# GLM-4.6V vs Llama 3.1-70B

> GLM-4.6V is the stronger model overall, scoring 41.3 to 29.6 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-4-6v-vs-llama-3-1-70b
- 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 3.1-70B in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-4.6V leads 56.6 to 35.4.
- Llama 3.1-70B is cheaper at $0.40 / $0.40 per million input/output tokens, against $0.30 / $0.90 for GLM-4.6V.

## Snapshot

| | GLM-4.6V | Llama 3.1-70B |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 41.3 | 29.6 |
| Rank | 137 | 308 |
| Context | 128K | 128K |
| Input $/M | $0.30 | $0.40 |
| Output $/M | $0.90 | $0.40 |
| Weights | Open | Open |

## Coding

- GLM-4.6V: 40.9 (#128)
- Llama 3.1-70B: 30.3 (#296)

| Benchmark | GLM-4.6V | Llama 3.1-70B |
|---|---|---|
| LMArena Coding | 1390 | 1260 |
| WeirdML | — | 9% |
| BigCodeBench Instruct | — | 46.1% |
| BigCodeBench Complete | — | 54.8% |

## Agentic & Tool Use

- GLM-4.6V: —
- Llama 3.1-70B: 25.1 (#112)

| Benchmark | GLM-4.6V | Llama 3.1-70B |
|---|---|---|
| TheAgentCompany | — | 6.9% |
| BALROG | — | 27.9% |

## Reasoning

- GLM-4.6V: 27.6 (#115)
- Llama 3.1-70B: 21.6 (#220)

| Benchmark | GLM-4.6V | Llama 3.1-70B |
|---|---|---|
| LMArena Hard Prompts | 1368 | 1241 |
| DTBench | — | 60% |
| LMCA | — | 14.8% |
| Epoch Capabilities Index | — | 125.92 |

## Math

- GLM-4.6V: —
- Llama 3.1-70B: 13.5 (#304)

| Benchmark | GLM-4.6V | Llama 3.1-70B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 3.6% |
| Omni-MATH | — | 21% |
| LMArena Math | — | 1252 |
| MATH Level 5 | — | 36.7% |

## Knowledge

- GLM-4.6V: 38.0 (#149)
- Llama 3.1-70B: 24.2 (#269)

| Benchmark | GLM-4.6V | Llama 3.1-70B |
|---|---|---|
| LMArena Expert | 1371 | 1209 |
| GPQA Diamond | — | 44.2% |
| MMLU-Pro | — | 65.3% |
| GPQA (HELM) | — | 42.6% |
| MMLU | — | 80.1% |

## Multimodal

- GLM-4.6V: 34.8 (#90)
- Llama 3.1-70B: —

| Benchmark | GLM-4.6V | Llama 3.1-70B |
|---|---|---|
| LMArena Vision | 1164 | — |

## Multilingual

- GLM-4.6V: 48.6 (#141)
- Llama 3.1-70B: 38.8 (#225)

| Benchmark | GLM-4.6V | Llama 3.1-70B |
|---|---|---|
| LMArena Non-English | 1359 | 1219 |
| LMArena Chinese | 1425 | 1215 |
| LMArena Russian | 1340 | 1234 |
| LMArena French | — | 1261 |
| LMArena German | — | 1222 |
| LMArena Japanese | — | 1132 |
| LMArena Korean | — | 1140 |
| LMArena Spanish | — | 1253 |

## Instruction Following

- GLM-4.6V: 71.4 (#151)
- Llama 3.1-70B: 65.3 (#223)

| Benchmark | GLM-4.6V | Llama 3.1-70B |
|---|---|---|
| LMArena Instruction Following | 1352 | 1231 |
| IFEval | — | 82.1% |

## Long Context

- GLM-4.6V: 41.3 (#143)
- Llama 3.1-70B: 37.6 (#214)

| Benchmark | GLM-4.6V | Llama 3.1-70B |
|---|---|---|
| LMArena Longer Query | 1358 | 1241 |

## Writing & Preference

- GLM-4.6V: 56.6 (#137)
- Llama 3.1-70B: 35.4 (#267)

| Benchmark | GLM-4.6V | Llama 3.1-70B |
|---|---|---|
| LMArena Text | 1377 | 1261 |
| LMArena Creative Writing | 1347 | 1232 |
| LMArena Multi-Turn | 1360 | 1256 |
| EQ-Bench Creative Writing | — | 784 |
| WildBench | — | 75.8% |

## FAQ

### Is GLM-4.6V better than Llama 3.1-70B?

GLM-4.6V is the stronger model overall, scoring 41.3 to 29.6 on the Noometry Index.

### Which is cheaper, GLM-4.6V or Llama 3.1-70B?

Llama 3.1-70B is cheaper. It lists at $0.40 per million input tokens and $0.40 per million output tokens; GLM-4.6V lists at $0.30 and $0.90.

### Is GLM-4.6V or Llama 3.1-70B better for coding?

GLM-4.6V scores higher on coding benchmarks: 40.9 versus 30.3 in the Noometry coding category.

### Which has the bigger context window?

Both accept 128K tokens.

### How many benchmarks do GLM-4.6V and Llama 3.1-70B share?

11 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and Llama 3.1-70B has 35.