# GLM-4.6V vs Qwen3-Next 80B-A3B Instruct

> Qwen3-Next 80B-A3B Instruct is the stronger model overall, scoring 43.0 to 41.3 on the Noometry Index. GLM-4.6V costs 1.9× less per token, which makes it the better buy when Qwen3-Next 80B-A3B Instruct's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/glm-4-6v-vs-qwen3-next-80b-a3b-instruct
- Last updated: 2026-10-10
- Shared benchmarks: 11

## Summary

- They share 11 benchmarks with published results for both. GLM-4.6V scores higher in 2 categories and Qwen3-Next 80B-A3B Instruct in 5 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in long context, where GLM-4.6V leads 41.3 to 37.0.
- GLM-4.6V is cheaper at $0.30 / $0.90 per million input/output tokens, against $0.50 / $2 for Qwen3-Next 80B-A3B Instruct.
- Qwen3-Next 80B-A3B Instruct accepts more context: 131K tokens versus 128K.

## Snapshot

| | GLM-4.6V | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 41.3 | 43.0 |
| Rank | 137 | 102 |
| Context | 128K | 131K |
| Input $/M | $0.30 | $0.50 |
| Output $/M | $0.90 | $2 |
| Weights | Open | Open |

## Coding

- GLM-4.6V: 40.9 (#128)
- Qwen3-Next 80B-A3B Instruct: 42.5 (#98)

| Benchmark | GLM-4.6V | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Coding | 1390 | 1440 |

## Reasoning

- GLM-4.6V: 27.6 (#115)
- Qwen3-Next 80B-A3B Instruct: 31.1 (#81)

| Benchmark | GLM-4.6V | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Hard Prompts | 1368 | 1428 |
| Kagi LLM Benchmark | — | 66.7% |

## Math

- GLM-4.6V: —
- Qwen3-Next 80B-A3B Instruct: 38.8 (#126)

| Benchmark | GLM-4.6V | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| Omni-MATH | — | 46.7% |
| LMArena Math | — | 1440 |

## Knowledge

- GLM-4.6V: 38.0 (#149)
- Qwen3-Next 80B-A3B Instruct: 41.8 (#106)

| Benchmark | GLM-4.6V | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Expert | 1371 | 1417 |
| MMLU-Pro | — | 78.6% |
| Vectara Hallucination Rate | — | 9.3% |
| GPQA (HELM) | — | 63% |

## Multimodal

- GLM-4.6V: 34.8 (#90)
- Qwen3-Next 80B-A3B Instruct: —

| Benchmark | GLM-4.6V | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Vision | 1164 | — |

## Multilingual

- GLM-4.6V: 48.6 (#141)
- Qwen3-Next 80B-A3B Instruct: 52.1 (#93)

| Benchmark | GLM-4.6V | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Non-English | 1359 | 1407 |
| LMArena Chinese | 1425 | 1460 |
| LMArena Russian | 1340 | 1404 |
| LMArena French | — | 1413 |
| LMArena German | — | 1417 |
| LMArena Japanese | — | 1395 |
| LMArena Korean | — | 1364 |
| LMArena Spanish | — | 1435 |

## Instruction Following

- GLM-4.6V: 71.4 (#151)
- Qwen3-Next 80B-A3B Instruct: 70.8 (#159)

| Benchmark | GLM-4.6V | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Instruction Following | 1352 | 1389 |
| IFEval | — | 81% |

## Long Context

- GLM-4.6V: 41.3 (#143)
- Qwen3-Next 80B-A3B Instruct: 37.0 (#223)

| Benchmark | GLM-4.6V | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Longer Query | 1358 | 1403 |
| Fiction.LiveBench | — | 55.6% |

## Writing & Preference

- GLM-4.6V: 56.6 (#137)
- Qwen3-Next 80B-A3B Instruct: 58.0 (#121)

| Benchmark | GLM-4.6V | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Text | 1377 | 1417 |
| LMArena Creative Writing | 1347 | 1334 |
| LMArena Multi-Turn | 1360 | 1416 |
| WildBench | — | 80.7% |

## FAQ

### Is GLM-4.6V better than Qwen3-Next 80B-A3B Instruct?

Qwen3-Next 80B-A3B Instruct is the stronger model overall, scoring 43.0 to 41.3 on the Noometry Index. GLM-4.6V costs 1.9× less per token, which makes it the better buy when Qwen3-Next 80B-A3B Instruct's lead doesn't matter for your workload.

### Which is cheaper, GLM-4.6V or Qwen3-Next 80B-A3B Instruct?

GLM-4.6V is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; Qwen3-Next 80B-A3B Instruct lists at $0.50 and $2.

### Is GLM-4.6V or Qwen3-Next 80B-A3B Instruct better for coding?

Qwen3-Next 80B-A3B Instruct scores higher on coding benchmarks: 42.5 versus 40.9 in the Noometry coding category.

### Which has the bigger context window?

Qwen3-Next 80B-A3B Instruct does, with 131K tokens against 128K.

### How many benchmarks do GLM-4.6V and Qwen3-Next 80B-A3B Instruct share?

11 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and Qwen3-Next 80B-A3B Instruct has 25.