# GLM-5V-Turbo vs GPT-6 Sol

> GPT-6 Sol is the stronger model overall, scoring 49.2 to 43.1 on the Noometry Index. GLM-5V-Turbo costs 2.1× less per token, which makes it the better buy when GPT-6 Sol's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/glm-5v-turbo-vs-gpt-6-sol
- Last updated: 2026-10-10
- Shared benchmarks: 14

## Summary

- They share 14 benchmarks with published results for both. GLM-5V-Turbo scores higher in 4 categories and GPT-6 Sol in 5 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-6 Sol leads 49.3 to 28.5.
- GLM-5V-Turbo is cheaper at $1.20 / $4 per million input/output tokens, against $2 / $10 for GPT-6 Sol.
- GPT-6 Sol accepts more context: 1.05M tokens versus 200K.

## Snapshot

| | GLM-5V-Turbo | GPT-6 Sol |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 43.1 | 49.2 |
| Rank | 86 | 46 |
| Context | 200K | 1.05M |
| Input $/M | $1.20 | $2 |
| Output $/M | $4 | $10 |
| Weights | Proprietary | Proprietary |

## Coding

- GLM-5V-Turbo: 41.8 (#105)
- GPT-6 Sol: 59.6 (#11)

| Benchmark | GLM-5V-Turbo | GPT-6 Sol |
|---|---|---|
| LMArena WebDev | 1400 | 1692 |
| LMArena Coding | 1467 | 1453 |
| DeepSWE | — | 68.8% |
| FrontierCode | — | 49.3% |

## Agentic & Tool Use

- GLM-5V-Turbo: —
- GPT-6 Sol: 33.5 (#49)

| Benchmark | GLM-5V-Turbo | GPT-6 Sol |
|---|---|---|
| GDP.pdf | — | 28% |

## Reasoning

- GLM-5V-Turbo: 28.5 (#87)
- GPT-6 Sol: 49.3 (#34)

| Benchmark | GLM-5V-Turbo | GPT-6 Sol |
|---|---|---|
| LMArena Hard Prompts | 1443 | 1420 |
| NYT Connections (extended) | — | 90.1% |
| EBR-Bench | — | 53.3% |

## Math

- GLM-5V-Turbo: 36.6 (#109)
- GPT-6 Sol: 35.8 (#135)

| Benchmark | GLM-5V-Turbo | GPT-6 Sol |
|---|---|---|
| LMArena Math | 1440 | 1404 |

## Knowledge

- GLM-5V-Turbo: 39.9 (#113)
- GPT-6 Sol: 43.0 (#87)

| Benchmark | GLM-5V-Turbo | GPT-6 Sol |
|---|---|---|
| LMArena Expert | 1451 | 1449 |
| Vectara Hallucination Rate | — | 6.5% |

## Multimodal

- GLM-5V-Turbo: 40.1 (#41)
- GPT-6 Sol: 47.6 (#9)

| Benchmark | GLM-5V-Turbo | GPT-6 Sol |
|---|---|---|
| LMArena Vision | 1264 | — |
| Furniture Assembly | — | 58.3% |
| LMArena Document | 1416 | — |

## Multilingual

- GLM-5V-Turbo: 53.2 (#69)
- GPT-6 Sol: 51.0 (#113)

| Benchmark | GLM-5V-Turbo | GPT-6 Sol |
|---|---|---|
| LMArena Non-English | 1420 | 1389 |
| LMArena Chinese | 1488 | 1416 |
| LMArena Russian | 1432 | 1407 |
| LMArena Spanish | 1446 | 1377 |
| LMArena French | 1443 | — |
| LMArena German | 1422 | — |
| LMArena Korean | 1399 | — |

## Instruction Following

- GLM-5V-Turbo: 75.1 (#75)
- GPT-6 Sol: 74.4 (#97)

| Benchmark | GLM-5V-Turbo | GPT-6 Sol |
|---|---|---|
| LMArena Instruction Following | 1422 | 1406 |

## Long Context

- GLM-5V-Turbo: 44.0 (#76)
- GPT-6 Sol: 43.2 (#100)

| Benchmark | GLM-5V-Turbo | GPT-6 Sol |
|---|---|---|
| LMArena Longer Query | 1439 | 1415 |

## Writing & Preference

- GLM-5V-Turbo: 62.6 (#69)
- GPT-6 Sol: 72.3 (#15)

| Benchmark | GLM-5V-Turbo | GPT-6 Sol |
|---|---|---|
| LMArena Text | 1437 | 1396 |
| LMArena Creative Writing | 1414 | 1392 |
| LMArena Multi-Turn | 1432 | 1405 |
| EQ-Bench Creative Writing | — | 2125 |

## FAQ

### Is GLM-5V-Turbo better than GPT-6 Sol?

GPT-6 Sol is the stronger model overall, scoring 49.2 to 43.1 on the Noometry Index. GLM-5V-Turbo costs 2.1× less per token, which makes it the better buy when GPT-6 Sol's lead doesn't matter for your workload.

### Which is cheaper, GLM-5V-Turbo or GPT-6 Sol?

GLM-5V-Turbo is cheaper. It lists at $1.20 per million input tokens and $4 per million output tokens; GPT-6 Sol lists at $2 and $10.

### Is GLM-5V-Turbo or GPT-6 Sol better for coding?

GPT-6 Sol scores higher on coding benchmarks: 59.6 versus 41.8 in the Noometry coding category.

### Which has the bigger context window?

GPT-6 Sol does, with 1.05M tokens against 200K.

### How many benchmarks do GLM-5V-Turbo and GPT-6 Sol share?

14 benchmarks have published results for both models. GLM-5V-Turbo has 19 scored results on Noometry and GPT-6 Sol has 22.
