# Claude Sonnet 4.5 vs GLM-5.3

> GLM-5.3 is the stronger model overall, scoring 54.8 to 44.1 on the Noometry Index.

- Canonical page: https://noometry.com/compare/claude-sonnet-4-5-vs-glm-5-3
- Last updated: 2026-10-10
- Shared benchmarks: 36

## Summary

- They share 36 benchmarks with published results for both. Claude Sonnet 4.5 scores higher in 1 category and GLM-5.3 in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.3 leads 62.3 to 32.3.
- The biggest single-benchmark swing is FrontierMath (Tiers 1-3): 23.9% for Claude Sonnet 4.5 and 68.8% for GLM-5.3.
- GLM-5.3 is cheaper at $1.40 / $4.40 per million input/output tokens, against $3 / $15 for Claude Sonnet 4.5.
- GLM-5.3 accepts more context: 1M tokens versus 200K.
- GLM-5.3 has downloadable open weights; the other is API-only.

## Snapshot

| | Claude Sonnet 4.5 | GLM-5.3 |
|---|---|---|
| Provider | Anthropic | Z.ai (Zhipu) |
| Noometry Index | 44.1 | 54.8 |
| Rank | 81 | 26 |
| Context | 200K | 1M |
| Input $/M | $3 | $1.40 |
| Output $/M | $15 | $4.40 |
| Weights | Proprietary | Open |

## Coding

- Claude Sonnet 4.5: 47.3 (#61)
- GLM-5.3: 59.5 (#14)

| Benchmark | Claude Sonnet 4.5 | GLM-5.3 |
|---|---|---|
| LMArena WebDev | 1393 | 1622 |
| SciCode | 44.7% | 59% |
| WeirdML | 47.7% | 75.4% |
| LMArena Coding | 1489 | 1496 |
| ALE-Bench | 796.15 | 1,317 |
| SWE-bench Verified | 71.3% | — |
| DeepSWE | — | 69% |
| FrontierCode | — | 40.1% |
| SWE-bench Verified (bash only) | 71.4% | — |
| CursorBench | — | 42.6% |
| SWE-bench Multilingual | 67% | — |
| FrontierSWE | — | 30.2% |
| GSO | 14.7% | — |
| AlgoTune | 1.52 | — |

## Agentic & Tool Use

- Claude Sonnet 4.5: 38.3 (#32)
- GLM-5.3: 36.4 (#38)

| Benchmark | Claude Sonnet 4.5 | GLM-5.3 |
|---|---|---|
| Vending-Bench 2 | 3,839 | 8,164 |
| Terminal-Bench | 46.5% | — |
| APEX-Agents | — | 56.6% |
| Berkeley Function Calling Leaderboard | 73.2% | — |
| GDPval | 42.5% | — |
| Remote Labor Index | 2.1% | — |
| τ²-bench Airline | 72% | — |
| τ²-bench Banking | 25.3% | — |
| τ²-bench Retail | 72.4% | — |
| τ²-bench Telecom | 84.9% | — |
| Cybench | 60% | — |
| DeepResearch Bench | 52.6% | — |
| OSWorld | 62.9% | — |
| LMArena Search | 1159 | — |
| METR Time Horizons | 67.4% | — |

## Reasoning

- Claude Sonnet 4.5: 26.9 (#125)
- GLM-5.3: 46.1 (#46)

| Benchmark | Claude Sonnet 4.5 | GLM-5.3 |
|---|---|---|
| NYT Connections (extended) | 37.3% | 74.2% |
| CritPt | 1.1% | 19.1% |
| Chess Puzzles | 12% | 21% |
| LMArena Hard Prompts | 1462 | 1489 |
| Mystery Game Puzzles | 17% | 33% |
| DTBench | 83.2% | 87.7% |
| LMCA | 38.8% | 55.5% |
| Epoch Capabilities Index | 146.84 | 155.61 |
| ARC-AGI-2 | 13.6% | — |
| SimpleBench | 54.3% | — |
| Kagi LLM Benchmark | 57.9% | — |
| ARC-AGI-1 | 63.7% | — |
| EnigmaEval | 6% | — |
| EBR-Bench | 2.4% | — |
| Bench to the Future 3 | — | 0.15 |
| ForecastBench | 61.9 | — |

