# Claude Opus 4.8 vs GLM-4.5V

> Claude Opus 4.8 is the stronger model overall, scoring 60.7 to 39.2 on the Noometry Index. GLM-4.5V costs 11× less per token, which makes it the better buy when Claude Opus 4.8's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/claude-opus-4-8-vs-glm-4-5v
- Last updated: 2026-10-10
- Shared benchmarks: 15

## Summary

- They share 15 benchmarks with published results for both. Claude Opus 4.8 scores higher in 9 categories and GLM-4.5V in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where Claude Opus 4.8 leads 78.2 to 34.7.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 88.8% for Claude Opus 4.8 and 59.8% for GLM-4.5V.
- GLM-4.5V is cheaper at $0.60 / $1.80 per million input/output tokens, against $5 / $25 for Claude Opus 4.8.
- Claude Opus 4.8 accepts more context: 1M tokens versus 64K.
- GLM-4.5V has downloadable open weights; the other is API-only.

## Snapshot

| | Claude Opus 4.8 | GLM-4.5V |
|---|---|---|
| Provider | Anthropic | Z.ai (Zhipu) |
| Noometry Index | 60.7 | 39.2 |
| Rank | 9 | 158 |
| Context | 1M | 64K |
| Input $/M | $5 | $0.60 |
| Output $/M | $25 | $1.80 |
| Weights | Proprietary | Open |

## Coding

- Claude Opus 4.8: 60.2 (#9)
- GLM-4.5V: 38.9 (#152)

| Benchmark | Claude Opus 4.8 | GLM-4.5V |
|---|---|---|
| LMArena Coding | 1491 | 1347 |
| DeepSWE | 59% | — |
| FrontierCode | 46.5% | — |
| LMArena WebDev | 1556 | — |
| SciCode | 53.5% | — |
| GSO | 47.1% | — |
| WeirdML | 82.9% | — |
| ALE-Bench | 1,564 | — |

## Agentic & Tool Use

- Claude Opus 4.8: 47.6 (#10)
- GLM-4.5V: —

| Benchmark | Claude Opus 4.8 | GLM-4.5V |
|---|---|---|
| APEX-Agents | 48.9% | — |
| OSWorld 2.0 | 20.6% | — |
| Remote Labor Index | 8.3% | — |
| τ²-bench Banking | 39.7% | — |
| DeepResearch Bench | 50.2% | — |
| PostTrainBench | 33.8% | — |
| GBAEval | 70.9% | — |
| GDP.pdf | 24% | — |
| LMArena Search | 1204 | — |
| Vending-Bench 2 | 5,787 | — |

## Reasoning

- Claude Opus 4.8: 64.6 (#14)
- GLM-4.5V: 26.5 (#118)

| Benchmark | Claude Opus 4.8 | GLM-4.5V |
|---|---|---|
| Kagi LLM Benchmark | 88.8% | 59.8% |
| LMArena Hard Prompts | 1481 | 1335 |
| ARC-AGI-2 | 72.1% | — |
| SimpleBench | 64.8% | — |
| NYT Connections (extended) | 91.1% | — |
| ARC-AGI-1 | 92.5% | — |
| CritPt | 20.9% | — |
| Chess Puzzles | 34% | — |
| EnigmaEval | 23.5% | — |
| EBR-Bench | 28.6% | — |
| Mystery Game Puzzles | 36% | — |
| DTBench | 94.9% | — |
| LMCA | 57.5% | — |
| Surface Evolver Bench | 87.5% | — |
| Bench to the Future 3 | 0.14 | — |
| Epoch Capabilities Index | 158.3 | — |
| ForecastBench | 59.9 | — |

