# GLM-5.2 vs GPT-4

> GLM-5.2 is the stronger model overall, scoring 50.9 to 28.9 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-5-2-vs-gpt-4
- Last updated: 2026-10-10
- Shared benchmarks: 26

## Summary

- They share 26 benchmarks with published results for both. GLM-5.2 scores higher in 8 categories and GPT-4 in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.2 leads 55.4 to 10.1.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 86.4% for GLM-5.2 and 1.1% for GPT-4.
- GLM-5.2 is cheaper at $1.40 / $4.40 per million input/output tokens, against $30 / $60 for GPT-4.
- GLM-5.2 accepts more context: 1M tokens versus 8K.
- GLM-5.2 has downloadable open weights; the other is API-only.

## Snapshot

| | GLM-5.2 | GPT-4 |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 50.9 | 28.9 |
| Rank | 38 | 308 |
| Context | 1M | 8K |
| Input $/M | $1.40 | $30 |
| Output $/M | $4.40 | $60 |
| Weights | Open | Proprietary |

## Coding

- GLM-5.2: 50.9 (#37)
- GPT-4: 31.2 (#277)

| Benchmark | GLM-5.2 | GPT-4 |
|---|---|---|
| WeirdML | 70.1% | 12.4% |
| LMArena Coding | 1484 | 1254 |
| SWE-bench Verified | 78.7% | — |
| DeepSWE | 43.8% | — |
| FrontierCode | 24.5% | — |
| LMArena WebDev | 1604 | — |
| SciCode | 50.5% | — |
| BigCodeBench Instruct | — | 46% |
| BigCodeBench Complete | — | 57.2% |
| ALE-Bench | 1,047 | — |
| HumanEval+ | — | 79.3% |

## Agentic & Tool Use

- GLM-5.2: 31.6 (#63)
- GPT-4: —

| Benchmark | GLM-5.2 | GPT-4 |
|---|---|---|
| τ²-bench Banking | 37.1% | — |
| PostTrainBench | 31.7% | — |
| GBAEval | 0% | — |
| METR Time Horizons | — | 36.1% |
| Vending-Bench 2 | 8,314 | — |

## Reasoning

- GLM-5.2: 42.1 (#46)
- GPT-4: 17.3 (#285)

| Benchmark | GLM-5.2 | GPT-4 |
|---|---|---|
| Chess Puzzles | 21% | 4% |
| LMArena Hard Prompts | 1480 | 1241 |
| Mystery Game Puzzles | 19% | 12% |
| DTBench | 93.6% | 62.7% |
| LMCA | 45.8% | 17.1% |
| Epoch Capabilities Index | 151.76 | 125.88 |
| 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% | — |
| EBR-Bench | 9.5% | — |
| Surface Evolver Bench | 55.6% | — |
| BIG-Bench Hard | — | 75.1% |
| ForecastBench | — | 57.8 |
| HellaSwag | — | 95.3% |
| WinoGrande | — | 87.5% |

## Math

- GLM-5.2: 55.4 (#35)
- GPT-4: 10.1 (#304)

| Benchmark | GLM-5.2 | GPT-4 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 86.4% | 1.1% |
| LMArena Math | 1482 | 1269 |
| FrontierMath (Tiers 1-3) | 59.2% | — |
| FrontierMath Tier 4 | 29.3% | — |
| MathArena Final-Answer Competitions | 67.6% | — |
| ProofBench | 35% | — |
| MATH Level 5 | — | 23% |
| GSM8K | — | 92% |

## Knowledge

- GLM-5.2: 57.1 (#36)
- GPT-4: 18.4 (#277)

| Benchmark | GLM-5.2 | GPT-4 |
|---|---|---|
| GPQA Diamond | 91.9% | 35.7% |
| LMArena Expert | 1484 | 1211 |
| SimpleQA Verified | 34.2% | — |
| MMLU | — | 86.4% |
| TriviaQA | — | 84.8% |

## Multilingual

- GLM-5.2: 56.0 (#25)
- GPT-4: 40.8 (#210)

| Benchmark | GLM-5.2 | GPT-4 |
|---|---|---|
| LMArena Non-English | 1459 | 1246 |
| LMArena Chinese | 1518 | 1242 |
| LMArena French | 1476 | 1282 |
| LMArena German | 1470 | 1251 |
| LMArena Japanese | 1451 | 1209 |
| LMArena Korean | 1445 | 1184 |
| LMArena Russian | 1467 | 1251 |
| LMArena Spanish | 1477 | 1261 |

## Instruction Following

- GLM-5.2: 77.1 (#32)
- GPT-4: 65.5 (#217)

| Benchmark | GLM-5.2 | GPT-4 |
|---|---|---|
| LMArena Instruction Following | 1465 | 1241 |

## Long Context

- GLM-5.2: 45.3 (#43)
- GPT-4: 37.7 (#207)

| Benchmark | GLM-5.2 | GPT-4 |
|---|---|---|
| LMArena Longer Query | 1479 | 1244 |

## Writing & Preference

- GLM-5.2: 70.7 (#20)
- GPT-4: 35.0 (#263)

| Benchmark | GLM-5.2 | GPT-4 |
|---|---|---|
| LMArena Text | 1470 | 1263 |
| LMArena Creative Writing | 1465 | 1244 |
| EQ-Bench Creative Writing | 1757 | 752 |
| LMArena Multi-Turn | 1468 | 1257 |
| EQ-Bench 4 | 1222 | — |

## FAQ

### Is GLM-5.2 better than GPT-4?

GLM-5.2 is the stronger model overall, scoring 50.9 to 28.9 on the Noometry Index.

### Which is cheaper, GLM-5.2 or GPT-4?

GLM-5.2 is cheaper. It lists at $1.40 per million input tokens and $4.40 per million output tokens; GPT-4 lists at $30 and $60.

### Is GLM-5.2 or GPT-4 better for coding?

GLM-5.2 scores higher on coding benchmarks: 50.9 versus 31.2 in the Noometry coding category.

### Which has the bigger context window?

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

### How many benchmarks do GLM-5.2 and GPT-4 share?

26 benchmarks have published results for both models. GLM-5.2 has 50 scored results on Noometry and GPT-4 has 38.