## Math

- Claude Sonnet 4.5: 32.3 (#216)
- GLM-5.3: 62.3 (#33)

| Benchmark | Claude Sonnet 4.5 | GLM-5.3 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 23.9% | 68.8% |
| FrontierMath Tier 4 | 2.4% | 29.3% |
| OTIS Mock AIME 2024-2025 | 77.8% | 91.1% |
| ProofBench | 19% | 49% |
| LMArena Math | 1449 | 1489 |
| Omni-MATH | 55.3% | — |
| MATH Level 5 | 97.7% | — |
| FrontierMath (Feb 2025 set) | 15.2% | — |
| FrontierMath Tier 4 (v1) | 4.2% | — |

## Knowledge

- Claude Sonnet 4.5: 48.4 (#76)
- GLM-5.3: 58.3 (#37)

| Benchmark | Claude Sonnet 4.5 | GLM-5.3 |
|---|---|---|
| GPQA Diamond | 82.3% | 90.9% |
| SimpleQA Verified | 30.7% | 41% |
| LMArena Expert | 1482 | 1516 |
| Humanity's Last Exam | 13.7% | — |
| MMLU-Pro | 86.9% | — |
| Vectara Hallucination Rate | 12% | — |
| GPQA (HELM) | 68.6% | — |

## Multimodal

- Claude Sonnet 4.5: 34.8 (#89)
- GLM-5.3: —

| Benchmark | Claude Sonnet 4.5 | GLM-5.3 |
|---|---|---|
| VPCT | 39.8% | — |
| LMArena Document | 1450 | — |

## Multilingual

- Claude Sonnet 4.5: 53.4 (#69)
- GLM-5.3: 55.7 (#28)

| Benchmark | Claude Sonnet 4.5 | GLM-5.3 |
|---|---|---|
| LMArena Non-English | 1425 | 1457 |
| LMArena Chinese | 1459 | 1528 |
| LMArena French | 1458 | 1499 |
| LMArena German | 1427 | 1499 |
| LMArena Japanese | 1390 | 1453 |
| LMArena Korean | 1403 | 1472 |
| LMArena Russian | 1437 | 1463 |
| LMArena Spanish | 1457 | 1460 |

## Instruction Following

- Claude Sonnet 4.5: 75.0 (#78)
- GLM-5.3: 77.5 (#23)

| Benchmark | Claude Sonnet 4.5 | GLM-5.3 |
|---|---|---|
| LMArena Instruction Following | 1459 | 1477 |
| IFEval | 85% | — |

## Long Context

- Claude Sonnet 4.5: 45.2 (#46)
- GLM-5.3: 45.4 (#41)

| Benchmark | Claude Sonnet 4.5 | GLM-5.3 |
|---|---|---|
| LMArena Longer Query | 1476 | 1482 |

## Writing & Preference

- Claude Sonnet 4.5: 66.5 (#34)
- GLM-5.3: 75.7 (#6)

| Benchmark | Claude Sonnet 4.5 | GLM-5.3 |
|---|---|---|
| LMArena Text | 1439 | 1471 |
| LMArena Creative Writing | 1442 | 1457 |
| EQ-Bench Creative Writing | 1678 | 2075 |
| LMArena Multi-Turn | 1465 | 1472 |
| WildBench | 85.4% | — |

## FAQ

### Is Claude Sonnet 4.5 better than GLM-5.3?

GLM-5.3 is the stronger model overall, scoring 54.8 to 44.1 on the Noometry Index.

### Which is cheaper, Claude Sonnet 4.5 or GLM-5.3?

GLM-5.3 is cheaper. It lists at $1.40 per million input tokens and $4.40 per million output tokens; Claude Sonnet 4.5 lists at $3 and $15.

### Is Claude Sonnet 4.5 or GLM-5.3 better for coding?

GLM-5.3 scores higher on coding benchmarks: 59.5 versus 47.3 in the Noometry coding category.

### Which has the bigger context window?

GLM-5.3 does, with 1M tokens against 200K.

### How many benchmarks do Claude Sonnet 4.5 and GLM-5.3 share?

36 benchmarks have published results for both models. Claude Sonnet 4.5 has 73 scored results on Noometry and GLM-5.3 has 42.