# GLM-4.5V vs Llama 4 Maverick

> GLM-4.5V is the stronger model overall, scoring 39.2 to 30.6 on the Noometry Index. Llama 4 Maverick costs 3.0× less per token, which makes it the better buy when GLM-4.5V's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/glm-4-5v-vs-llama-4-maverick
- Last updated: 2026-10-10
- Shared benchmarks: 15

## Summary

- They share 15 benchmarks with published results for both. GLM-4.5V scores higher in 8 categories and Llama 4 Maverick in 1 category; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-4.5V leads 26.5 to 10.1.
- Llama 4 Maverick is cheaper at $0.19 / $0.65 per million input/output tokens, against $0.60 / $1.80 for GLM-4.5V.
- Llama 4 Maverick accepts more context: 128K tokens versus 64K.

## Snapshot

| | GLM-4.5V | Llama 4 Maverick |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 39.2 | 30.6 |
| Rank | 158 | 276 |
| Context | 64K | 128K |
| Input $/M | $0.60 | $0.19 |
| Output $/M | $1.80 | $0.65 |
| Weights | Open | Open |

## Coding

- GLM-4.5V: 38.9 (#152)
- Llama 4 Maverick: 26.3 (#318)

| Benchmark | GLM-4.5V | Llama 4 Maverick |
|---|---|---|
| LMArena Coding | 1347 | 1302 |
| SWE-bench Verified (bash only) | — | 21% |
| Aider Polyglot | — | 15.6% |
| SciCode | — | 33.1% |
| WeirdML | — | 24.5% |
| BigCodeBench Instruct | — | 49.7% |
| BigCodeBench Complete | — | 61.4% |
| ALE-Bench | — | 172.97 |

## Agentic & Tool Use

- GLM-4.5V: —
- Llama 4 Maverick: 27.9 (#86)

| Benchmark | GLM-4.5V | Llama 4 Maverick |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 37.3% |

## Reasoning

- GLM-4.5V: 26.5 (#118)
- Llama 4 Maverick: 10.1 (#337)

| Benchmark | GLM-4.5V | Llama 4 Maverick |
|---|---|---|
| Kagi LLM Benchmark | 59.8% | 55.9% |
| LMArena Hard Prompts | 1335 | 1281 |
| ARC-AGI-2 | — | 0% |
| SimpleBench | — | 27.7% |
| NYT Connections (extended) | — | 8% |
| ARC-AGI-1 | — | 4.4% |
| CritPt | — | 0% |
| EnigmaEval | — | 0.6% |
| DTBench | — | 61.9% |
| LMCA | — | 15.9% |
| Epoch Capabilities Index | — | 132.2 |
| ForecastBench | — | 57.5 |

## Math

- GLM-4.5V: 34.7 (#157)
- Llama 4 Maverick: 24.6 (#256)

| Benchmark | GLM-4.5V | Llama 4 Maverick |
|---|---|---|
| LMArena Math | 1353 | 1299 |
| OTIS Mock AIME 2024-2025 | — | 20.6% |
| Omni-MATH | — | 42.2% |
| MATH Level 5 | — | 73% |
| FrontierMath (Feb 2025 set) | — | 0.7% |

## Knowledge

- GLM-4.5V: 36.8 (#155)
- Llama 4 Maverick: 33.3 (#196)

| Benchmark | GLM-4.5V | Llama 4 Maverick |
|---|---|---|
| LMArena Expert | 1353 | 1259 |
| GPQA Diamond | — | 67% |
| Humanity's Last Exam | — | 5.7% |
| MMLU-Pro | — | 81% |
| Confabulations | — | 22.6% |
| Vectara Hallucination Rate | — | 8.2% |
| GPQA (HELM) | — | 65% |

## Multimodal

- GLM-4.5V: 33.6 (#86)
- Llama 4 Maverick: 30.9 (#100)

| Benchmark | GLM-4.5V | Llama 4 Maverick |
|---|---|---|
| LMArena Vision | 1154 | 1141 |
| GeoBench | — | 52% |
| SpatialViz-Bench | — | 31.8% |

## Multilingual

- GLM-4.5V: 44.9 (#172)
- Llama 4 Maverick: 42.4 (#190)

| Benchmark | GLM-4.5V | Llama 4 Maverick |
|---|---|---|
| LMArena Non-English | 1304 | 1269 |
| LMArena Chinese | 1338 | 1277 |
| LMArena Russian | 1299 | 1286 |
| LMArena Spanish | 1335 | 1293 |
| LMArena French | — | 1258 |
| LMArena German | — | 1291 |
| LMArena Japanese | — | 1207 |
| LMArena Korean | — | 1203 |

## Instruction Following

- GLM-4.5V: 69.4 (#170)
- Llama 4 Maverick: 71.9 (#140)

| Benchmark | GLM-4.5V | Llama 4 Maverick |
|---|---|---|
| LMArena Instruction Following | 1311 | 1267 |
| IFEval | — | 90.8% |

## Long Context

- GLM-4.5V: 39.6 (#166)
- Llama 4 Maverick: 31.4 (#274)

| Benchmark | GLM-4.5V | Llama 4 Maverick |
|---|---|---|
| LMArena Longer Query | 1305 | 1280 |
| Fiction.LiveBench | — | 46.2% |

## Writing & Preference

- GLM-4.5V: 52.7 (#162)
- Llama 4 Maverick: 39.0 (#247)

| Benchmark | GLM-4.5V | Llama 4 Maverick |
|---|---|---|
| LMArena Text | 1333 | 1287 |
| LMArena Creative Writing | 1295 | 1266 |
| LMArena Multi-Turn | 1333 | 1289 |
| Short-Story Creative Writing | — | 62% |
| EQ-Bench Creative Writing | — | 860 |
| WildBench | — | 80% |

## FAQ

### Is GLM-4.5V better than Llama 4 Maverick?

GLM-4.5V is the stronger model overall, scoring 39.2 to 30.6 on the Noometry Index. Llama 4 Maverick costs 3.0× less per token, which makes it the better buy when GLM-4.5V's lead doesn't matter for your workload.

### Which is cheaper, GLM-4.5V or Llama 4 Maverick?

Llama 4 Maverick is cheaper. It lists at $0.19 per million input tokens and $0.65 per million output tokens; GLM-4.5V lists at $0.60 and $1.80.

### Is GLM-4.5V or Llama 4 Maverick better for coding?

GLM-4.5V scores higher on coding benchmarks: 38.9 versus 26.3 in the Noometry coding category.

### Which has the bigger context window?

Llama 4 Maverick does, with 128K tokens against 64K.

### How many benchmarks do GLM-4.5V and Llama 4 Maverick share?

15 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and Llama 4 Maverick has 54.