# GLM-4.7 vs Llama-3.3-70B-Instruct

> GLM-4.7 is the stronger model overall, scoring 41.7 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 6.5× less per token, which makes it the better buy when GLM-4.7's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/glm-4-7-vs-llama-3-3-70b-instruct
- Last updated: 2026-10-10
- Shared benchmarks: 24

## Summary

- They share 24 benchmarks with published results for both. GLM-4.7 scores higher in 9 categories and Llama-3.3-70B-Instruct in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-4.7 leads 37.7 to 14.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 83.3% for GLM-4.7 and 5.1% for Llama-3.3-70B-Instruct.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $0.60 / $2.20 for GLM-4.7.
- GLM-4.7 accepts more context: 205K tokens versus 128K.

## Snapshot

| | GLM-4.7 | Llama-3.3-70B-Instruct |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 41.7 | 30.3 |
| Rank | 117 | 283 |
| Context | 205K | 128K |
| Input $/M | $0.60 | $0.10 |
| Output $/M | $2.20 | $0.32 |
| Weights | Open | Open |

## Coding

- GLM-4.7: 43.6 (#74)
- Llama-3.3-70B-Instruct: 30.6 (#284)

| Benchmark | GLM-4.7 | Llama-3.3-70B-Instruct |
|---|---|---|
| SciCode | 45.1% | 26% |
| LMArena Coding | 1454 | 1268 |
| LMArena WebDev | 1435 | — |
| WeirdML | — | 14.4% |
| BigCodeBench Instruct | — | 46.9% |
| LiveBench Coding | — | 36.6% |
| BigCodeBench Complete | — | 57.5% |
| ALE-Bench | 399.48 | — |

## Agentic & Tool Use

- GLM-4.7: 26.4 (#96)
- Llama-3.3-70B-Instruct: 25.6 (#100)

| Benchmark | GLM-4.7 | Llama-3.3-70B-Instruct |
|---|---|---|
| Terminal-Bench | 33.4% | — |
| Berkeley Function Calling Leaderboard | — | 31.9% |
| BALROG | — | 23% |
| Vending-Bench 2 | 2,377 | — |

## Reasoning

- GLM-4.7: 23.7 (#161)
- Llama-3.3-70B-Instruct: 13.7 (#325)

| Benchmark | GLM-4.7 | Llama-3.3-70B-Instruct |
|---|---|---|
| SimpleBench | 47.7% | 19.9% |
| CritPt | 1.7% | 0% |
| LMArena Hard Prompts | 1443 | 1257 |
| Epoch Capabilities Index | 143.53 | 127.32 |
| Chess Puzzles | 6% | — |
| LiveBench Reasoning | — | 50.8% |
| DTBench | — | 59.5% |
| LiveBench Data Analysis | — | 49.5% |
| LMCA | — | 17.5% |
| ForecastBench | — | 58.6 |
| LiveBench | — | 50.2% |

## Math

- GLM-4.7: 37.7 (#92)
- Llama-3.3-70B-Instruct: 14.3 (#292)

| Benchmark | GLM-4.7 | Llama-3.3-70B-Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 83.3% | 5.1% |
| LMArena Math | 1423 | 1268 |
| ProofBench | 6% | — |
| LiveBench Math | — | 42.2% |
| MATH Level 5 | — | 41.6% |
| FrontierMath (Feb 2025 set) | 2.4% | — |
| FrontierMath Tier 4 (v1) | 0% | — |

## Knowledge

- GLM-4.7: 47.0 (#74)
- Llama-3.3-70B-Instruct: 30.5 (#220)

| Benchmark | GLM-4.7 | Llama-3.3-70B-Instruct |
|---|---|---|
| GPQA Diamond | 83.3% | 47.4% |
| Vectara Hallucination Rate | 11.7% | 4.1% |
| LMArena Expert | 1424 | 1225 |
| SimpleQA Verified | 32.2% | — |
| Confabulations | — | 22.8% |
| MMLU | — | 86.3% |

## Multilingual

- GLM-4.7: 53.0 (#75)
- Llama-3.3-70B-Instruct: 40.1 (#215)

| Benchmark | GLM-4.7 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | 1417 | 1236 |
| LMArena Chinese | 1495 | 1217 |
| LMArena French | 1432 | 1280 |
| LMArena German | 1426 | 1251 |
| LMArena Japanese | 1439 | 1149 |
| LMArena Korean | 1401 | 1142 |
| LMArena Russian | 1423 | 1252 |
| LMArena Spanish | 1433 | 1269 |

## Instruction Following

- GLM-4.7: 74.6 (#90)
- Llama-3.3-70B-Instruct: 71.3 (#152)

| Benchmark | GLM-4.7 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Instruction Following | 1411 | 1242 |
| LiveBench Instruction Following | — | 82.7% |

## Long Context

- GLM-4.7: 42.8 (#111)
- Llama-3.3-70B-Instruct: 26.4 (#290)

| Benchmark | GLM-4.7 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Longer Query | 1432 | 1256 |
| Fiction.LiveBench | — | 33.3% |
| CL-bench | 15.9% | — |
| CL-bench Life | 10.9% | — |

## Writing & Preference

- GLM-4.7: 61.1 (#89)
- Llama-3.3-70B-Instruct: 47.8 (#203)

| Benchmark | GLM-4.7 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | 1436 | 1274 |
| LMArena Creative Writing | 1401 | 1250 |
| LMArena Multi-Turn | 1446 | 1279 |
| EQ-Bench Creative Writing | 1413 | — |
| LiveBench Language | — | 39.2% |

## FAQ

### Is GLM-4.7 better than Llama-3.3-70B-Instruct?

GLM-4.7 is the stronger model overall, scoring 41.7 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 6.5× less per token, which makes it the better buy when GLM-4.7's lead doesn't matter for your workload.

### Which is cheaper, GLM-4.7 or Llama-3.3-70B-Instruct?

Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; GLM-4.7 lists at $0.60 and $2.20.

### Is GLM-4.7 or Llama-3.3-70B-Instruct better for coding?

GLM-4.7 scores higher on coding benchmarks: 43.6 versus 30.6 in the Noometry coding category.

### Which has the bigger context window?

GLM-4.7 does, with 205K tokens against 128K.

### How many benchmarks do GLM-4.7 and Llama-3.3-70B-Instruct share?

24 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and Llama-3.3-70B-Instruct has 43.