
| LMArena Instruction Following | 1413 | 1248 |
| IFEval | — | 81.8% |

## Long Context

- DeepSeek-V3.2-Exp: 47.6 (#15)
- Llama 4 Scout: 27.5 (#289)

| Benchmark | DeepSeek-V3.2-Exp | Llama 4 Scout |
|---|---|---|
| Fiction.LiveBench | 83.3% | 36% |
| LMArena Longer Query | 1428 | 1265 |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |

## Writing & Preference

- DeepSeek-V3.2-Exp: 62.5 (#73)
- Llama 4 Scout: 37.1 (#256)

| Benchmark | DeepSeek-V3.2-Exp | Llama 4 Scout |
|---|---|---|
| LMArena Text | 1425 | 1279 |
| LMArena Creative Writing | 1403 | 1249 |
| EQ-Bench Creative Writing | 1515 | 783 |
| LMArena Multi-Turn | 1427 | 1281 |
| WildBench | — | 78% |

## FAQ

### Is DeepSeek-V3.2-Exp better than Llama 4 Scout?

DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.9 to 27.4 on the Noometry Index. Llama 4 Scout costs 1.9× less per token, which makes it the better buy when DeepSeek-V3.2-Exp's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek-V3.2-Exp 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; DeepSeek-V3.2-Exp lists at $0.26 and $0.38.

### Is DeepSeek-V3.2-Exp or Llama 4 Scout better for coding?

DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.4 versus 19.9 in the Noometry coding category.

### Which has the bigger context window?

DeepSeek-V3.2-Exp does, with 164K tokens against 128K.

### How many benchmarks do DeepSeek-V3.2-Exp and Llama 4 Scout share?

33 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 48 scored results on Noometry and Llama 4 Scout has 43.