# DeepSeek-V3.1 vs GLM-4.6V

> DeepSeek-V3.1 is the stronger model overall, scoring 42.2 to 40.9 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v3-1-vs-glm-4-6v
- Last updated: 2026-10-10
- Shared benchmarks: 11

## Summary

- They share 11 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 5 categories and GLM-4.6V in 2 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where DeepSeek-V3.1 leads 43.0 to 37.4.
- DeepSeek-V3.1 is cheaper at $0.25 / $0.95 per million input/output tokens, against $0.30 / $0.90 for GLM-4.6V.
- DeepSeek-V3.1 accepts more context: 164K tokens versus 128K.

## Snapshot

| | DeepSeek-V3.1 | GLM-4.6V |
|---|---|---|
| Provider | DeepSeek | Z.ai (Zhipu) |
| Noometry Index | 42.2 | 40.9 |
| Rank | 106 | 133 |
| Context | 164K | 128K |
| Input $/M | $0.25 | $0.30 |
| Output $/M | $0.95 | $0.90 |
| Weights | Open | Open |

## Coding

- DeepSeek-V3.1: 39.8 (#140)
- GLM-4.6V: 40.2 (#128)

| Benchmark | DeepSeek-V3.1 | GLM-4.6V |
|---|---|---|
| LMArena Coding | 1417 | 1390 |
| WeirdML | 38.4% | — |

## Reasoning

- DeepSeek-V3.1: 27.3 (#109)
- GLM-4.6V: 26.5 (#117)

| Benchmark | DeepSeek-V3.1 | GLM-4.6V |
|---|---|---|
| LMArena Hard Prompts | 1417 | 1368 |
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| DTBench | 82.7% | — |
| LMCA | 24.3% | — |
| Epoch Capabilities Index | 139.92 | — |
| ForecastBench | 58 | — |

## Math

- DeepSeek-V3.1: 36.1 (#125)
- GLM-4.6V: —

| Benchmark | DeepSeek-V3.1 | GLM-4.6V |
|---|---|---|
| LMArena Math | 1420 | — |

## Knowledge

- DeepSeek-V3.1: 43.0 (#86)
- GLM-4.6V: 37.4 (#148)

| Benchmark | DeepSeek-V3.1 | GLM-4.6V |
|---|---|---|
| LMArena Expert | 1403 | 1372 |
| Vectara Hallucination Rate | 5.5% | — |

## Multimodal

- DeepSeek-V3.1: —
- GLM-4.6V: 34.2 (#83)

| Benchmark | DeepSeek-V3.1 | GLM-4.6V |
|---|---|---|
| LMArena Vision | — | 1164 |

## Multilingual

- DeepSeek-V3.1: 51.8 (#100)
- GLM-4.6V: 48.8 (#135)

| Benchmark | DeepSeek-V3.1 | GLM-4.6V |
|---|---|---|
| LMArena Non-English | 1400 | 1359 |
| LMArena Chinese | 1470 | 1426 |
| LMArena Russian | 1405 | 1341 |
| LMArena French | 1447 | — |
| LMArena German | 1412 | — |
| LMArena Japanese | 1374 | — |
| LMArena Korean | 1337 | — |
| LMArena Spanish | 1429 | — |

## Instruction Following

- DeepSeek-V3.1: 74.1 (#105)
- GLM-4.6V: 71.6 (#145)

| Benchmark | DeepSeek-V3.1 | GLM-4.6V |
|---|---|---|
| LMArena Instruction Following | 1400 | 1351 |

## Long Context

- DeepSeek-V3.1: 36.3 (#227)
- GLM-4.6V: 41.3 (#138)

| Benchmark | DeepSeek-V3.1 | GLM-4.6V |
|---|---|---|
| LMArena Longer Query | 1422 | 1358 |
| Fiction.LiveBench | 52.8% | — |

## Writing & Preference

- DeepSeek-V3.1: 60.5 (#93)
- GLM-4.6V: 56.8 (#132)

| Benchmark | DeepSeek-V3.1 | GLM-4.6V |
|---|---|---|
| LMArena Text | 1420 | 1377 |
| LMArena Creative Writing | 1401 | 1347 |
| LMArena Multi-Turn | 1408 | 1359 |
| EQ-Bench Creative Writing | 1436 | — |

## FAQ

### Is DeepSeek-V3.1 better than GLM-4.6V?

DeepSeek-V3.1 is the stronger model overall, scoring 42.2 to 40.9 on the Noometry Index.

### Which is cheaper, DeepSeek-V3.1 or GLM-4.6V?

DeepSeek-V3.1 is cheaper. It lists at $0.25 per million input tokens and $0.95 per million output tokens; GLM-4.6V lists at $0.30 and $0.90.

### Is DeepSeek-V3.1 or GLM-4.6V better for coding?

They score almost the same on coding (39.8 vs 40.2); test both on your own repository before choosing.

### Which has the bigger context window?

DeepSeek-V3.1 does, with 164K tokens against 128K.

### How many benchmarks do DeepSeek-V3.1 and GLM-4.6V share?

11 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and GLM-4.6V has 12.