# DeepSeek-V3.1-Terminus vs Llama 4 Scout

> DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 27.7 on the Noometry Index. Llama 4 Scout costs 3.0× less per token, which makes it the better buy when DeepSeek-V3.1-Terminus's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-v3-1-terminus-vs-llama-4-scout
- Last updated: 2026-10-12
- Shared benchmarks: 15

## Summary

- They share 15 benchmarks with published results for both. DeepSeek-V3.1-Terminus scores higher in 7 categories and Llama 4 Scout in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3.1-Terminus leads 61.0 to 37.0.
- The biggest single-benchmark swing is SciCode: 40.6% for DeepSeek-V3.1-Terminus and 17% for Llama 4 Scout.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $0.27 / $1 for DeepSeek-V3.1-Terminus.
- DeepSeek-V3.1-Terminus accepts more context: 164K tokens versus 128K.

## Snapshot

| | DeepSeek-V3.1-Terminus | Llama 4 Scout |
|---|---|---|
| Provider | DeepSeek | Meta |
| Noometry Index | 43.1 | 27.7 |
| Rank | 97 | 330 |
| Context | 164K | 128K |
| Input $/M | $0.27 | $0.10 |
| Output $/M | $1 | $0.30 |
| Weights | Open | Open |

## Coding

- DeepSeek-V3.1-Terminus: 42.0 (#113)
- Llama 4 Scout: 20.2 (#339)

| Benchmark | DeepSeek-V3.1-Terminus | Llama 4 Scout |
|---|---|---|
| SciCode | 40.6% | 17% |
| LMArena Coding | 1426 | 1286 |
| SWE-bench Verified (bash only) | — | 9.1% |
| BigCodeBench Complete | — | 43.1% |
| ALE-Bench | 745.17 | — |

## Agentic & Tool Use

- DeepSeek-V3.1-Terminus: —
- Llama 4 Scout: 24.6 (#119)

| Benchmark | DeepSeek-V3.1-Terminus | Llama 4 Scout |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 28.1% |

## Reasoning

- DeepSeek-V3.1-Terminus: 26.4 (#133)
- Llama 4 Scout: 9.1 (#345)

| Benchmark | DeepSeek-V3.1-Terminus | Llama 4 Scout |
|---|---|---|
| Kagi LLM Benchmark | 57.4% | 36.9% |
| CritPt | 1.7% | 0% |
| LMArena Hard Prompts | 1426 | 1266 |
| DTBench | 81.3% | 57.9% |
| LMCA | 28.6% | 12% |
| ARC-AGI-2 | — | 0% |
| ARC-AGI-1 | — | 0.5% |
| Epoch Capabilities Index | — | 129.64 |
| ForecastBench | — | 57.5 |

## Math

- DeepSeek-V3.1-Terminus: 38.5 (#137)
- Llama 4 Scout: 19.6 (#286)

| Benchmark | DeepSeek-V3.1-Terminus | Llama 4 Scout |
|---|---|---|
| LMArena Math | 1402 | 1287 |
| OTIS Mock AIME 2024-2025 | — | 7.8% |
| Omni-MATH | — | 37.3% |
| MATH Level 5 | — | 62.3% |
| FrontierMath (Feb 2025 set) | — | 0% |

## Knowledge

- DeepSeek-V3.1-Terminus: —
- Llama 4 Scout: 31.9 (#217)

| Benchmark | DeepSeek-V3.1-Terminus | Llama 4 Scout |
|---|---|---|
| GPQA Diamond | — | 51.8% |
| MMLU-Pro | — | 74.2% |
| Vectara Hallucination Rate | — | 7.7% |
| GPQA (HELM) | — | 50.7% |
| LMArena Expert | — | 1235 |

## Multimodal

- DeepSeek-V3.1-Terminus: —
- Llama 4 Scout: 32.2 (#102)

| Benchmark | DeepSeek-V3.1-Terminus | Llama 4 Scout |
|---|---|---|
| LMArena Vision | — | 1118 |
| SpatialViz-Bench | — | 34.2% |

## Multilingual

- DeepSeek-V3.1-Terminus: 52.1 (#92)
- Llama 4 Scout: 41.0 (#212)

| Benchmark | DeepSeek-V3.1-Terminus | Llama 4 Scout |
|---|---|---|
| LMArena Non-English | 1407 | 1252 |
| LMArena Russian | 1436 | 1263 |
| LMArena Chinese | — | 1255 |
| LMArena French | — | 1282 |
| LMArena German | — | 1272 |
| LMArena Japanese | — | 1206 |
| LMArena Korean | — | 1207 |
| LMArena Spanish | — | 1278 |

## Instruction Following

- DeepSeek-V3.1-Terminus: 74.0 (#106)
- Llama 4 Scout: 65.8 (#217)

| Benchmark | DeepSeek-V3.1-Terminus | Llama 4 Scout |
|---|---|---|
| LMArena Instruction Following | 1404 | 1248 |
| IFEval | — | 81.8% |

## Long Context

- DeepSeek-V3.1-Terminus: 43.4 (#97)
- Llama 4 Scout: 27.5 (#294)

| Benchmark | DeepSeek-V3.1-Terminus | Llama 4 Scout |
|---|---|---|
| LMArena Longer Query | 1421 | 1265 |
| Fiction.LiveBench | — | 36% |

## Writing & Preference

- DeepSeek-V3.1-Terminus: 61.0 (#92)
- Llama 4 Scout: 37.0 (#261)

| Benchmark | DeepSeek-V3.1-Terminus | Llama 4 Scout |
|---|---|---|
| LMArena Text | 1419 | 1279 |
| LMArena Creative Writing | 1403 | 1249 |
| LMArena Multi-Turn | 1411 | 1280 |
| EQ-Bench Creative Writing | — | 783 |
| WildBench | — | 78% |

## FAQ

### Is DeepSeek-V3.1-Terminus better than Llama 4 Scout?

DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 27.7 on the Noometry Index. Llama 4 Scout costs 3.0× less per token, which makes it the better buy when DeepSeek-V3.1-Terminus's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek-V3.1-Terminus 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-V3.1-Terminus lists at $0.27 and $1.

### Is DeepSeek-V3.1-Terminus or Llama 4 Scout better for coding?

DeepSeek-V3.1-Terminus scores higher on coding benchmarks: 42.0 versus 20.2 in the Noometry coding category.

### Which has the bigger context window?

DeepSeek-V3.1-Terminus does, with 164K tokens against 128K.

### How many benchmarks do DeepSeek-V3.1-Terminus and Llama 4 Scout share?

15 benchmarks have published results for both models. DeepSeek-V3.1-Terminus has 16 scored results on Noometry and Llama 4 Scout has 43.