# GLM-5.2 vs GPT-5 Nano

> GLM-5.2 is the stronger model overall, scoring 50.9 to 33.4 on the Noometry Index. GPT-5 Nano costs 16× 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/glm-5-2-vs-gpt-5-nano
- Last updated: 2026-10-10
- Shared benchmarks: 32

## Summary

- They share 32 benchmarks with published results for both. GLM-5.2 scores higher in 9 categories and GPT-5 Nano in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-5.2 leads 70.7 to 39.4.
- The biggest single-benchmark swing is ARC-AGI-1: 77% for GLM-5.2 and 20.7% for GPT-5 Nano.
- GPT-5 Nano is cheaper at $0.05 / $0.40 per million input/output tokens, against $1.40 / $4.40 for GLM-5.2.
- GLM-5.2 accepts more context: 1M tokens versus 400K.
- GLM-5.2 has downloadable open weights; the other is API-only.

## Snapshot

| | GLM-5.2 | GPT-5 Nano |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 50.9 | 33.4 |
| Rank | 38 | 234 |
| Context | 1M | 400K |
| Input $/M | $1.40 | $0.05 |
| Output $/M | $4.40 | $0.40 |
| Weights | Open | Proprietary |

## Coding

- GLM-5.2: 50.9 (#37)
- GPT-5 Nano: 33.3 (#248)

| Benchmark | GLM-5.2 | GPT-5 Nano |
|---|---|---|
| WeirdML | 70.1% | 38.1% |
| LMArena Coding | 1484 | 1351 |
| ALE-Bench | 1,047 | 718.67 |
| SWE-bench Verified | 78.7% | — |
| DeepSWE | 43.8% | — |
| FrontierCode | 24.5% | — |
| SWE-bench Verified (bash only) | — | 34.8% |
| LMArena WebDev | 1604 | — |
| SciCode | 50.5% | — |

## Agentic & Tool Use

- GLM-5.2: 31.6 (#63)
- GPT-5 Nano: 25.6 (#99)

| Benchmark | GLM-5.2 | GPT-5 Nano |
|---|---|---|
| Terminal-Bench | — | 21.8% |
| Berkeley Function Calling Leaderboard | — | 51.5% |
| τ²-bench Banking | 37.1% | — |
| PostTrainBench | 31.7% | — |
| GBAEval | 0% | — |
| Vending-Bench 2 | 8,314 | — |

## Reasoning

- GLM-5.2: 42.1 (#46)
- GPT-5 Nano: 16.4 (#291)

| Benchmark | GLM-5.2 | GPT-5 Nano |
|---|---|---|
| ARC-AGI-2 | 22.8% | 2.6% |
| Kagi LLM Benchmark | 62.6% | 62.2% |
| ARC-AGI-1 | 77% | 20.7% |
| Chess Puzzles | 21% | 27% |
| LMArena Hard Prompts | 1480 | 1329 |
| Mystery Game Puzzles | 19% | 9% |
| DTBench | 93.6% | 62.7% |
| LMCA | 45.8% | 7.9% |
| Epoch Capabilities Index | 151.76 | 139.39 |
| SimpleBench | 58.8% | — |
| NYT Connections (extended) | 74.3% | — |
| CritPt | 20.9% | — |
| EBR-Bench | 9.5% | — |
| Surface Evolver Bench | 55.6% | — |
| ForecastBench | — | 59.1 |

## Math

- GLM-5.2: 55.4 (#35)
- GPT-5 Nano: 29.1 (#221)

| Benchmark | GLM-5.2 | GPT-5 Nano |
|---|---|---|
| FrontierMath (Tiers 1-3) | 59.2% | 20% |
| FrontierMath Tier 4 | 29.3% | 2.4% |
| OTIS Mock AIME 2024-2025 | 86.4% | 81.1% |
| ProofBench | 35% | 12% |
| LMArena Math | 1482 | 1318 |
| MathArena Final-Answer Competitions | 67.6% | — |
| Omni-MATH | — | 54.6% |
| MATH Level 5 | — | 95.2% |
| FrontierMath (Feb 2025 set) | — | 8.3% |
| FrontierMath Tier 4 (v1) | — | 2.1% |

## Knowledge

- GLM-5.2: 57.1 (#36)
- GPT-5 Nano: 35.8 (#166)

| Benchmark | GLM-5.2 | GPT-5 Nano |
|---|---|---|
| GPQA Diamond | 91.9% | 69.4% |
| SimpleQA Verified | 34.2% | 11.7% |
| LMArena Expert | 1484 | 1321 |
| MMLU-Pro | — | 77.8% |
| Vectara Hallucination Rate | — | 10.5% |
| GPQA (HELM) | — | 67.9% |

## Multimodal

- GLM-5.2: —
- GPT-5 Nano: 30.7 (#102)

| Benchmark | GLM-5.2 | GPT-5 Nano |
|---|---|---|
| LMArena Vision | — | 1159 |
| VPCT | — | 37.2% |

## Multilingual

- GLM-5.2: 56.0 (#25)
- GPT-5 Nano: 45.5 (#167)

| Benchmark | GLM-5.2 | GPT-5 Nano |
|---|---|---|
| LMArena Non-English | 1459 | 1313 |
| LMArena Chinese | 1518 | 1355 |
| LMArena German | 1470 | 1329 |
| LMArena Korean | 1445 | 1269 |
| LMArena Russian | 1467 | 1296 |
| LMArena Spanish | 1477 | 1360 |
| LMArena French | 1476 | — |
| LMArena Japanese | 1451 | — |

## Instruction Following

- GLM-5.2: 77.1 (#32)
- GPT-5 Nano: 75.2 (#72)

| Benchmark | GLM-5.2 | GPT-5 Nano |
|---|---|---|
| LMArena Instruction Following | 1465 | 1306 |
| IFEval | — | 93.2% |

## Long Context

- GLM-5.2: 45.3 (#43)
- GPT-5 Nano: 31.3 (#275)

| Benchmark | GLM-5.2 | GPT-5 Nano |
|---|---|---|
| LMArena Longer Query | 1479 | 1313 |
| Fiction.LiveBench | — | 44.4% |

## Writing & Preference

- GLM-5.2: 70.7 (#20)
- GPT-5 Nano: 39.4 (#244)

| Benchmark | GLM-5.2 | GPT-5 Nano |
|---|---|---|
| LMArena Text | 1470 | 1320 |
| LMArena Creative Writing | 1465 | 1250 |
| EQ-Bench Creative Writing | 1757 | 705 |
| LMArena Multi-Turn | 1468 | 1311 |
| WildBench | — | 80.6% |
| EQ-Bench 4 | 1222 | — |

## FAQ

### Is GLM-5.2 better than GPT-5 Nano?

GLM-5.2 is the stronger model overall, scoring 50.9 to 33.4 on the Noometry Index. GPT-5 Nano costs 16× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.

### Which is cheaper, GLM-5.2 or GPT-5 Nano?

GPT-5 Nano is cheaper. It lists at $0.05 per million input tokens and $0.40 per million output tokens; GLM-5.2 lists at $1.40 and $4.40.

### Is GLM-5.2 or GPT-5 Nano better for coding?

GLM-5.2 scores higher on coding benchmarks: 50.9 versus 33.3 in the Noometry coding category.

### Which has the bigger context window?

GLM-5.2 does, with 1M tokens against 400K.

### How many benchmarks do GLM-5.2 and GPT-5 Nano share?

32 benchmarks have published results for both models. GLM-5.2 has 50 scored results on Noometry and GPT-5 Nano has 48.