# GLM-4.6V vs Qwen3 14B

> GLM-4.6V is the stronger model overall, scoring 41.3 to 35.5 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-4-6v-vs-qwen3-14b
- Last updated: 2026-10-12
- Shared benchmarks: 0

## Summary

- The widest gap is in reasoning, where GLM-4.6V leads 27.6 to 18.5.
- GLM-4.6V is cheaper at $0.30 / $0.90 per million input/output tokens, against $0.35 / $1.40 for Qwen3 14B.
- Qwen3 14B accepts more context: 131K tokens versus 128K.

## Snapshot

| | GLM-4.6V | Qwen3 14B |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 41.3 | 35.5 |
| Rank | 137 | 225 |
| Context | 128K | 131K |
| Input $/M | $0.30 | $0.35 |
| Output $/M | $0.90 | $1.40 |
| Weights | Open | Open |

## Coding

- GLM-4.6V: 40.9 (#128)
- Qwen3 14B: 37.3 (#195)

| Benchmark | GLM-4.6V | Qwen3 14B |
|---|---|---|
| SciCode | — | 31.6% |
| LMArena Coding | 1390 | — |

## Agentic & Tool Use

- GLM-4.6V: —
- Qwen3 14B: 29.6 (#83)

| Benchmark | GLM-4.6V | Qwen3 14B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 41% |

## Reasoning

- GLM-4.6V: 27.6 (#115)
- Qwen3 14B: 18.5 (#280)

| Benchmark | GLM-4.6V | Qwen3 14B |
|---|---|---|
| Kagi LLM Benchmark | — | 49.1% |
| CritPt | — | 0% |
| Chess Puzzles | — | 4% |
| LMArena Hard Prompts | 1368 | — |
| DTBench | — | 64% |
| LMCA | — | 18.2% |
| Epoch Capabilities Index | — | 138.23 |

## Math

- GLM-4.6V: —
- Qwen3 14B: 38.6 (#133)

| Benchmark | GLM-4.6V | Qwen3 14B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 66.4% |

## Knowledge

- GLM-4.6V: 38.0 (#149)
- Qwen3 14B: 39.3 (#134)

| Benchmark | GLM-4.6V | Qwen3 14B |
|---|---|---|
| GPQA Diamond | — | 63.8% |
| Vectara Hallucination Rate | — | 5.4% |
| LMArena Expert | 1371 | — |

## Multimodal

- GLM-4.6V: 34.8 (#90)
- Qwen3 14B: —

| Benchmark | GLM-4.6V | Qwen3 14B |
|---|---|---|
| LMArena Vision | 1164 | — |

## Multilingual

- GLM-4.6V: 48.6 (#141)
- Qwen3 14B: —

| Benchmark | GLM-4.6V | Qwen3 14B |
|---|---|---|
| LMArena Non-English | 1359 | — |
| LMArena Chinese | 1425 | — |
| LMArena Russian | 1340 | — |

## Instruction Following

- GLM-4.6V: 71.4 (#151)
- Qwen3 14B: —

| Benchmark | GLM-4.6V | Qwen3 14B |
|---|---|---|
| LMArena Instruction Following | 1352 | — |

## Long Context

- GLM-4.6V: 41.3 (#143)
- Qwen3 14B: 38.1 (#204)

| Benchmark | GLM-4.6V | Qwen3 14B |
|---|---|---|
| Fiction.LiveBench | — | 62.5% |
| LMArena Longer Query | 1358 | — |

## Writing & Preference

- GLM-4.6V: 56.6 (#137)
- Qwen3 14B: —

| Benchmark | GLM-4.6V | Qwen3 14B |
|---|---|---|
| LMArena Text | 1377 | — |
| LMArena Creative Writing | 1347 | — |
| LMArena Multi-Turn | 1360 | — |

## FAQ

### Is GLM-4.6V better than Qwen3 14B?

GLM-4.6V is the stronger model overall, scoring 41.3 to 35.5 on the Noometry Index.

### Which is cheaper, GLM-4.6V or Qwen3 14B?

GLM-4.6V is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; Qwen3 14B lists at $0.35 and $1.40.

### Is GLM-4.6V or Qwen3 14B better for coding?

GLM-4.6V scores higher on coding benchmarks: 40.9 versus 37.3 in the Noometry coding category.

### Which has the bigger context window?

Qwen3 14B does, with 131K tokens against 128K.

### How many benchmarks do GLM-4.6V and Qwen3 14B share?

0 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and Qwen3 14B has 12.