# GLM-5.3 vs Llama 3-8B

> GLM-5.3 is the stronger model overall, scoring 54.8 to 25.5 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-5-3-vs-llama-3-8b
- Last updated: 2026-10-10
- Shared benchmarks: 22

## Summary

- They share 22 benchmarks with published results for both. GLM-5.3 scores higher in 8 categories and Llama 3-8B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.3 leads 62.3 to 8.8.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 91.1% for GLM-5.3 and 1.9% for Llama 3-8B.

## Snapshot

| | GLM-5.3 | Llama 3-8B |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 54.8 | 25.5 |
| Rank | 26 | 344 |
| Context | 1M | — |
| Input $/M | $1.40 | — |
| Output $/M | $4.40 | — |
| Weights | Open | Open |

## Coding

- GLM-5.3: 59.5 (#14)
- Llama 3-8B: 31.0 (#289)

| Benchmark | GLM-5.3 | Llama 3-8B |
|---|---|---|
| LMArena Coding | 1496 | 1152 |
| DeepSWE | 69% | — |
| FrontierCode | 40.1% | — |
| CursorBench | 42.6% | — |
| LMArena WebDev | 1622 | — |
| FrontierSWE | 30.2% | — |
| SciCode | 59% | — |
| WeirdML | 75.4% | — |
| BigCodeBench Instruct | — | 31.9% |
| BigCodeBench Complete | — | 36.9% |
| ALE-Bench | 1,317 | — |
| HumanEval+ | — | 56.7% |
| MBPP+ | — | 54.8% |

## Agentic & Tool Use

- GLM-5.3: 36.4 (#38)
- Llama 3-8B: —

| Benchmark | GLM-5.3 | Llama 3-8B |
|---|---|---|
| APEX-Agents | 56.6% | — |
| Vending-Bench 2 | 8,164 | — |

## Reasoning

- GLM-5.3: 46.1 (#46)
- Llama 3-8B: 14.3 (#326)

| Benchmark | GLM-5.3 | Llama 3-8B |
|---|---|---|
| Chess Puzzles | 21% | 0% |
| LMArena Hard Prompts | 1489 | 1133 |
| DTBench | 87.7% | 43.9% |
| Epoch Capabilities Index | 155.61 | 116.45 |
| NYT Connections (extended) | 74.2% | — |
| CritPt | 19.1% | — |
| Mystery Game Puzzles | 33% | — |
| LMCA | 55.5% | — |
| Adversarial NLI | — | 57.3% |
| Bench to the Future 3 | 0.15 | — |
| ForecastBench | — | 58.6 |
| WinoGrande | — | 75.7% |

## Math

- GLM-5.3: 62.3 (#33)
- Llama 3-8B: 8.8 (#323)

| Benchmark | GLM-5.3 | Llama 3-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 91.1% | 1.9% |
| LMArena Math | 1489 | 1151 |
| FrontierMath (Tiers 1-3) | 68.8% | — |
| FrontierMath Tier 4 | 29.3% | — |
| ProofBench | 49% | — |
| MATH Level 5 | — | 6.1% |

## Knowledge

- GLM-5.3: 58.3 (#37)
- Llama 3-8B: 7.8 (#308)

| Benchmark | GLM-5.3 | Llama 3-8B |
|---|---|---|
| GPQA Diamond | 90.9% | 26.1% |
| LMArena Expert | 1516 | 1113 |
| SimpleQA Verified | 41% | — |
| ARC (AI2) Challenge | — | 82.8% |
| MMLU | — | 68.8% |
| OpenBookQA | — | 82.6% |
| TriviaQA | — | 67.7% |

## Multilingual

- GLM-5.3: 55.7 (#28)
- Llama 3-8B: 30.8 (#261)

| Benchmark | GLM-5.3 | Llama 3-8B |
|---|---|---|
| LMArena Non-English | 1457 | 1098 |
| LMArena Chinese | 1528 | 1076 |
| LMArena French | 1499 | 1159 |
| LMArena German | 1499 | 1104 |
| LMArena Japanese | 1453 | 967 |
| LMArena Korean | 1472 | 1004 |
| LMArena Russian | 1463 | 1109 |
| LMArena Spanish | 1460 | 1173 |

## Instruction Following

- GLM-5.3: 77.5 (#23)
- Llama 3-8B: 58.4 (#260)

| Benchmark | GLM-5.3 | Llama 3-8B |
|---|---|---|
| LMArena Instruction Following | 1477 | 1127 |

## Long Context

- GLM-5.3: 45.4 (#41)
- Llama 3-8B: 34.2 (#251)

| Benchmark | GLM-5.3 | Llama 3-8B |
|---|---|---|
| LMArena Longer Query | 1482 | 1128 |

## Writing & Preference

- GLM-5.3: 75.7 (#6)
- Llama 3-8B: 37.5 (#256)

| Benchmark | GLM-5.3 | Llama 3-8B |
|---|---|---|
| LMArena Text | 1471 | 1166 |
| LMArena Creative Writing | 1457 | 1150 |
| LMArena Multi-Turn | 1472 | 1152 |
| EQ-Bench Creative Writing | 2075 | — |

## FAQ

### Is GLM-5.3 better than Llama 3-8B?

GLM-5.3 is the stronger model overall, scoring 54.8 to 25.5 on the Noometry Index.

### Is GLM-5.3 or Llama 3-8B better for coding?

GLM-5.3 scores higher on coding benchmarks: 59.5 versus 31.0 in the Noometry coding category.

### How many benchmarks do GLM-5.3 and Llama 3-8B share?

22 benchmarks have published results for both models. GLM-5.3 has 42 scored results on Noometry and Llama 3-8B has 34.