# GLM-4.6V vs Llama 3-8B

> GLM-4.6V is the stronger model overall, scoring 40.9 to 25.3 on the Noometry Index. Llama 3-8B costs 3.2× less per token, which makes it the better buy when GLM-4.6V's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/glm-4-6v-vs-llama-3-8b
- Last updated: 2026-10-10
- Shared benchmarks: 11

## Summary

- They share 11 benchmarks with published results for both. GLM-4.6V scores higher in 7 categories and Llama 3-8B in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-4.6V leads 37.4 to 7.8.
- Llama 3-8B is cheaper at $0.14 / $0.14 per million input/output tokens, against $0.30 / $0.90 for GLM-4.6V.
- GLM-4.6V accepts more context: 128K tokens versus 8K.

## Snapshot

| | GLM-4.6V | Llama 3-8B |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 40.9 | 25.3 |
| Rank | 133 | 338 |
| Context | 128K | 8K |
| Input $/M | $0.30 | $0.14 |
| Output $/M | $0.90 | $0.14 |
| Weights | Open | Open |

## Coding

- GLM-4.6V: 40.2 (#128)
- Llama 3-8B: 30.5 (#285)

| Benchmark | GLM-4.6V | Llama 3-8B |
|---|---|---|
| LMArena Coding | 1390 | 1152 |
| BigCodeBench Instruct | — | 31.9% |
| BigCodeBench Complete | — | 36.9% |
| HumanEval+ | — | 56.7% |
| MBPP+ | — | 54.8% |

## Reasoning

- GLM-4.6V: 26.5 (#117)
- Llama 3-8B: 13.8 (#321)

| Benchmark | GLM-4.6V | Llama 3-8B |
|---|---|---|
| LMArena Hard Prompts | 1368 | 1133 |
| Chess Puzzles | — | 0% |
| DTBench | — | 43.9% |
| Adversarial NLI | — | 57.3% |
| Epoch Capabilities Index | — | 116.33 |
| ForecastBench | — | 58.6 |
| WinoGrande | — | 75.7% |

## Math

- GLM-4.6V: —
- Llama 3-8B: 8.3 (#317)

| Benchmark | GLM-4.6V | Llama 3-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 1.9% |
| LMArena Math | — | 1151 |
| MATH Level 5 | — | 6.1% |

## Knowledge

- GLM-4.6V: 37.4 (#148)
- Llama 3-8B: 7.8 (#303)

| Benchmark | GLM-4.6V | Llama 3-8B |
|---|---|---|
| LMArena Expert | 1372 | 1113 |
| GPQA Diamond | — | 26.1% |
| ARC (AI2) Challenge | — | 82.8% |
| MMLU | — | 68.8% |
| OpenBookQA | — | 82.6% |
| TriviaQA | — | 67.7% |

## Multimodal

- GLM-4.6V: 34.2 (#83)
- Llama 3-8B: —

| Benchmark | GLM-4.6V | Llama 3-8B |
|---|---|---|
| LMArena Vision | 1164 | — |

## Multilingual

- GLM-4.6V: 48.8 (#135)
- Llama 3-8B: 31.0 (#256)

| Benchmark | GLM-4.6V | Llama 3-8B |
|---|---|---|
| LMArena Non-English | 1359 | 1098 |
| LMArena Chinese | 1426 | 1076 |
| LMArena Russian | 1341 | 1109 |
| LMArena French | — | 1158 |
| LMArena German | — | 1104 |
| LMArena Japanese | — | 967 |
| LMArena Korean | — | 1003 |
| LMArena Spanish | — | 1172 |

## Instruction Following

- GLM-4.6V: 71.6 (#145)
- Llama 3-8B: 58.7 (#255)

| Benchmark | GLM-4.6V | Llama 3-8B |
|---|---|---|
| LMArena Instruction Following | 1351 | 1127 |

## Long Context

- GLM-4.6V: 41.3 (#138)
- Llama 3-8B: 34.2 (#246)

| Benchmark | GLM-4.6V | Llama 3-8B |
|---|---|---|
| LMArena Longer Query | 1358 | 1128 |

## Writing & Preference

- GLM-4.6V: 56.8 (#132)
- Llama 3-8B: 37.7 (#251)

| Benchmark | GLM-4.6V | Llama 3-8B |
|---|---|---|
| LMArena Text | 1377 | 1166 |
| LMArena Creative Writing | 1347 | 1150 |
| LMArena Multi-Turn | 1359 | 1152 |

## FAQ

### Is GLM-4.6V better than Llama 3-8B?

GLM-4.6V is the stronger model overall, scoring 40.9 to 25.3 on the Noometry Index. Llama 3-8B costs 3.2× less per token, which makes it the better buy when GLM-4.6V's lead doesn't matter for your workload.

### Which is cheaper, GLM-4.6V or Llama 3-8B?

Llama 3-8B is cheaper. It lists at $0.14 per million input tokens and $0.14 per million output tokens; GLM-4.6V lists at $0.30 and $0.90.

### Is GLM-4.6V or Llama 3-8B better for coding?

GLM-4.6V scores higher on coding benchmarks: 40.2 versus 30.5 in the Noometry coding category.

### Which has the bigger context window?

GLM-4.6V does, with 128K tokens against 8K.

### How many benchmarks do GLM-4.6V and Llama 3-8B share?

11 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and Llama 3-8B has 34.