# GLM-4.7-Flash vs Llama 3-8B

> GLM-4.7-Flash is the stronger model overall, scoring 38.4 to 25.3 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-4-7-flash-vs-llama-3-8b
- Last updated: 2026-10-10
- Shared benchmarks: 19

## Summary

- They share 19 benchmarks with published results for both. GLM-4.7-Flash scores higher in 8 categories and Llama 3-8B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-4.7-Flash leads 35.4 to 7.8.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 58.3% for GLM-4.7-Flash and 1.9% for Llama 3-8B.
- Both cost about the same: $0.06 input and $0.40 output per million tokens.
- GLM-4.7-Flash accepts more context: 200K tokens versus 8K.

## Snapshot

| | GLM-4.7-Flash | Llama 3-8B |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 38.4 | 25.3 |
| Rank | 175 | 338 |
| Context | 200K | 8K |
| Input $/M | $0.06 | $0.14 |
| Output $/M | $0.40 | $0.14 |
| Weights | Open | Open |

## Coding

- GLM-4.7-Flash: 40.0 (#133)
- Llama 3-8B: 30.5 (#285)

| Benchmark | GLM-4.7-Flash | Llama 3-8B |
|---|---|---|
| LMArena Coding | 1383 | 1152 |
| BigCodeBench Instruct | — | 31.9% |
| BigCodeBench Complete | — | 36.9% |
| HumanEval+ | — | 56.7% |
| MBPP+ | — | 54.8% |

## Reasoning

- GLM-4.7-Flash: 20.2 (#224)
- Llama 3-8B: 13.8 (#321)

| Benchmark | GLM-4.7-Flash | Llama 3-8B |
|---|---|---|
| Chess Puzzles | 0% | 0% |
| LMArena Hard Prompts | 1356 | 1133 |
| DTBench | — | 43.9% |
| Adversarial NLI | — | 57.3% |
| Epoch Capabilities Index | — | 116.33 |
| ForecastBench | — | 58.6 |
| WinoGrande | — | 75.7% |

## Math

- GLM-4.7-Flash: 34.4 (#159)
- Llama 3-8B: 8.3 (#317)

| Benchmark | GLM-4.7-Flash | Llama 3-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 58.3% | 1.9% |
| LMArena Math | 1354 | 1151 |
| MATH Level 5 | — | 6.1% |

## Knowledge

- GLM-4.7-Flash: 35.4 (#177)
- Llama 3-8B: 7.8 (#303)

| Benchmark | GLM-4.7-Flash | Llama 3-8B |
|---|---|---|
| GPQA Diamond | 60.5% | 26.1% |
| LMArena Expert | 1357 | 1113 |
| Vectara Hallucination Rate | 9.3% | — |
| ARC (AI2) Challenge | — | 82.8% |
| MMLU | — | 68.8% |
| OpenBookQA | — | 82.6% |
| TriviaQA | — | 67.7% |

## Multilingual

- GLM-4.7-Flash: 46.8 (#152)
- Llama 3-8B: 31.0 (#256)

| Benchmark | GLM-4.7-Flash | Llama 3-8B |
|---|---|---|
| LMArena Non-English | 1331 | 1098 |
| LMArena Chinese | 1403 | 1076 |
| LMArena French | 1332 | 1158 |
| LMArena German | 1336 | 1104 |
| LMArena Korean | 1284 | 1003 |
| LMArena Russian | 1333 | 1109 |
| LMArena Spanish | 1351 | 1172 |
| LMArena Japanese | — | 967 |

## Instruction Following

- GLM-4.7-Flash: 70.3 (#162)
- Llama 3-8B: 58.7 (#255)

| Benchmark | GLM-4.7-Flash | Llama 3-8B |
|---|---|---|
| LMArena Instruction Following | 1327 | 1127 |

## Long Context

- GLM-4.7-Flash: 40.9 (#143)
- Llama 3-8B: 34.2 (#246)

| Benchmark | GLM-4.7-Flash | Llama 3-8B |
|---|---|---|
| LMArena Longer Query | 1345 | 1128 |

## Writing & Preference

- GLM-4.7-Flash: 47.7 (#206)
- Llama 3-8B: 37.7 (#251)

| Benchmark | GLM-4.7-Flash | Llama 3-8B |
|---|---|---|
| LMArena Text | 1351 | 1166 |
| LMArena Creative Writing | 1297 | 1150 |
| LMArena Multi-Turn | 1342 | 1152 |
| EQ-Bench Creative Writing | 1125 | — |

## FAQ

### Is GLM-4.7-Flash better than Llama 3-8B?

GLM-4.7-Flash is the stronger model overall, scoring 38.4 to 25.3 on the Noometry Index.

### Which is cheaper, GLM-4.7-Flash or Llama 3-8B?

Llama 3-8B is cheaper. It lists at $0.14 per million input tokens and $0.14 per million output tokens; GLM-4.7-Flash lists at $0.06 and $0.40.

### Is GLM-4.7-Flash or Llama 3-8B better for coding?

GLM-4.7-Flash scores higher on coding benchmarks: 40.0 versus 30.5 in the Noometry coding category.

### Which has the bigger context window?

GLM-4.7-Flash does, with 200K tokens against 8K.

### How many benchmarks do GLM-4.7-Flash and Llama 3-8B share?

19 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and Llama 3-8B has 34.