# GLM-5.1 vs Llama 3-70B

> GLM-5.1 is the stronger model overall, scoring 47.6 to 28.5 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-5-1-vs-llama-3-70b
- Last updated: 2026-10-10
- Shared benchmarks: 20

## Summary

- They share 20 benchmarks with published results for both. GLM-5.1 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-5.1 leads 49.0 to 12.0.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 93.3% for GLM-5.1 and 4.3% for Llama 3-70B.

## Snapshot

| | GLM-5.1 | Llama 3-70B |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 47.6 | 28.5 |
| Rank | 51 | 317 |
| Context | 200K | — |
| Input $/M | $1.40 | — |
| Output $/M | $4.40 | — |
| Weights | Open | Open |

## Coding

- GLM-5.1: 48.5 (#52)
- Llama 3-70B: 35.3 (#215)

| Benchmark | GLM-5.1 | Llama 3-70B |
|---|---|---|
| LMArena Coding | 1486 | 1206 |
| SWE-bench Verified | 74.2% | — |
| LMArena WebDev | 1509 | — |
| SciCode | 43.8% | — |
| WeirdML | 57.1% | — |
| BigCodeBench Instruct | — | 43.6% |
| BigCodeBench Complete | — | 54.5% |
| ALE-Bench | 887.1 | — |
| HumanEval+ | — | 72% |
| MBPP+ | — | 69% |

## Agentic & Tool Use

- GLM-5.1: 24.8 (#106)
- Llama 3-70B: 20.9 (#132)

| Benchmark | GLM-5.1 | Llama 3-70B |
|---|---|---|
| APEX-Agents | 40.9% | — |
| Cybench | — | 5% |
| ExploitBench | 18.1% | — |
| GBAEval | 0% | — |
| Vending-Bench 2 | 5,634 | — |

## Reasoning

- GLM-5.1: 38.2 (#57)
- Llama 3-70B: 17.4 (#284)

| Benchmark | GLM-5.1 | Llama 3-70B |
|---|---|---|
| LMArena Hard Prompts | 1472 | 1195 |
| Epoch Capabilities Index | 149.86 | 122.91 |
| SimpleBench | 55.1% | — |
| Kagi LLM Benchmark | — | 35.1% |
| NYT Connections (extended) | 77.7% | — |
| CritPt | 4.6% | — |
| Chess Puzzles | 19% | — |
| Thematic Generalization | 69.8% | — |
| DTBench | — | 54.2% |
| ForecastBench | — | 57.1 |
| WinoGrande | — | 83.5% |

## Math

- GLM-5.1: 49.0 (#48)
- Llama 3-70B: 12.0 (#299)

| Benchmark | GLM-5.1 | Llama 3-70B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 93.3% | 4.3% |
| LMArena Math | 1472 | 1218 |
| FrontierMath (Tiers 1-3) | 36.8% | — |
| MathArena Final-Answer Competitions | 67.1% | — |
| ProofBench | 22.2% | — |
| MATH Level 5 | — | 22.6% |
| FrontierMath (Feb 2025 set) | 33.4% | — |
| FrontierMath Tier 4 (v1) | 12.5% | — |

## Knowledge

- GLM-5.1: 55.0 (#45)
- Llama 3-70B: 20.8 (#272)

| Benchmark | GLM-5.1 | Llama 3-70B |
|---|---|---|
| GPQA Diamond | 89.9% | 40.6% |
| LMArena Expert | 1477 | 1149 |
| SimpleQA Verified | 34% | — |
| MMLU | — | 79.3% |

## Multilingual

- GLM-5.1: 55.2 (#34)
- Llama 3-70B: 33.8 (#246)

| Benchmark | GLM-5.1 | Llama 3-70B |
|---|---|---|
| LMArena Non-English | 1448 | 1142 |
| LMArena Chinese | 1516 | 1114 |
| LMArena French | 1478 | 1231 |
| LMArena German | 1466 | 1169 |
| LMArena Japanese | 1436 | 1017 |
| LMArena Korean | 1423 | 1017 |
| LMArena Russian | 1453 | 1159 |
| LMArena Spanish | 1467 | 1241 |

## Instruction Following

- GLM-5.1: 76.5 (#41)
- Llama 3-70B: 62.8 (#233)

| Benchmark | GLM-5.1 | Llama 3-70B |
|---|---|---|
| LMArena Instruction Following | 1451 | 1194 |

## Long Context

- GLM-5.1: 44.9 (#51)
- Llama 3-70B: 35.6 (#235)

| Benchmark | GLM-5.1 | Llama 3-70B |
|---|---|---|
| LMArena Longer Query | 1467 | 1174 |

## Writing & Preference

- GLM-5.1: 67.2 (#29)
- Llama 3-70B: 43.0 (#225)

| Benchmark | GLM-5.1 | Llama 3-70B |
|---|---|---|
| LMArena Text | 1462 | 1221 |
| LMArena Creative Writing | 1454 | 1210 |
| LMArena Multi-Turn | 1473 | 1223 |
| EQ-Bench Creative Writing | 1592 | — |

## FAQ

### Is GLM-5.1 better than Llama 3-70B?

GLM-5.1 is the stronger model overall, scoring 47.6 to 28.5 on the Noometry Index.

### Is GLM-5.1 or Llama 3-70B better for coding?

GLM-5.1 scores higher on coding benchmarks: 48.5 versus 35.3 in the Noometry coding category.

### How many benchmarks do GLM-5.1 and Llama 3-70B share?

20 benchmarks have published results for both models. GLM-5.1 has 41 scored results on Noometry and Llama 3-70B has 31.