# Claude Sonnet 4.6 vs GLM-4.7

> Claude Sonnet 4.6 is the stronger model overall, scoring 50.1 to 41.7 on the Noometry Index. GLM-4.7 costs 6.0× less per token, which makes it the better buy when Claude Sonnet 4.6's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/claude-sonnet-4-6-vs-glm-4-7
- Last updated: 2026-10-10
- Shared benchmarks: 33

## Summary

- They share 33 benchmarks with published results for both. Claude Sonnet 4.6 scores higher in 9 categories and GLM-4.7 in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Claude Sonnet 4.6 leads 46.1 to 23.7.
- The biggest single-benchmark swing is ProofBench: 45% for Claude Sonnet 4.6 and 6% for GLM-4.7.
- GLM-4.7 is cheaper at $0.60 / $2.20 per million input/output tokens, against $3 / $15 for Claude Sonnet 4.6.
- Claude Sonnet 4.6 accepts more context: 1M tokens versus 205K.
- GLM-4.7 has downloadable open weights; the other is API-only.

## Snapshot

| | Claude Sonnet 4.6 | GLM-4.7 |
|---|---|---|
| Provider | Anthropic | Z.ai (Zhipu) |
| Noometry Index | 50.1 | 41.7 |
| Rank | 42 | 117 |
| Context | 1M | 205K |
| Input $/M | $3 | $0.60 |
| Output $/M | $15 | $2.20 |
| Weights | Proprietary | Open |

## Coding

- Claude Sonnet 4.6: 46.0 (#62)
- GLM-4.7: 43.6 (#74)

| Benchmark | Claude Sonnet 4.6 | GLM-4.7 |
|---|---|---|
| LMArena WebDev | 1521 | 1435 |
| SciCode | 46.8% | 45.1% |
| LMArena Coding | 1504 | 1454 |
| ALE-Bench | 1,327 | 399.48 |
| SWE-bench Verified | 75.2% | — |
| DeepSWE | 29.9% | — |
| FrontierCode | 24.3% | — |
| WeirdML | 66.1% | — |

## Agentic & Tool Use

- Claude Sonnet 4.6: 39.1 (#26)
- GLM-4.7: 26.4 (#96)

| Benchmark | Claude Sonnet 4.6 | GLM-4.7 |
|---|---|---|
| Terminal-Bench | 53.4% | 33.4% |
| Vending-Bench 2 | 7,204 | 2,377 |
| APEX-Agents | 43% | — |
| OSWorld 2.0 | 9.3% | — |
| DeepResearch Bench | 54.9% | — |
| OSWorld | 72.1% | — |
| ExploitBench | 23.6% | — |
| GBAEval | 48.8% | — |
| GDP.pdf | 18% | — |
| LMArena Search | 1221 | — |

## Reasoning

- Claude Sonnet 4.6: 46.1 (#40)
- GLM-4.7: 23.7 (#161)

| Benchmark | Claude Sonnet 4.6 | GLM-4.7 |
|---|---|---|
| CritPt | 3.1% | 1.7% |
| Chess Puzzles | 13% | 6% |
| LMArena Hard Prompts | 1485 | 1443 |
| Epoch Capabilities Index | 152.25 | 143.53 |
| ARC-AGI-2 | 60.4% | — |
| SimpleBench | — | 47.7% |
| NYT Connections (extended) | 80.9% | — |
| ARC-AGI-1 | 86.5% | — |
| Thematic Generalization | 76.3% | — |
| Mystery Game Puzzles | 16% | — |
| DTBench | 89.9% | — |
| LMCA | 46.5% | — |
| ForecastBench | 62 | — |

## Math

- Claude Sonnet 4.6: 51.9 (#41)
- GLM-4.7: 37.7 (#92)

| Benchmark | Claude Sonnet 4.6 | GLM-4.7 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 85.8% | 83.3% |
| ProofBench | 45% | 6% |
| LMArena Math | 1463 | 1423 |
| FrontierMath (Feb 2025 set) | 32.4% | 2.4% |
| FrontierMath Tier 4 (v1) | 8.3% | 0% |

## Knowledge

- Claude Sonnet 4.6: 51.6 (#60)
- GLM-4.7: 47.0 (#74)

| Benchmark | Claude Sonnet 4.6 | GLM-4.7 |
|---|---|---|
| GPQA Diamond | 87.4% | 83.3% |
| SimpleQA Verified | 35.5% | 32.2% |
| Vectara Hallucination Rate | 10.6% | 11.7% |
| LMArena Expert | 1500 | 1424 |

## Multimodal

- Claude Sonnet 4.6: 38.0 (#61)
- GLM-4.7: —

| Benchmark | Claude Sonnet 4.6 | GLM-4.7 |
|---|---|---|
| LMArena Vision | 1283 | — |
| Blueprint-Bench 2 | 6.7% | — |
| LMArena Document | 1482 | — |

## Multilingual

- Claude Sonnet 4.6: 54.6 (#39)
- GLM-4.7: 53.0 (#75)

| Benchmark | Claude Sonnet 4.6 | GLM-4.7 |
|---|---|---|
| LMArena Non-English | 1440 | 1417 |
| LMArena Chinese | 1491 | 1495 |
| LMArena French | 1465 | 1432 |
| LMArena German | 1428 | 1426 |
| LMArena Japanese | 1419 | 1439 |
| LMArena Korean | 1411 | 1401 |
| LMArena Russian | 1440 | 1423 |
| LMArena Spanish | 1465 | 1433 |

## Instruction Following

- Claude Sonnet 4.6: 77.6 (#23)
- GLM-4.7: 74.6 (#90)

| Benchmark | Claude Sonnet 4.6 | GLM-4.7 |
|---|---|---|
| LMArena Instruction Following | 1476 | 1411 |

## Long Context

- Claude Sonnet 4.6: 45.3 (#40)
- GLM-4.7: 42.8 (#111)

| Benchmark | Claude Sonnet 4.6 | GLM-4.7 |
|---|---|---|
| LMArena Longer Query | 1479 | 1432 |
| CL-bench | — | 15.9% |
| CL-bench Life | — | 10.9% |

## Writing & Preference

- Claude Sonnet 4.6: 70.4 (#22)
- GLM-4.7: 61.1 (#89)

| Benchmark | Claude Sonnet 4.6 | GLM-4.7 |
|---|---|---|
| LMArena Text | 1458 | 1436 |
| LMArena Creative Writing | 1435 | 1401 |
| EQ-Bench Creative Writing | 1810 | 1413 |
| LMArena Multi-Turn | 1464 | 1446 |
| EQ-Bench 4 | 1207 | — |

## FAQ

### Is Claude Sonnet 4.6 better than GLM-4.7?

Claude Sonnet 4.6 is the stronger model overall, scoring 50.1 to 41.7 on the Noometry Index. GLM-4.7 costs 6.0× less per token, which makes it the better buy when Claude Sonnet 4.6's lead doesn't matter for your workload.

### Which is cheaper, Claude Sonnet 4.6 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 Sonnet 4.6 lists at $3 and $15.

### Is Claude Sonnet 4.6 or GLM-4.7 better for coding?

Claude Sonnet 4.6 scores higher on coding benchmarks: 46.0 versus 43.6 in the Noometry coding category.

### Which has the bigger context window?

Claude Sonnet 4.6 does, with 1M tokens against 205K.

### How many benchmarks do Claude Sonnet 4.6 and GLM-4.7 share?

33 benchmarks have published results for both models. Claude Sonnet 4.6 has 57 scored results on Noometry and GLM-4.7 has 36.