# GLM-4.6 vs Llama 3-70B

> GLM-4.6 is the stronger model overall, scoring 40.9 to 28.5 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-4-6-vs-llama-3-70b
- Last updated: 2026-10-10
- Shared benchmarks: 18

## Summary

- They share 18 benchmarks with published results for both. GLM-4.6 scores higher in 9 categories and Llama 3-70B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-4.6 leads 36.4 to 12.0.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 47.4% for GLM-4.6 and 35.1% for Llama 3-70B.

## Snapshot

| | GLM-4.6 | Llama 3-70B |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 40.9 | 28.5 |
| Rank | 132 | 317 |
| Context | 205K | — |
| Input $/M | $0.60 | — |
| Output $/M | $2.20 | — |
| Weights | Open | Open |

## Coding

- GLM-4.6: 39.9 (#137)
- Llama 3-70B: 35.3 (#215)

| Benchmark | GLM-4.6 | Llama 3-70B |
|---|---|---|
| LMArena Coding | 1449 | 1206 |
| SWE-bench Verified (bash only) | 55.4% | — |
| LMArena WebDev | 1339 | — |
| SciCode | 38.4% | — |
| BigCodeBench Instruct | — | 43.6% |
| BigCodeBench Complete | — | 54.5% |
| ALE-Bench | 340.82 | — |
| HumanEval+ | — | 72% |
| MBPP+ | — | 69% |

## Agentic & Tool Use

- GLM-4.6: 32.1 (#60)
- Llama 3-70B: 20.9 (#132)

| Benchmark | GLM-4.6 | Llama 3-70B |
|---|---|---|
| Terminal-Bench | 24.5% | — |
| Berkeley Function Calling Leaderboard | 72.4% | — |
| Cybench | — | 5% |

## Reasoning

- GLM-4.6: 23.1 (#168)
- Llama 3-70B: 17.4 (#284)

| Benchmark | GLM-4.6 | Llama 3-70B |
|---|---|---|
| Kagi LLM Benchmark | 47.4% | 35.1% |
| LMArena Hard Prompts | 1440 | 1195 |
| CritPt | 1.1% | — |
| DTBench | — | 54.2% |
| Epoch Capabilities Index | — | 122.91 |
| ForecastBench | — | 57.1 |
| WinoGrande | — | 83.5% |

## Math

- GLM-4.6: 36.4 (#115)
- Llama 3-70B: 12.0 (#299)

| Benchmark | GLM-4.6 | Llama 3-70B |
|---|---|---|
| LMArena Math | 1431 | 1218 |
| OTIS Mock AIME 2024-2025 | — | 4.3% |
| MATH Level 5 | — | 22.6% |
| FrontierMath (Feb 2025 set) | 3.8% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |

## Knowledge

- GLM-4.6: 39.6 (#118)
- Llama 3-70B: 20.8 (#272)

| Benchmark | GLM-4.6 | Llama 3-70B |
|---|---|---|
| LMArena Expert | 1432 | 1149 |
| GPQA Diamond | — | 40.6% |
| Vectara Hallucination Rate | 9.5% | — |
| MMLU | — | 79.3% |

## Multilingual

- GLM-4.6: 53.6 (#62)
- Llama 3-70B: 33.8 (#246)

| Benchmark | GLM-4.6 | Llama 3-70B |
|---|---|---|
| LMArena Non-English | 1426 | 1142 |
| LMArena Chinese | 1500 | 1114 |
| LMArena French | 1459 | 1231 |
| LMArena German | 1447 | 1169 |
| LMArena Japanese | 1394 | 1017 |
| LMArena Korean | 1401 | 1017 |
| LMArena Russian | 1418 | 1159 |
| LMArena Spanish | 1436 | 1241 |

## Instruction Following

- GLM-4.6: 74.5 (#93)
- Llama 3-70B: 62.8 (#233)

| Benchmark | GLM-4.6 | Llama 3-70B |
|---|---|---|
| LMArena Instruction Following | 1410 | 1194 |

## Long Context

- GLM-4.6: 43.4 (#91)
- Llama 3-70B: 35.6 (#235)

| Benchmark | GLM-4.6 | Llama 3-70B |
|---|---|---|
| LMArena Longer Query | 1423 | 1174 |

## Writing & Preference

- GLM-4.6: 61.3 (#86)
- Llama 3-70B: 43.0 (#225)

| Benchmark | GLM-4.6 | Llama 3-70B |
|---|---|---|
| LMArena Text | 1440 | 1221 |
| LMArena Creative Writing | 1411 | 1210 |
| LMArena Multi-Turn | 1427 | 1223 |
| EQ-Bench Creative Writing | 1411 | — |

## FAQ

### Is GLM-4.6 better than Llama 3-70B?

GLM-4.6 is the stronger model overall, scoring 40.9 to 28.5 on the Noometry Index.

### Is GLM-4.6 or Llama 3-70B better for coding?

GLM-4.6 scores higher on coding benchmarks: 39.9 versus 35.3 in the Noometry coding category.

### How many benchmarks do GLM-4.6 and Llama 3-70B share?

18 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Llama 3-70B has 31.