# Claude Opus 4.5 vs GLM-4.7

> Claude Opus 4.5 is the stronger model overall, scoring 50.5 to 41.7 on the Noometry Index. GLM-4.7 costs 10× less per token, which makes it the better buy when Claude Opus 4.5's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/claude-opus-4-5-vs-glm-4-7
- Last updated: 2026-10-10
- Shared benchmarks: 33

## Summary

- They share 33 benchmarks with published results for both. Claude Opus 4.5 scores higher in 9 categories and GLM-4.7 in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in agentic & tool use, where Claude Opus 4.5 leads 47.2 to 26.4.
- The biggest single-benchmark swing is ProofBench: 36% for Claude Opus 4.5 and 6% for GLM-4.7.
- GLM-4.7 is cheaper at $0.60 / $2.20 per million input/output tokens, against $5 / $25 for Claude Opus 4.5.
- GLM-4.7 accepts more context: 205K tokens versus 200K.
- GLM-4.7 has downloadable open weights; the other is API-only.

## Snapshot

| | Claude Opus 4.5 | GLM-4.7 |
|---|---|---|
| Provider | Anthropic | Z.ai (Zhipu) |
| Noometry Index | 50.5 | 41.7 |
| Rank | 39 | 117 |
| Context | 200K | 205K |
| Input $/M | $5 | $0.60 |
| Output $/M | $25 | $2.20 |
| Weights | Proprietary | Open |

## Coding

- Claude Opus 4.5: 55.4 (#21)
- GLM-4.7: 43.6 (#74)

| Benchmark | Claude Opus 4.5 | GLM-4.7 |
|---|---|---|
| LMArena WebDev | 1493 | 1435 |
| LMArena Coding | 1504 | 1454 |
| ALE-Bench | 1,025 | 399.48 |
| SWE-bench Verified | 76.7% | — |
| SWE-bench Verified (bash only) | 76.8% | — |
| SWE-bench Multilingual | 70.7% | — |
| SciCode | — | 45.1% |
| GSO | 26.5% | — |
| WeirdML | 63.7% | — |
| AlgoTune | 1.77 | — |

## Agentic & Tool Use

- Claude Opus 4.5: 47.2 (#11)
- GLM-4.7: 26.4 (#96)

| Benchmark | Claude Opus 4.5 | GLM-4.7 |
|---|---|---|
| Terminal-Bench | 63.1% | 33.4% |
| Vending-Bench 2 | 4,967 | 2,377 |
| Berkeley Function Calling Leaderboard | 77.5% | — |
| GDPval | 45.5% | — |
| Remote Labor Index | 3.8% | — |
| τ²-bench Airline | 84% | — |
| τ²-bench Banking | 24.7% | — |
| τ²-bench Retail | 79.6% | — |
| τ²-bench Telecom | 92.3% | — |
| Cybench | 82% | — |
| DeepResearch Bench | 54.8% | — |
| OSWorld | 66.3% | — |
| BALROG | 43.5% | — |
| LMArena Search | 1180 | — |
| METR Time Horizons | 75% | — |

## Reasoning

- Claude Opus 4.5: 42.5 (#43)
- GLM-4.7: 23.7 (#161)

| Benchmark | Claude Opus 4.5 | GLM-4.7 |
|---|---|---|
| SimpleBench | 62% | 47.7% |
| Chess Puzzles | 12% | 6% |
| LMArena Hard Prompts | 1476 | 1443 |
| Epoch Capabilities Index | 150.1 | 143.53 |
| ARC-AGI-2 | 37.6% | — |
| Kagi LLM Benchmark | 80.2% | — |
| NYT Connections (extended) | 52.5% | — |
| ARC-AGI-1 | 80% | — |
| CritPt | — | 1.7% |
| EnigmaEval | 11.9% | — |
| EBR-Bench | 14.3% | — |
| Mystery Game Puzzles | 22% | — |
| DTBench | 89.9% | — |
| LMCA | 44.5% | — |
| ForecastBench | 60.7 | — |

