# GLM-4.5V vs Llama 3.1-8B

> GLM-4.5V is the stronger model overall, scoring 39.2 to 22.8 on the Noometry Index. Llama 3.1-8B costs 16× 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-3-1-8b
- Last updated: 2026-10-10
- Shared benchmarks: 13

## Summary

- They share 13 benchmarks with published results for both. GLM-4.5V scores higher in 8 categories and Llama 3.1-8B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-4.5V leads 36.8 to 8.0.
- Llama 3.1-8B is cheaper at $0.05 / $0.08 per million input/output tokens, against $0.60 / $1.80 for GLM-4.5V.
- Llama 3.1-8B accepts more context: 128K tokens versus 64K.

## Snapshot

| | GLM-4.5V | Llama 3.1-8B |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 39.2 | 22.8 |
| Rank | 158 | 347 |
| Context | 64K | 128K |
| Input $/M | $0.60 | $0.05 |
| Output $/M | $1.80 | $0.08 |
| Weights | Open | Open |

## Coding

- GLM-4.5V: 38.9 (#152)
- Llama 3.1-8B: 19.9 (#335)

| Benchmark | GLM-4.5V | Llama 3.1-8B |
|---|---|---|
| LMArena Coding | 1347 | 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-4.5V: —
- Llama 3.1-8B: 22.3 (#123)

| Benchmark | GLM-4.5V | Llama 3.1-8B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 25.8% |
| BALROG | — | 15.1% |

## Reasoning

- GLM-4.5V: 26.5 (#118)
- Llama 3.1-8B: 14.5 (#316)

| Benchmark | GLM-4.5V | Llama 3.1-8B |
|---|---|---|
| LMArena Hard Prompts | 1335 | 1175 |
| Kagi LLM Benchmark | 59.8% | — |
| CritPt | — | 0% |
| Chess Puzzles | — | 0% |
| DTBench | — | 50.9% |
| LMCA | — | 5.4% |
| Epoch Capabilities Index | — | 116.48 |
| PIQA | — | 81.2% |

## Math

- GLM-4.5V: 34.7 (#157)
- Llama 3.1-8B: 9.5 (#311)

| Benchmark | GLM-4.5V | Llama 3.1-8B |
|---|---|---|
| LMArena Math | 1353 | 1179 |
| OTIS Mock AIME 2024-2025 | — | 1.7% |
| Omni-MATH | — | 13.7% |
| MATH Level 5 | — | 22.9% |
| GSM8K | — | 82.4% |

## Knowledge

- GLM-4.5V: 36.8 (#155)
- Llama 3.1-8B: 8.0 (#302)

| Benchmark | GLM-4.5V | Llama 3.1-8B |
|---|---|---|
| LMArena Expert | 1353 | 1144 |
| GPQA Diamond | — | 27% |
| MMLU-Pro | — | 40.6% |
| GPQA (HELM) | — | 24.7% |
| BoolQ | — | 82.8% |
| MMLU | — | 56.1% |

## Multimodal

- GLM-4.5V: 33.6 (#86)
- Llama 3.1-8B: —

| Benchmark | GLM-4.5V | Llama 3.1-8B |
|---|---|---|
| LMArena Vision | 1154 | — |

## Multilingual

- GLM-4.5V: 44.9 (#172)
- Llama 3.1-8B: 34.2 (#244)

| Benchmark | GLM-4.5V | Llama 3.1-8B |
|---|---|---|
| LMArena Non-English | 1304 | 1148 |
| LMArena Chinese | 1338 | 1151 |
| LMArena Russian | 1299 | 1158 |
| LMArena Spanish | 1335 | 1169 |
| LMArena French | — | 1176 |
| LMArena German | — | 1144 |
| LMArena Japanese | — | 1061 |
| LMArena Korean | — | 1053 |

## Instruction Following

- GLM-4.5V: 69.4 (#170)
- Llama 3.1-8B: 59.1 (#253)

| Benchmark | GLM-4.5V | Llama 3.1-8B |
|---|---|---|
| LMArena Instruction Following | 1311 | 1159 |
| IFEval | — | 74.3% |

## Long Context

- GLM-4.5V: 39.6 (#166)
- Llama 3.1-8B: 35.8 (#233)

| Benchmark | GLM-4.5V | Llama 3.1-8B |
|---|---|---|
| LMArena Longer Query | 1305 | 1182 |

## Writing & Preference

- GLM-4.5V: 52.7 (#162)
- Llama 3.1-8B: 29.9 (#285)

| Benchmark | GLM-4.5V | Llama 3.1-8B |
|---|---|---|
| LMArena Text | 1333 | 1187 |
| LMArena Creative Writing | 1295 | 1154 |
| LMArena Multi-Turn | 1333 | 1172 |
| EQ-Bench Creative Writing | — | 713 |
| WildBench | — | 68.7% |

## FAQ

### Is GLM-4.5V better than Llama 3.1-8B?

GLM-4.5V is the stronger model overall, scoring 39.2 to 22.8 on the Noometry Index. Llama 3.1-8B costs 16× 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 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-4.5V lists at $0.60 and $1.80.

### Is GLM-4.5V or Llama 3.1-8B better for coding?

GLM-4.5V scores higher on coding benchmarks: 38.9 versus 19.9 in the Noometry coding category.

### Which has the bigger context window?

Llama 3.1-8B does, with 128K tokens against 64K.

### How many benchmarks do GLM-4.5V and Llama 3.1-8B share?

13 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and Llama 3.1-8B has 43.