# GLM-5.3-Flash vs Llama 4 Maverick

> GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 30.6 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-5-3-flash-vs-llama-4-maverick
- Last updated: 2026-10-10
- Shared benchmarks: 24

## Summary

- They share 24 benchmarks with published results for both. GLM-5.3-Flash scores higher in 10 categories and Llama 4 Maverick 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 24.6.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 93.9% for GLM-5.3-Flash and 20.6% for Llama 4 Maverick.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $0.19 / $0.65 for Llama 4 Maverick.
- GLM-5.3-Flash accepts more context: 1M tokens versus 128K.

## Snapshot

| | GLM-5.3-Flash | Llama 4 Maverick |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 49.6 | 30.6 |
| Rank | 45 | 276 |
| Context | 1M | 128K |
| Input $/M | $0.15 | $0.19 |
| Output $/M | $0.50 | $0.65 |
| Weights | Open | Open |

## Coding

- GLM-5.3-Flash: 53.7 (#26)
- Llama 4 Maverick: 26.3 (#318)

| Benchmark | GLM-5.3-Flash | Llama 4 Maverick |
|---|---|---|
| SciCode | 51.6% | 33.1% |
| LMArena Coding | 1506 | 1302 |
| ALE-Bench | 303.55 | 172.97 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| SWE-bench Verified (bash only) | — | 21% |
| Aider Polyglot | — | 15.6% |
| LMArena WebDev | 1614 | — |
| WeirdML | — | 24.5% |
| BigCodeBench Instruct | — | 49.7% |
| BigCodeBench Complete | — | 61.4% |

## Agentic & Tool Use

- GLM-5.3-Flash: 34.3 (#41)
- Llama 4 Maverick: 27.9 (#86)

| Benchmark | GLM-5.3-Flash | Llama 4 Maverick |
|---|---|---|
| APEX-Agents | 52.8% | — |
| Berkeley Function Calling Leaderboard | — | 37.3% |
| GDP.pdf | 14% | — |

## Reasoning

- GLM-5.3-Flash: 31.7 (#69)
- Llama 4 Maverick: 10.1 (#337)

| Benchmark | GLM-5.3-Flash | Llama 4 Maverick |
|---|---|---|
| CritPt | 15.4% | 0% |
| LMArena Hard Prompts | 1490 | 1281 |
| Epoch Capabilities Index | 151.9 | 132.2 |
| ARC-AGI-2 | — | 0% |
| SimpleBench | — | 27.7% |
| Kagi LLM Benchmark | — | 55.9% |
| NYT Connections (extended) | — | 8% |
| ARC-AGI-1 | — | 4.4% |
| Chess Puzzles | 14% | — |
| EnigmaEval | — | 0.6% |
| Mystery Game Puzzles | 8% | — |
| DTBench | — | 61.9% |
| LMCA | — | 15.9% |
| 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 Maverick: 24.6 (#256)

| Benchmark | GLM-5.3-Flash | Llama 4 Maverick |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 93.9% | 20.6% |
| LMArena Math | 1499 | 1299 |
| FrontierMath (Tiers 1-3) | 55.8% | — |
| FrontierMath Tier 4 | 17.1% | — |
| ProofBench | 21% | — |
| Omni-MATH | — | 42.2% |
| MATH Level 5 | — | 73% |
| FrontierMath (Feb 2025 set) | — | 0.7% |

## Knowledge

- GLM-5.3-Flash: 58.4 (#33)
- Llama 4 Maverick: 33.3 (#196)

| Benchmark | GLM-5.3-Flash | Llama 4 Maverick |
|---|---|---|
| GPQA Diamond | 90.2% | 67% |
| LMArena Expert | 1512 | 1259 |
| Humanity's Last Exam | — | 5.7% |
| MMLU-Pro | — | 81% |
| Confabulations | — | 22.6% |
| Vectara Hallucination Rate | — | 8.2% |
| GPQA (HELM) | — | 65% |

## Multimodal

- GLM-5.3-Flash: 42.6 (#25)
- Llama 4 Maverick: 30.9 (#100)

| Benchmark | GLM-5.3-Flash | Llama 4 Maverick |
|---|---|---|
| LMArena Vision | 1304 | 1141 |
| GeoBench | — | 52% |
| SpatialViz-Bench | — | 31.8% |

## Multilingual

- GLM-5.3-Flash: 56.2 (#24)
- Llama 4 Maverick: 42.4 (#190)

| Benchmark | GLM-5.3-Flash | Llama 4 Maverick |
|---|---|---|
| LMArena Non-English | 1462 | 1269 |
| LMArena Chinese | 1529 | 1277 |
| LMArena French | 1495 | 1258 |
| LMArena German | 1457 | 1291 |
| LMArena Japanese | 1420 | 1207 |
| LMArena Korean | 1443 | 1203 |
| LMArena Russian | 1472 | 1286 |
| LMArena Spanish | 1471 | 1293 |

## Instruction Following

- GLM-5.3-Flash: 77.8 (#17)
- Llama 4 Maverick: 71.9 (#140)

| Benchmark | GLM-5.3-Flash | Llama 4 Maverick |
|---|---|---|
| LMArena Instruction Following | 1480 | 1267 |
| IFEval | — | 90.8% |

## Long Context

- GLM-5.3-Flash: 45.3 (#41)
- Llama 4 Maverick: 31.4 (#274)

| Benchmark | GLM-5.3-Flash | Llama 4 Maverick |
|---|---|---|
| LMArena Longer Query | 1479 | 1280 |
| Fiction.LiveBench | — | 46.2% |

## Writing & Preference

- GLM-5.3-Flash: 65.4 (#49)
- Llama 4 Maverick: 39.0 (#247)

| Benchmark | GLM-5.3-Flash | Llama 4 Maverick |
|---|---|---|
| LMArena Text | 1471 | 1287 |
| LMArena Creative Writing | 1440 | 1266 |
| LMArena Multi-Turn | 1471 | 1289 |
| Short-Story Creative Writing | — | 62% |
| EQ-Bench Creative Writing | — | 860 |
| WildBench | — | 80% |

## FAQ

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

GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 30.6 on the Noometry Index.

### Which is cheaper, GLM-5.3-Flash or Llama 4 Maverick?

GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; Llama 4 Maverick lists at $0.19 and $0.65.

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

GLM-5.3-Flash scores higher on coding benchmarks: 53.7 versus 26.3 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 Maverick share?

24 benchmarks have published results for both models. GLM-5.3-Flash has 36 scored results on Noometry and Llama 4 Maverick has 54.