## Math

- Claude Opus 4.8: 78.2 (#11)
- GLM-4.5V: 34.7 (#157)

| Benchmark | Claude Opus 4.8 | GLM-4.5V |
|---|---|---|
| LMArena Math | 1487 | 1353 |
| FrontierMath (Tiers 1-3) | 80% | — |
| FrontierMath Tier 4 | 56.1% | — |
| MathArena Final-Answer Competitions | 91.8% | — |
| OTIS Mock AIME 2024-2025 | 98.3% | — |
| ProofBench | 69% | — |
| FrontierMath (Feb 2025 set) | 47.2% | — |
| FrontierMath Tier 4 (v1) | 31.3% | — |

## Knowledge

- Claude Opus 4.8: 61.3 (#26)
- GLM-4.5V: 36.8 (#155)

| Benchmark | Claude Opus 4.8 | GLM-4.5V |
|---|---|---|
| LMArena Expert | 1501 | 1353 |
| GPQA Diamond | 91% | — |
| SimpleQA Verified | 53% | — |

## Multimodal

- Claude Opus 4.8: 43.2 (#20)
- GLM-4.5V: 33.6 (#86)

| Benchmark | Claude Opus 4.8 | GLM-4.5V |
|---|---|---|
| LMArena Vision | 1295 | 1154 |
| Blueprint-Bench 2 | 14.5% | — |
| Furniture Assembly | 42.5% | — |
| LMArena Document | 1475 | — |

## Multilingual

- Claude Opus 4.8: 55.4 (#31)
- GLM-4.5V: 44.9 (#172)

| Benchmark | Claude Opus 4.8 | GLM-4.5V |
|---|---|---|
| LMArena Non-English | 1450 | 1304 |
| LMArena Chinese | 1509 | 1338 |
| LMArena Russian | 1476 | 1299 |
| LMArena Spanish | 1470 | 1335 |
| LMArena French | 1482 | — |
| LMArena German | 1470 | — |
| LMArena Japanese | 1440 | — |
| LMArena Korean | 1432 | — |

## Instruction Following

- Claude Opus 4.8: 77.6 (#22)
- GLM-4.5V: 69.4 (#170)

| Benchmark | Claude Opus 4.8 | GLM-4.5V |
|---|---|---|
| LMArena Instruction Following | 1476 | 1311 |

## Long Context

- Claude Opus 4.8: 45.4 (#37)
- GLM-4.5V: 39.6 (#166)

| Benchmark | Claude Opus 4.8 | GLM-4.5V |
|---|---|---|
| LMArena Longer Query | 1482 | 1305 |

## Writing & Preference

- Claude Opus 4.8: 72.1 (#17)
- GLM-4.5V: 52.7 (#162)

| Benchmark | Claude Opus 4.8 | GLM-4.5V |
|---|---|---|
| LMArena Text | 1460 | 1333 |
| LMArena Creative Writing | 1453 | 1295 |
| LMArena Multi-Turn | 1476 | 1333 |
| EQ-Bench Creative Writing | 1840 | — |
| EQ-Bench 4 | 1281 | — |

## FAQ

### Is Claude Opus 4.8 better than GLM-4.5V?

Claude Opus 4.8 is the stronger model overall, scoring 60.7 to 39.2 on the Noometry Index. GLM-4.5V costs 11× less per token, which makes it the better buy when Claude Opus 4.8's lead doesn't matter for your workload.

### Which is cheaper, Claude Opus 4.8 or GLM-4.5V?

GLM-4.5V is cheaper. It lists at $0.60 per million input tokens and $1.80 per million output tokens; Claude Opus 4.8 lists at $5 and $25.

### Is Claude Opus 4.8 or GLM-4.5V better for coding?

Claude Opus 4.8 scores higher on coding benchmarks: 60.2 versus 38.9 in the Noometry coding category.

### Which has the bigger context window?

Claude Opus 4.8 does, with 1M tokens against 64K.

### How many benchmarks do Claude Opus 4.8 and GLM-4.5V share?

15 benchmarks have published results for both models. Claude Opus 4.8 has 65 scored results on Noometry and GLM-4.5V has 15.