# GLM-4.7 vs o1

> GLM-4.7 is the stronger model overall, scoring 41.7 to 40.6 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-4-7-vs-o1
- Last updated: 2026-10-10
- Shared benchmarks: 24

## Summary

- They share 24 benchmarks with published results for both. GLM-4.7 scores higher in 5 categories and o1 in 4 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in long context, where o1 leads 50.3 to 42.8.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 83.3% for GLM-4.7 and 73.3% for o1.
- GLM-4.7 is cheaper at $0.60 / $2.20 per million input/output tokens, against $15 / $60 for o1.
- GLM-4.7 accepts more context: 205K tokens versus 200K.
- GLM-4.7 has downloadable open weights; the other is API-only.

## Snapshot

| | GLM-4.7 | o1 |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 41.7 | 40.6 |
| Rank | 117 | 140 |
| Context | 205K | 200K |
| Input $/M | $0.60 | $15 |
| Output $/M | $2.20 | $60 |
| Weights | Open | Proprietary |

## Coding

- GLM-4.7: 43.6 (#74)
- o1: 45.7 (#64)

| Benchmark | GLM-4.7 | o1 |
|---|---|---|
| LMArena Coding | 1454 | 1367 |
| Aider Polyglot | — | 61.7% |
| LMArena WebDev | 1435 | — |
| SciCode | 45.1% | — |
| WeirdML | — | 47.6% |
| LiveBench Coding | — | 69.7% |
| CadEval | — | 56% |
| ALE-Bench | 399.48 | — |
| HumanEval+ | — | 89% |
| MBPP+ | — | 80.2% |

## Agentic & Tool Use

- GLM-4.7: 26.4 (#96)
- o1: 24.3 (#111)

| Benchmark | GLM-4.7 | o1 |
|---|---|---|
| Terminal-Bench | 33.4% | — |
| Cybench | — | 10% |
| METR Time Horizons | — | 51.1% |
| Vending-Bench 2 | 2,377 | — |

## Reasoning

- GLM-4.7: 23.7 (#161)
- o1: 27.5 (#103)

| Benchmark | GLM-4.7 | o1 |
|---|---|---|
| SimpleBench | 47.7% | 41.7% |
| Chess Puzzles | 6% | 15% |
| LMArena Hard Prompts | 1443 | 1372 |
| Epoch Capabilities Index | 143.53 | 141.92 |
| ARC-AGI-1 | — | 30.7% |
| CritPt | 1.7% | — |
| EnigmaEval | — | 5.7% |
| LiveBench Reasoning | — | 91.6% |
| DTBench | — | 74.7% |
| LiveBench Data Analysis | — | 65.5% |
| LMCA | — | 22.3% |
| LiveBench | — | 75.7% |

## Math

- GLM-4.7: 37.7 (#92)
- o1: 34.9 (#150)

| Benchmark | GLM-4.7 | o1 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 83.3% | 73.3% |
| LMArena Math | 1423 | 1388 |
| FrontierMath (Feb 2025 set) | 2.4% | 9.3% |
| FrontierMath (Tiers 1-3) | — | 14.7% |
| ProofBench | 6% | — |
| LiveBench Math | — | 80.3% |
| MATH Level 5 | — | 94.7% |
| FrontierMath Tier 4 (v1) | 0% | — |

## Knowledge

- GLM-4.7: 47.0 (#74)
- o1: 41.5 (#101)

| Benchmark | GLM-4.7 | o1 |
|---|---|---|
| GPQA Diamond | 83.3% | 76.8% |
| SimpleQA Verified | 32.2% | 41.1% |
| LMArena Expert | 1424 | 1362 |
| Humanity's Last Exam | — | 8% |
| Confabulations | — | 11.7% |
| Vectara Hallucination Rate | 11.7% | — |

## Multimodal

- GLM-4.7: —
- o1: 33.6 (#87)

| Benchmark | GLM-4.7 | o1 |
|---|---|---|
| LMArena Vision | — | 1168 |
| GeoBench | — | 80% |
| VPCT | — | 37% |
| SpatialViz-Bench | — | 41.4% |

## Multilingual

- GLM-4.7: 53.0 (#75)
- o1: 48.8 (#137)

| Benchmark | GLM-4.7 | o1 |
|---|---|---|
| LMArena Non-English | 1417 | 1359 |
| LMArena Chinese | 1495 | 1394 |
| LMArena French | 1432 | 1343 |
| LMArena German | 1426 | 1337 |
| LMArena Japanese | 1439 | 1346 |
| LMArena Korean | 1401 | 1396 |
| LMArena Russian | 1423 | 1356 |
| LMArena Spanish | 1433 | 1345 |

## Instruction Following

- GLM-4.7: 74.6 (#90)
- o1: 75.0 (#82)

| Benchmark | GLM-4.7 | o1 |
|---|---|---|
| LMArena Instruction Following | 1411 | 1368 |
| LiveBench Instruction Following | — | 81.5% |

## Long Context

- GLM-4.7: 42.8 (#111)
- o1: 50.3 (#9)

| Benchmark | GLM-4.7 | o1 |
|---|---|---|
| LMArena Longer Query | 1432 | 1378 |
| Fiction.LiveBench | — | 83.3% |
| CL-bench | 15.9% | — |
| CL-bench Life | 10.9% | — |

## Writing & Preference

- GLM-4.7: 61.1 (#89)
- o1: 55.8 (#139)

| Benchmark | GLM-4.7 | o1 |
|---|---|---|
| LMArena Text | 1436 | 1366 |
| LMArena Creative Writing | 1401 | 1347 |
| LMArena Multi-Turn | 1446 | 1368 |
| Short-Story Creative Writing | — | 70.2% |
| EQ-Bench Creative Writing | 1413 | — |
| LiveBench Language | — | 65.4% |

## FAQ

### Is GLM-4.7 better than o1?

GLM-4.7 is the stronger model overall, scoring 41.7 to 40.6 on the Noometry Index.

### Which is cheaper, GLM-4.7 or o1?

GLM-4.7 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; o1 lists at $15 and $60.

### Is GLM-4.7 or o1 better for coding?

o1 scores higher on coding benchmarks: 45.7 versus 43.6 in the Noometry coding category.

### Which has the bigger context window?

GLM-4.7 does, with 205K tokens against 200K.

### How many benchmarks do GLM-4.7 and o1 share?

24 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and o1 has 52.