# GLM-5.2 vs GPT-4.1 nano

> GLM-5.2 is the stronger model overall, scoring 51.1 to 27.9 on the Noometry Index. GPT-4.1 nano costs 12× 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-4-1-nano
- Last updated: 2026-10-12
- Shared benchmarks: 27

## Summary

- They share 27 benchmarks with published results for both. GLM-5.2 scores higher in 9 categories and GPT-4.1 nano in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-5.2 leads 57.1 to 21.8.
- The biggest single-benchmark swing is ARC-AGI-1: 77% for GLM-5.2 and 0% for GPT-4.1 nano.
- GPT-4.1 nano is cheaper at $0.10 / $0.40 per million input/output tokens, against $1.40 / $4.40 for GLM-5.2.
- GPT-4.1 nano accepts more context: 1.05M tokens versus 1M.
- GLM-5.2 has downloadable open weights; the other is API-only.

## Snapshot

| | GLM-5.2 | GPT-4.1 nano |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 51.1 | 27.9 |
| Rank | 44 | 327 |
| Context | 1M | 1.05M |
| Input $/M | $1.40 | $0.10 |
| Output $/M | $4.40 | $0.40 |
| Weights | Open | Proprietary |

## Coding

- GLM-5.2: 51.3 (#41)
- GPT-4.1 nano: 24.1 (#330)

| Benchmark | GLM-5.2 | GPT-4.1 nano |
|---|---|---|
| SciCode | 50.5% | 25.9% |
| WeirdML | 70.1% | 19% |
| LMArena Coding | 1485 | 1306 |
| SWE-bench Verified | 78.7% | — |
| DeepSWE | 43.8% | — |
| FrontierCode | 24.5% | — |
| Aider Polyglot | — | 8.9% |
| LMArena WebDev | 1603 | — |
| ALE-Bench | 1,047 | — |

## Agentic & Tool Use

- GLM-5.2: 32.4 (#63)
- GPT-4.1 nano: 26.5 (#104)

| Benchmark | GLM-5.2 | GPT-4.1 nano |
|---|---|---|
| APEX-Agents | 45.2% | — |
| Berkeley Function Calling Leaderboard | — | 33% |
| τ²-bench Banking | 37.1% | — |
| PostTrainBench | 31.7% | — |
| GBAEval | 0% | — |
| Vending-Bench 2 | 8,314 | — |

## Reasoning

- GLM-5.2: 42.3 (#52)
- GPT-4.1 nano: 8.5 (#349)

| Benchmark | GLM-5.2 | GPT-4.1 nano |
|---|---|---|
| ARC-AGI-2 | 22.8% | 0% |
| Kagi LLM Benchmark | 62.6% | 33.3% |
| ARC-AGI-1 | 77% | 0% |
| CritPt | 20.9% | 0% |
| LMArena Hard Prompts | 1480 | 1286 |
| DTBench | 93.6% | 52.5% |
| LMCA | 45.8% | 5.5% |
| Epoch Capabilities Index | 151.78 | 129.62 |
| SimpleBench | 58.8% | — |
| NYT Connections (extended) | 74.3% | — |
| Chess Puzzles | 21% | — |
| EBR-Bench | 9.5% | — |
| Mystery Game Puzzles | 19% | — |
| Surface Evolver Bench | 55.6% | — |

## Math

- GLM-5.2: 55.7 (#43)
- GPT-4.1 nano: 26.9 (#252)

| Benchmark | GLM-5.2 | GPT-4.1 nano |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 86.4% | 28.9% |
| LMArena Math | 1482 | 1274 |
| FrontierMath (Tiers 1-3) | 59.2% | — |
| FrontierMath Tier 4 | 29.3% | — |
| MathArena Final-Answer Competitions | 67.6% | — |
| ProofBench | 35% | — |
| Omni-MATH | — | 36.7% |
| MATH Level 5 | — | 70% |
| FrontierMath (Feb 2025 set) | — | 1% |

## Knowledge

- GLM-5.2: 57.1 (#40)
- GPT-4.1 nano: 21.8 (#273)

| Benchmark | GLM-5.2 | GPT-4.1 nano |
|---|---|---|
| GPQA Diamond | 91.9% | 48.9% |
| SimpleQA Verified | 34.2% | 6% |
| LMArena Expert | 1486 | 1272 |
| MMLU-Pro | — | 55% |
| GPQA (HELM) | — | 50.7% |

## Multimodal

- GLM-5.2: —
- GPT-4.1 nano: 29.2 (#113)

| Benchmark | GLM-5.2 | GPT-4.1 nano |
|---|---|---|
| LMArena Vision | — | 1063 |

## Multilingual

- GLM-5.2: 55.8 (#26)
- GPT-4.1 nano: 41.6 (#205)

| Benchmark | GLM-5.2 | GPT-4.1 nano |
|---|---|---|
| LMArena Non-English | 1459 | 1260 |
| LMArena Chinese | 1519 | 1270 |
| LMArena German | 1468 | 1288 |
| LMArena Japanese | 1451 | 1198 |
| LMArena Russian | 1466 | 1261 |
| LMArena French | 1479 | — |
| LMArena Korean | 1445 | — |
| LMArena Spanish | 1477 | — |

## Instruction Following

- GLM-5.2: 76.9 (#34)
- GPT-4.1 nano: 67.8 (#193)

| Benchmark | GLM-5.2 | GPT-4.1 nano |
|---|---|---|
| LMArena Instruction Following | 1465 | 1267 |
| IFEval | — | 84.3% |

## Long Context

- GLM-5.2: 45.3 (#43)
- GPT-4.1 nano: 23.7 (#296)

| Benchmark | GLM-5.2 | GPT-4.1 nano |
|---|---|---|
| LMArena Longer Query | 1479 | 1283 |
| Fiction.LiveBench | — | 25% |

## Writing & Preference

- GLM-5.2: 70.4 (#21)
- GPT-4.1 nano: 40.5 (#243)

| Benchmark | GLM-5.2 | GPT-4.1 nano |
|---|---|---|
| LMArena Text | 1470 | 1285 |
| LMArena Creative Writing | 1462 | 1260 |
| EQ-Bench Creative Writing | 1757 | 946 |
| LMArena Multi-Turn | 1469 | 1277 |
| WildBench | — | 81.2% |
| EQ-Bench 4 | 1222 | — |

## FAQ

### Is GLM-5.2 better than GPT-4.1 nano?

GLM-5.2 is the stronger model overall, scoring 51.1 to 27.9 on the Noometry Index. GPT-4.1 nano costs 12× 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-4.1 nano?

GPT-4.1 nano is cheaper. It lists at $0.10 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-4.1 nano better for coding?

GLM-5.2 scores higher on coding benchmarks: 51.3 versus 24.1 in the Noometry coding category.

### Which has the bigger context window?

GPT-4.1 nano does, with 1.05M tokens against 1M.

### How many benchmarks do GLM-5.2 and GPT-4.1 nano share?

27 benchmarks have published results for both models. GLM-5.2 has 51 scored results on Noometry and GPT-4.1 nano has 38.