# Llama 4 Scout vs Qwen2.5 7B Instruct

> Qwen2.5 7B Instruct is the stronger model overall, scoring 29.0 to 27.7 on the Noometry Index. Llama 4 Scout costs 2.0× less per token, which makes it the better buy when Qwen2.5 7B Instruct's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/llama-4-scout-vs-qwen2-5-7b-instruct
- Last updated: 2026-10-12
- Shared benchmarks: 11

## Summary

- They share 11 benchmarks with published results for both. Llama 4 Scout scores higher in 4 categories and Qwen2.5 7B Instruct in 3 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in coding, where Qwen2.5 7B Instruct leads 36.5 to 20.2.
- The biggest single-benchmark swing is MMLU-Pro: 74.2% for Llama 4 Scout and 53.9% for Qwen2.5 7B Instruct.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $0.17 / $0.70 for Qwen2.5 7B Instruct.
- Qwen2.5 7B Instruct accepts more context: 131K tokens versus 128K.

## Snapshot

| | Llama 4 Scout | Qwen2.5 7B Instruct |
|---|---|---|
| Provider | Meta | Alibaba (Qwen) |
| Noometry Index | 27.7 | 29.0 |
| Rank | 330 | 320 |
| Context | 128K | 131K |
| Input $/M | $0.10 | $0.17 |
| Output $/M | $0.30 | $0.70 |
| Weights | Open | Open |

## Coding

- Llama 4 Scout: 20.2 (#339)
- Qwen2.5 7B Instruct: 36.5 (#208)

| Benchmark | Llama 4 Scout | Qwen2.5 7B Instruct |
|---|---|---|
| BigCodeBench Complete | 43.1% | 46.1% |
| SWE-bench Verified (bash only) | 9.1% | — |
| SciCode | 17% | — |
| BigCodeBench Instruct | — | 37.6% |
| LMArena Coding | 1286 | — |

## Agentic & Tool Use

- Llama 4 Scout: 24.6 (#119)
- Qwen2.5 7B Instruct: 23.8 (#124)

| Benchmark | Llama 4 Scout | Qwen2.5 7B Instruct |
|---|---|---|
| Berkeley Function Calling Leaderboard | 28.1% | — |
| BALROG | — | 7.8% |

## Reasoning

- Llama 4 Scout: 9.1 (#345)
- Qwen2.5 7B Instruct: 14.8 (#322)

| Benchmark | Llama 4 Scout | Qwen2.5 7B Instruct |
|---|---|---|
| DTBench | 57.9% | 47.7% |
| LMCA | 12% | 6.4% |
| Epoch Capabilities Index | 129.64 | 118.51 |
| ARC-AGI-2 | 0% | — |
| Kagi LLM Benchmark | 36.9% | — |
| ARC-AGI-1 | 0.5% | — |
| CritPt | 0% | — |
| Chess Puzzles | — | 0% |
| LMArena Hard Prompts | 1266 | — |
| ForecastBench | 57.5 | — |

## Math

- Llama 4 Scout: 19.6 (#286)
- Qwen2.5 7B Instruct: 12.6 (#306)

| Benchmark | Llama 4 Scout | Qwen2.5 7B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 7.8% | 2.5% |
| Omni-MATH | 37.3% | 29.4% |
| LMArena Math | 1287 | — |
| MATH Level 5 | 62.3% | — |
| FrontierMath (Feb 2025 set) | 0% | — |

## Knowledge

- Llama 4 Scout: 31.9 (#217)
- Qwen2.5 7B Instruct: 17.0 (#286)

| Benchmark | Llama 4 Scout | Qwen2.5 7B Instruct |
|---|---|---|
| GPQA Diamond | 51.8% | 35.5% |
| MMLU-Pro | 74.2% | 53.9% |
| GPQA (HELM) | 50.7% | 34.1% |
| Vectara Hallucination Rate | 7.7% | — |
| LMArena Expert | 1235 | — |
| MMLU | — | 72.9% |

## Multimodal

- Llama 4 Scout: 32.2 (#102)
- Qwen2.5 7B Instruct: —

| Benchmark | Llama 4 Scout | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Vision | 1118 | — |
| SpatialViz-Bench | 34.2% | — |

## Multilingual

- Llama 4 Scout: 41.0 (#212)
- Qwen2.5 7B Instruct: —

| Benchmark | Llama 4 Scout | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Non-English | 1252 | — |
| LMArena Chinese | 1255 | — |
| LMArena French | 1282 | — |
| LMArena German | 1272 | — |
| LMArena Japanese | 1206 | — |
| LMArena Korean | 1207 | — |
| LMArena Russian | 1263 | — |
| LMArena Spanish | 1278 | — |

## Instruction Following

- Llama 4 Scout: 65.8 (#217)
- Qwen2.5 7B Instruct: 63.2 (#231)

| Benchmark | Llama 4 Scout | Qwen2.5 7B Instruct |
|---|---|---|
| IFEval | 81.8% | 74.1% |
| LMArena Instruction Following | 1248 | — |

## Long Context

- Llama 4 Scout: 27.5 (#294)
- Qwen2.5 7B Instruct: —

| Benchmark | Llama 4 Scout | Qwen2.5 7B Instruct |
|---|---|---|
| Fiction.LiveBench | 36% | — |
| LMArena Longer Query | 1265 | — |

## Writing & Preference

- Llama 4 Scout: 37.0 (#261)
- Qwen2.5 7B Instruct: 48.8 (#195)

| Benchmark | Llama 4 Scout | Qwen2.5 7B Instruct |
|---|---|---|
| WildBench | 78% | 73.1% |
| LMArena Text | 1279 | — |
| LMArena Creative Writing | 1249 | — |
| EQ-Bench Creative Writing | 783 | — |
| LMArena Multi-Turn | 1280 | — |

## FAQ

### Is Llama 4 Scout better than Qwen2.5 7B Instruct?

Qwen2.5 7B Instruct is the stronger model overall, scoring 29.0 to 27.7 on the Noometry Index. Llama 4 Scout costs 2.0× less per token, which makes it the better buy when Qwen2.5 7B Instruct's lead doesn't matter for your workload.

### Which is cheaper, Llama 4 Scout or Qwen2.5 7B Instruct?

Llama 4 Scout is cheaper. It lists at $0.10 per million input tokens and $0.30 per million output tokens; Qwen2.5 7B Instruct lists at $0.17 and $0.70.

### Is Llama 4 Scout or Qwen2.5 7B Instruct better for coding?

Qwen2.5 7B Instruct scores higher on coding benchmarks: 36.5 versus 20.2 in the Noometry coding category.

### Which has the bigger context window?

Qwen2.5 7B Instruct does, with 131K tokens against 128K.

### How many benchmarks do Llama 4 Scout and Qwen2.5 7B Instruct share?

11 benchmarks have published results for both models. Llama 4 Scout has 43 scored results on Noometry and Qwen2.5 7B Instruct has 15.