# GLM-4.6V vs Llama 3.1-8B

> GLM-4.6V is the stronger model overall, scoring 41.3 to 23.0 on the Noometry Index. Llama 3.1-8B costs 7.8× 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-3-1-8b
- Last updated: 2026-10-12
- Shared benchmarks: 11

## Summary

- They share 11 benchmarks with published results for both. GLM-4.6V scores higher in 7 categories and Llama 3.1-8B in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-4.6V leads 38.0 to 8.0.
- Llama 3.1-8B is cheaper at $0.05 / $0.08 per million input/output tokens, against $0.30 / $0.90 for GLM-4.6V.

## Snapshot

| | GLM-4.6V | Llama 3.1-8B |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 41.3 | 23.0 |
| Rank | 137 | 352 |
| Context | 128K | 128K |
| Input $/M | $0.30 | $0.05 |
| Output $/M | $0.90 | $0.08 |
| Weights | Open | Open |

## Coding

- GLM-4.6V: 40.9 (#128)
- Llama 3.1-8B: 20.2 (#340)

| Benchmark | GLM-4.6V | Llama 3.1-8B |
|---|---|---|
| LMArena Coding | 1390 | 1195 |
| SciCode | — | 13.2% |
| WeirdML | — | 1.7% |
| BigCodeBench Instruct | — | 32.8% |
| BigCodeBench Complete | — | 40.5% |
| HumanEval+ | — | 62.8% |
| MBPP+ | — | 55.6% |

## Agentic & Tool Use

- GLM-4.6V: —
- Llama 3.1-8B: 22.5 (#131)

| Benchmark | GLM-4.6V | Llama 3.1-8B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 25.8% |
| BALROG | — | 15.1% |

## Reasoning

- GLM-4.6V: 27.6 (#115)
- Llama 3.1-8B: 14.9 (#321)

| Benchmark | GLM-4.6V | Llama 3.1-8B |
|---|---|---|
| LMArena Hard Prompts | 1368 | 1175 |
| CritPt | — | 0% |
| Chess Puzzles | — | 0% |
| DTBench | — | 50.9% |
| LMCA | — | 5.4% |
| Epoch Capabilities Index | — | 116.57 |
| PIQA | — | 81.2% |

## Math

- GLM-4.6V: —
- Llama 3.1-8B: 10.2 (#317)

| Benchmark | GLM-4.6V | Llama 3.1-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 1.7% |
| Omni-MATH | — | 13.7% |
| LMArena Math | — | 1179 |
| MATH Level 5 | — | 22.9% |
| GSM8K | — | 82.4% |

## Knowledge

- GLM-4.6V: 38.0 (#149)
- Llama 3.1-8B: 8.0 (#307)

| Benchmark | GLM-4.6V | Llama 3.1-8B |
|---|---|---|
| LMArena Expert | 1371 | 1144 |
| GPQA Diamond | — | 27% |
| MMLU-Pro | — | 40.6% |
| GPQA (HELM) | — | 24.7% |
| BoolQ | — | 82.8% |
| MMLU | — | 56.1% |

## Multimodal

- GLM-4.6V: 34.8 (#90)
- Llama 3.1-8B: —

| Benchmark | GLM-4.6V | Llama 3.1-8B |
|---|---|---|
| LMArena Vision | 1164 | — |

## Multilingual

- GLM-4.6V: 48.6 (#141)
- Llama 3.1-8B: 34.0 (#249)

| Benchmark | GLM-4.6V | Llama 3.1-8B |
|---|---|---|
| LMArena Non-English | 1359 | 1148 |
| LMArena Chinese | 1425 | 1151 |
| LMArena Russian | 1340 | 1158 |
| LMArena French | — | 1177 |
| LMArena German | — | 1144 |
| LMArena Japanese | — | 1061 |
| LMArena Korean | — | 1053 |
| LMArena Spanish | — | 1169 |

## Instruction Following

- GLM-4.6V: 71.4 (#151)
- Llama 3.1-8B: 58.9 (#258)

| Benchmark | GLM-4.6V | Llama 3.1-8B |
|---|---|---|
| LMArena Instruction Following | 1352 | 1159 |
| IFEval | — | 74.3% |

## Long Context

- GLM-4.6V: 41.3 (#143)
- Llama 3.1-8B: 35.8 (#238)

| Benchmark | GLM-4.6V | Llama 3.1-8B |
|---|---|---|
| LMArena Longer Query | 1358 | 1182 |

## Writing & Preference

- GLM-4.6V: 56.6 (#137)
- Llama 3.1-8B: 29.7 (#290)

| Benchmark | GLM-4.6V | Llama 3.1-8B |
|---|---|---|
| LMArena Text | 1377 | 1187 |
| LMArena Creative Writing | 1347 | 1154 |
| LMArena Multi-Turn | 1360 | 1172 |
| EQ-Bench Creative Writing | — | 713 |
| WildBench | — | 68.7% |

## FAQ

### Is GLM-4.6V better than Llama 3.1-8B?

GLM-4.6V is the stronger model overall, scoring 41.3 to 23.0 on the Noometry Index. Llama 3.1-8B costs 7.8× 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 3.1-8B?

Llama 3.1-8B is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; GLM-4.6V lists at $0.30 and $0.90.

### Is GLM-4.6V or Llama 3.1-8B better for coding?

GLM-4.6V scores higher on coding benchmarks: 40.9 versus 20.2 in the Noometry coding category.

### Which has the bigger context window?

Both accept 128K tokens.

### How many benchmarks do GLM-4.6V and Llama 3.1-8B share?

11 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and Llama 3.1-8B has 43.