# GLM-5.3-Flash vs Llama 4 Scout

> GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 27.4 on the Noometry Index. Llama 4 Scout costs 1.6× less per token, which makes it the better buy when GLM-5.3-Flash's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/glm-5-3-flash-vs-llama-4-scout
- Last updated: 2026-10-10
- Shared benchmarks: 23

## Summary

- They share 23 benchmarks with published results for both. GLM-5.3-Flash scores higher in 10 categories and Llama 4 Scout in 0 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.3-Flash leads 53.1 to 18.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 93.9% for GLM-5.3-Flash and 7.8% for Llama 4 Scout.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $0.15 / $0.50 for GLM-5.3-Flash.
- GLM-5.3-Flash accepts more context: 1M tokens versus 128K.

## Snapshot

| | GLM-5.3-Flash | Llama 4 Scout |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 49.6 | 27.4 |
| Rank | 45 | 323 |
| Context | 1M | 128K |
| Input $/M | $0.15 | $0.10 |
| Output $/M | $0.50 | $0.30 |
| Weights | Open | Open |

## Coding

- GLM-5.3-Flash: 53.7 (#26)
- Llama 4 Scout: 19.9 (#334)

| Benchmark | GLM-5.3-Flash | Llama 4 Scout |
|---|---|---|
| SciCode | 51.6% | 17% |
| LMArena Coding | 1506 | 1286 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| SWE-bench Verified (bash only) | — | 9.1% |
| LMArena WebDev | 1614 | — |
| BigCodeBench Complete | — | 43.1% |
| ALE-Bench | 303.55 | — |

## Agentic & Tool Use

- GLM-5.3-Flash: 34.3 (#41)
- Llama 4 Scout: 24.4 (#109)

| Benchmark | GLM-5.3-Flash | Llama 4 Scout |
|---|---|---|
| APEX-Agents | 52.8% | — |
| Berkeley Function Calling Leaderboard | — | 28.1% |
| GDP.pdf | 14% | — |

## Reasoning

- GLM-5.3-Flash: 31.7 (#69)
- Llama 4 Scout: 9.2 (#339)

| Benchmark | GLM-5.3-Flash | Llama 4 Scout |
|---|---|---|
| CritPt | 15.4% | 0% |
| LMArena Hard Prompts | 1490 | 1266 |
| Epoch Capabilities Index | 151.9 | 129.64 |
| ARC-AGI-2 | — | 0% |
| Kagi LLM Benchmark | — | 36.9% |
| ARC-AGI-1 | — | 0.5% |
| Chess Puzzles | 14% | — |
| Mystery Game Puzzles | 8% | — |
| DTBench | — | 57.9% |
| LMCA | — | 12% |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |
| ForecastBench | — | 57.5 |

## Math

- GLM-5.3-Flash: 53.1 (#38)
- Llama 4 Scout: 18.4 (#280)

| Benchmark | GLM-5.3-Flash | Llama 4 Scout |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 93.9% | 7.8% |
| LMArena Math | 1499 | 1286 |
| FrontierMath (Tiers 1-3) | 55.8% | — |
| FrontierMath Tier 4 | 17.1% | — |
| ProofBench | 21% | — |
| Omni-MATH | — | 37.3% |
| MATH Level 5 | — | 62.3% |
| FrontierMath (Feb 2025 set) | — | 0% |

## Knowledge

- GLM-5.3-Flash: 58.4 (#33)
- Llama 4 Scout: 31.8 (#208)

| Benchmark | GLM-5.3-Flash | Llama 4 Scout |
|---|---|---|
| GPQA Diamond | 90.2% | 51.8% |
| LMArena Expert | 1512 | 1236 |
| MMLU-Pro | — | 74.2% |
| Vectara Hallucination Rate | — | 7.7% |
| GPQA (HELM) | — | 50.7% |

## Multimodal

- GLM-5.3-Flash: 42.6 (#25)
- Llama 4 Scout: 31.6 (#98)

| Benchmark | GLM-5.3-Flash | Llama 4 Scout |
|---|---|---|
| LMArena Vision | 1304 | 1118 |
| SpatialViz-Bench | — | 34.2% |

## Multilingual

- GLM-5.3-Flash: 56.2 (#24)
- Llama 4 Scout: 41.3 (#207)

| Benchmark | GLM-5.3-Flash | Llama 4 Scout |
|---|---|---|
| LMArena Non-English | 1462 | 1253 |
| LMArena Chinese | 1529 | 1256 |
| LMArena French | 1495 | 1283 |
| LMArena German | 1457 | 1274 |
| LMArena Japanese | 1420 | 1209 |
| LMArena Korean | 1443 | 1207 |
| LMArena Russian | 1472 | 1263 |
| LMArena Spanish | 1471 | 1277 |

## Instruction Following

- GLM-5.3-Flash: 77.8 (#17)
- Llama 4 Scout: 66.0 (#212)

| Benchmark | GLM-5.3-Flash | Llama 4 Scout |
|---|---|---|
| LMArena Instruction Following | 1480 | 1248 |
| IFEval | — | 81.8% |

## Long Context

- GLM-5.3-Flash: 45.3 (#41)
- Llama 4 Scout: 27.5 (#289)

| Benchmark | GLM-5.3-Flash | Llama 4 Scout |
|---|---|---|
| LMArena Longer Query | 1479 | 1265 |
| Fiction.LiveBench | — | 36% |

## Writing & Preference

- GLM-5.3-Flash: 65.4 (#49)
- Llama 4 Scout: 37.1 (#256)

| Benchmark | GLM-5.3-Flash | Llama 4 Scout |
|---|---|---|
| LMArena Text | 1471 | 1279 |
| LMArena Creative Writing | 1440 | 1249 |
| LMArena Multi-Turn | 1471 | 1281 |
| EQ-Bench Creative Writing | — | 783 |
| WildBench | — | 78% |

## FAQ

### Is GLM-5.3-Flash better than Llama 4 Scout?

GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 27.4 on the Noometry Index. Llama 4 Scout costs 1.6× less per token, which makes it the better buy when GLM-5.3-Flash's lead doesn't matter for your workload.

### Which is cheaper, GLM-5.3-Flash 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; GLM-5.3-Flash lists at $0.15 and $0.50.

### Is GLM-5.3-Flash or Llama 4 Scout better for coding?

GLM-5.3-Flash scores higher on coding benchmarks: 53.7 versus 19.9 in the Noometry coding category.

### Which has the bigger context window?

GLM-5.3-Flash does, with 1M tokens against 128K.

### How many benchmarks do GLM-5.3-Flash and Llama 4 Scout share?

23 benchmarks have published results for both models. GLM-5.3-Flash has 36 scored results on Noometry and Llama 4 Scout has 43.