# Gemma 2 27B vs GLM-5.2

> GLM-5.2 is the stronger model overall, scoring 51.1 to 29.4 on the Noometry Index. Gemma 2 27B costs 3.3× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/gemma-2-27b-vs-glm-5-2
- Last updated: 2026-10-10
- Shared benchmarks: 22

## Summary

- They share 22 benchmarks with published results for both. Gemma 2 27B scores higher in 0 categories and GLM-5.2 in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.2 leads 55.7 to 10.7.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 1.4% for Gemma 2 27B and 86.4% for GLM-5.2.
- Gemma 2 27B is cheaper at $0.65 / $0.65 per million input/output tokens, against $1.40 / $4.40 for GLM-5.2.
- GLM-5.2 accepts more context: 1M tokens versus 8K.

## Snapshot

| | Gemma 2 27B | GLM-5.2 |
|---|---|---|
| Provider | Google | Z.ai (Zhipu) |
| Noometry Index | 29.4 | 51.1 |
| Rank | 312 | 44 |
| Context | 8K | 1M |
| Input $/M | $0.65 | $1.40 |
| Output $/M | $0.65 | $4.40 |
| Weights | Open | Open |

## Coding

- Gemma 2 27B: 34.1 (#246)
- GLM-5.2: 51.3 (#41)

| Benchmark | Gemma 2 27B | GLM-5.2 |
|---|---|---|
| LMArena Coding | 1211 | 1485 |
| SWE-bench Verified | — | 78.7% |
| DeepSWE | — | 43.8% |
| FrontierCode | — | 24.5% |
| LMArena WebDev | — | 1603 |
| SciCode | — | 50.5% |
| WeirdML | — | 70.1% |
| BigCodeBench Instruct | 42.8% | — |
| LiveBench Coding | 36% | — |
| BigCodeBench Complete | 52.5% | — |
| ALE-Bench | — | 1,047 |

## Agentic & Tool Use

- Gemma 2 27B: —
- GLM-5.2: 32.4 (#63)

| Benchmark | Gemma 2 27B | GLM-5.2 |
|---|---|---|
| APEX-Agents | — | 45.2% |
| τ²-bench Banking | — | 37.1% |
| PostTrainBench | — | 31.7% |
| GBAEval | — | 0% |
| Vending-Bench 2 | — | 8,314 |

## Reasoning

- Gemma 2 27B: 15.3 (#315)
- GLM-5.2: 42.3 (#52)

| Benchmark | Gemma 2 27B | GLM-5.2 |
|---|---|---|
| LMArena Hard Prompts | 1198 | 1480 |
| DTBench | 48% | 93.6% |
| LMCA | 7.1% | 45.8% |
| Epoch Capabilities Index | 122.08 | 151.78 |
| ARC-AGI-2 | — | 22.8% |
| SimpleBench | — | 58.8% |
| Kagi LLM Benchmark | — | 62.6% |
| NYT Connections (extended) | — | 74.3% |
| ARC-AGI-1 | — | 77% |
| CritPt | — | 20.9% |
| Chess Puzzles | — | 21% |
| EBR-Bench | — | 9.5% |
| LiveBench Reasoning | 28.1% | — |
| Mystery Game Puzzles | — | 19% |
| LiveBench Data Analysis | 47.9% | — |
| Surface Evolver Bench | — | 55.6% |
| LiveBench | 38.2% | — |

## Math

- Gemma 2 27B: 10.7 (#311)
- GLM-5.2: 55.7 (#43)

| Benchmark | Gemma 2 27B | GLM-5.2 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 1.4% | 86.4% |
| LMArena Math | 1212 | 1482 |
| FrontierMath (Tiers 1-3) | — | 59.2% |
| FrontierMath Tier 4 | — | 29.3% |
| MathArena Final-Answer Competitions | — | 67.6% |
| ProofBench | — | 35% |
| LiveBench Math | 26.5% | — |
| MATH Level 5 | 27.9% | — |

## Knowledge

- Gemma 2 27B: 19.0 (#280)
- GLM-5.2: 57.1 (#40)

| Benchmark | Gemma 2 27B | GLM-5.2 |
|---|---|---|
| GPQA Diamond | 36.5% | 91.9% |
| LMArena Expert | 1172 | 1486 |
| SimpleQA Verified | — | 34.2% |
| Confabulations | 27.1% | — |
| MMLU | 75.7% | — |

## Multilingual

- Gemma 2 27B: 38.6 (#226)
- GLM-5.2: 55.8 (#26)

| Benchmark | Gemma 2 27B | GLM-5.2 |
|---|---|---|
| LMArena Non-English | 1217 | 1459 |
| LMArena Chinese | 1221 | 1519 |
| LMArena French | 1247 | 1479 |
| LMArena German | 1209 | 1468 |
| LMArena Japanese | 1175 | 1451 |
| LMArena Korean | 1174 | 1445 |
| LMArena Russian | 1234 | 1466 |
| LMArena Spanish | 1228 | 1477 |

## Instruction Following

- Gemma 2 27B: 60.5 (#249)
- GLM-5.2: 76.9 (#34)

| Benchmark | Gemma 2 27B | GLM-5.2 |
|---|---|---|
| LMArena Instruction Following | 1206 | 1465 |
| LiveBench Instruction Following | 58.1% | — |

## Long Context

- Gemma 2 27B: 37.3 (#218)
- GLM-5.2: 45.3 (#43)

| Benchmark | Gemma 2 27B | GLM-5.2 |
|---|---|---|
| LMArena Longer Query | 1231 | 1479 |

## Writing & Preference

- Gemma 2 27B: 44.2 (#225)
- GLM-5.2: 70.4 (#21)

| Benchmark | Gemma 2 27B | GLM-5.2 |
|---|---|---|
| LMArena Text | 1231 | 1470 |
| LMArena Creative Writing | 1241 | 1462 |
| LMArena Multi-Turn | 1224 | 1469 |
| EQ-Bench Creative Writing | — | 1757 |
| EQ-Bench 4 | — | 1222 |
| LiveBench Language | 32.6% | — |

## FAQ

### Is Gemma 2 27B better than GLM-5.2?

GLM-5.2 is the stronger model overall, scoring 51.1 to 29.4 on the Noometry Index. Gemma 2 27B costs 3.3× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.

### Which is cheaper, Gemma 2 27B or GLM-5.2?

Gemma 2 27B is cheaper. It lists at $0.65 per million input tokens and $0.65 per million output tokens; GLM-5.2 lists at $1.40 and $4.40.

### Is Gemma 2 27B or GLM-5.2 better for coding?

GLM-5.2 scores higher on coding benchmarks: 51.3 versus 34.1 in the Noometry coding category.

### Which has the bigger context window?

GLM-5.2 does, with 1M tokens against 8K.

### How many benchmarks do Gemma 2 27B and GLM-5.2 share?

22 benchmarks have published results for both models. Gemma 2 27B has 34 scored results on Noometry and GLM-5.2 has 51.