## Math

- Claude Opus 4.5: 38.5 (#85)
- GLM-4.7: 37.7 (#92)

| Benchmark | Claude Opus 4.5 | GLM-4.7 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 86.1% | 83.3% |
| ProofBench | 36% | 6% |
| LMArena Math | 1463 | 1423 |
| FrontierMath (Feb 2025 set) | 20.7% | 2.4% |
| FrontierMath Tier 4 (v1) | 4.2% | 0% |
| FrontierMath (Tiers 1-3) | 34.4% | — |
| FrontierMath Tier 4 | 4.9% | — |

## Knowledge

- Claude Opus 4.5: 56.4 (#38)
- GLM-4.7: 47.0 (#74)

| Benchmark | Claude Opus 4.5 | GLM-4.7 |
|---|---|---|
| GPQA Diamond | 86% | 83.3% |
| SimpleQA Verified | 45.7% | 32.2% |
| Vectara Hallucination Rate | 10.9% | 11.7% |
| LMArena Expert | 1488 | 1424 |
| Humanity's Last Exam | 25.2% | — |

## Multimodal

- Claude Opus 4.5: 31.1 (#99)
- GLM-4.7: —

| Benchmark | Claude Opus 4.5 | GLM-4.7 |
|---|---|---|
| GeoBench | 75% | — |
| VPCT | 40% | — |
| Furniture Assembly | 28.3% | — |
| LMArena Document | 1462 | — |

## Multilingual

- Claude Opus 4.5: 54.5 (#45)
- GLM-4.7: 53.0 (#75)

| Benchmark | Claude Opus 4.5 | GLM-4.7 |
|---|---|---|
| LMArena Non-English | 1438 | 1417 |
| LMArena Chinese | 1470 | 1495 |
| LMArena French | 1471 | 1432 |
| LMArena German | 1449 | 1426 |
| LMArena Japanese | 1415 | 1439 |
| LMArena Korean | 1424 | 1401 |
| LMArena Russian | 1448 | 1423 |
| LMArena Spanish | 1455 | 1433 |

## Instruction Following

- Claude Opus 4.5: 77.7 (#20)
- GLM-4.7: 74.6 (#90)

| Benchmark | Claude Opus 4.5 | GLM-4.7 |
|---|---|---|
| LMArena Instruction Following | 1478 | 1411 |

## Long Context

- Claude Opus 4.5: 46.4 (#21)
- GLM-4.7: 42.8 (#111)

| Benchmark | Claude Opus 4.5 | GLM-4.7 |
|---|---|---|
| CL-bench | 21.1% | 15.9% |
| LMArena Longer Query | 1480 | 1432 |
| CL-bench Life | — | 10.9% |

## Writing & Preference

- Claude Opus 4.5: 68.2 (#28)
- GLM-4.7: 61.1 (#89)

| Benchmark | Claude Opus 4.5 | GLM-4.7 |
|---|---|---|
| LMArena Text | 1451 | 1436 |
| LMArena Creative Writing | 1445 | 1401 |
| EQ-Bench Creative Writing | 1687 | 1413 |
| LMArena Multi-Turn | 1466 | 1446 |

## FAQ

### Is Claude Opus 4.5 better than GLM-4.7?

Claude Opus 4.5 is the stronger model overall, scoring 50.5 to 41.7 on the Noometry Index. GLM-4.7 costs 10× less per token, which makes it the better buy when Claude Opus 4.5's lead doesn't matter for your workload.

### Which is cheaper, Claude Opus 4.5 or GLM-4.7?

GLM-4.7 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; Claude Opus 4.5 lists at $5 and $25.

### Is Claude Opus 4.5 or GLM-4.7 better for coding?

Claude Opus 4.5 scores higher on coding benchmarks: 55.4 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 Claude Opus 4.5 and GLM-4.7 share?

33 benchmarks have published results for both models. Claude Opus 4.5 has 69 scored results on Noometry and GLM-4.7 has 36.