# GLM-5 vs Llama 3.1-70B

> GLM-5 is the stronger model overall, scoring 45.9 to 29.4 on the Noometry Index. Llama 3.1-70B costs 3.9× less per token, which makes it the better buy when GLM-5's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/glm-5-vs-llama-3-1-70b
- Last updated: 2026-10-10
- Shared benchmarks: 22

## Summary

- They share 22 benchmarks with published results for both. GLM-5 scores higher in 9 categories and Llama 3.1-70B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5 leads 44.8 to 12.7.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 80% for GLM-5 and 3.6% for Llama 3.1-70B.
- Llama 3.1-70B is cheaper at $0.40 / $0.40 per million input/output tokens, against $1 / $3.20 for GLM-5.
- GLM-5 accepts more context: 205K tokens versus 128K.

## Snapshot

| | GLM-5 | Llama 3.1-70B |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 45.9 | 29.4 |
| Rank | 59 | 299 |
| Context | 205K | 128K |
| Input $/M | $1 | $0.40 |
| Output $/M | $3.20 | $0.40 |
| Weights | Open | Open |

## Coding

- GLM-5: 49.0 (#47)
- Llama 3.1-70B: 29.8 (#292)

| Benchmark | GLM-5 | Llama 3.1-70B |
|---|---|---|
| WeirdML | 48.2% | 9% |
| LMArena Coding | 1461 | 1260 |
| SWE-bench Verified | 72.1% | — |
| SWE-bench Verified (bash only) | 72.8% | — |
| LMArena WebDev | 1434 | — |
| SWE-bench Multilingual | 69.7% | — |
| BigCodeBench Instruct | — | 46.1% |
| BigCodeBench Complete | — | 54.8% |
| ALE-Bench | 765.62 | — |

## Agentic & Tool Use

- GLM-5: 30.9 (#65)
- Llama 3.1-70B: 24.9 (#104)

| Benchmark | GLM-5 | Llama 3.1-70B |
|---|---|---|
| Terminal-Bench | 52.4% | — |
| TheAgentCompany | — | 6.9% |
| τ²-bench Airline | 82.5% | — |
| τ²-bench Banking | 9.8% | — |
| τ²-bench Retail | 73.7% | — |
| τ²-bench Telecom | 86.8% | — |
| BALROG | — | 27.9% |
| Vending-Bench 2 | 4,432 | — |

## Reasoning

- GLM-5: 27.6 (#100)
- Llama 3.1-70B: 20.8 (#215)

| Benchmark | GLM-5 | Llama 3.1-70B |
|---|---|---|
| LMArena Hard Prompts | 1451 | 1241 |
| Epoch Capabilities Index | 145.84 | 125.91 |
| ARC-AGI-2 | 4.9% | — |
| SimpleBench | 53.2% | — |
| Kagi LLM Benchmark | 75% | — |
| NYT Connections (extended) | 74.8% | — |
| ARC-AGI-1 | 44.7% | — |
| Chess Puzzles | 10% | — |
| DTBench | — | 60% |
| LMCA | — | 14.8% |
| ForecastBench | 61 | — |

## Math

- GLM-5: 44.8 (#66)
- Llama 3.1-70B: 12.7 (#298)

| Benchmark | GLM-5 | Llama 3.1-70B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 80% | 3.6% |
| LMArena Math | 1440 | 1252 |
| MathArena Final-Answer Competitions | 65.7% | — |
| Omni-MATH | — | 21% |
| MATH Level 5 | — | 36.7% |
| FrontierMath (Feb 2025 set) | 16.4% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |

## Knowledge

- GLM-5: 52.2 (#59)
- Llama 3.1-70B: 24.1 (#264)

| Benchmark | GLM-5 | Llama 3.1-70B |
|---|---|---|
| GPQA Diamond | 87.8% | 44.2% |
| LMArena Expert | 1454 | 1209 |
| MMLU-Pro | — | 65.3% |
| Vectara Hallucination Rate | 10.1% | — |
| GPQA (HELM) | — | 42.6% |
| MMLU | — | 80.1% |

## Multilingual

- GLM-5: 53.9 (#57)
- Llama 3.1-70B: 38.9 (#220)

| Benchmark | GLM-5 | Llama 3.1-70B |
|---|---|---|
| LMArena Non-English | 1430 | 1219 |
| LMArena Chinese | 1511 | 1215 |
| LMArena French | 1455 | 1260 |
| LMArena German | 1445 | 1222 |
| LMArena Japanese | 1416 | 1132 |
| LMArena Korean | 1421 | 1140 |
| LMArena Russian | 1436 | 1234 |
| LMArena Spanish | 1452 | 1253 |

## Instruction Following

- GLM-5: 75.4 (#64)
- Llama 3.1-70B: 65.5 (#218)

| Benchmark | GLM-5 | Llama 3.1-70B |
|---|---|---|
| LMArena Instruction Following | 1427 | 1231 |
| IFEval | — | 82.1% |

## Long Context

- GLM-5: 44.6 (#58)
- Llama 3.1-70B: 37.6 (#208)

| Benchmark | GLM-5 | Llama 3.1-70B |
|---|---|---|
| LMArena Longer Query | 1446 | 1241 |
| CL-bench | 18.7% | — |

## Writing & Preference

- GLM-5: 66.2 (#38)
- Llama 3.1-70B: 35.6 (#262)

| Benchmark | GLM-5 | Llama 3.1-70B |
|---|---|---|
| LMArena Text | 1446 | 1261 |
| LMArena Creative Writing | 1438 | 1232 |
| EQ-Bench Creative Writing | 1601 | 784 |
| LMArena Multi-Turn | 1456 | 1256 |
| WildBench | — | 75.8% |

## FAQ

### Is GLM-5 better than Llama 3.1-70B?

GLM-5 is the stronger model overall, scoring 45.9 to 29.4 on the Noometry Index. Llama 3.1-70B costs 3.9× less per token, which makes it the better buy when GLM-5's lead doesn't matter for your workload.

### Which is cheaper, GLM-5 or Llama 3.1-70B?

Llama 3.1-70B is cheaper. It lists at $0.40 per million input tokens and $0.40 per million output tokens; GLM-5 lists at $1 and $3.20.

### Is GLM-5 or Llama 3.1-70B better for coding?

GLM-5 scores higher on coding benchmarks: 49.0 versus 29.8 in the Noometry coding category.

### Which has the bigger context window?

GLM-5 does, with 205K tokens against 128K.

### How many benchmarks do GLM-5 and Llama 3.1-70B share?

22 benchmarks have published results for both models. GLM-5 has 45 scored results on Noometry and Llama 3.1-70B has 35.