# GLM-4.7 vs Qwen3 14B

> GLM-4.7 is the stronger model overall, scoring 41.7 to 35.0 on the Noometry Index. Qwen3 14B costs 1.6× less per token, which makes it the better buy when GLM-4.7's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/glm-4-7-vs-qwen3-14b
- Last updated: 2026-10-10
- Shared benchmarks: 7

## Summary

- They share 7 benchmarks with published results for both. GLM-4.7 scores higher in 5 categories and Qwen3 14B in 1 category; 5 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-4.7 leads 47.0 to 39.2.
- The biggest single-benchmark swing is GPQA Diamond: 83.3% for GLM-4.7 and 63.8% for Qwen3 14B.
- Qwen3 14B is cheaper at $0.35 / $1.40 per million input/output tokens, against $0.60 / $2.20 for GLM-4.7.
- GLM-4.7 accepts more context: 205K tokens versus 131K.

## Snapshot

| | GLM-4.7 | Qwen3 14B |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 41.7 | 35.0 |
| Rank | 117 | 222 |
| Context | 205K | 131K |
| Input $/M | $0.60 | $0.35 |
| Output $/M | $2.20 | $1.40 |
| Weights | Open | Open |

## Coding

- GLM-4.7: 43.6 (#74)
- Qwen3 14B: 36.7 (#191)

| Benchmark | GLM-4.7 | Qwen3 14B |
|---|---|---|
| SciCode | 45.1% | 31.6% |
| LMArena WebDev | 1435 | — |
| LMArena Coding | 1454 | — |
| ALE-Bench | 399.48 | — |

## Agentic & Tool Use

- GLM-4.7: 26.4 (#96)
- Qwen3 14B: 29.4 (#78)

| Benchmark | GLM-4.7 | Qwen3 14B |
|---|---|---|
| Terminal-Bench | 33.4% | — |
| Berkeley Function Calling Leaderboard | — | 41% |
| Vending-Bench 2 | 2,377 | — |

## Reasoning

- GLM-4.7: 23.7 (#161)
- Qwen3 14B: 18.1 (#272)

| Benchmark | GLM-4.7 | Qwen3 14B |
|---|---|---|
| CritPt | 1.7% | 0% |
| Chess Puzzles | 6% | 4% |
| Epoch Capabilities Index | 143.53 | 138.23 |
| SimpleBench | 47.7% | — |
| Kagi LLM Benchmark | — | 49.1% |
| LMArena Hard Prompts | 1443 | — |
| DTBench | — | 64% |
| LMCA | — | 18.2% |

## Math

- GLM-4.7: 37.7 (#92)
- Qwen3 14B: 37.0 (#102)

| Benchmark | GLM-4.7 | Qwen3 14B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 83.3% | 66.4% |
| ProofBench | 6% | — |
| LMArena Math | 1423 | — |
| FrontierMath (Feb 2025 set) | 2.4% | — |
| FrontierMath Tier 4 (v1) | 0% | — |

## Knowledge

- GLM-4.7: 47.0 (#74)
- Qwen3 14B: 39.2 (#124)

| Benchmark | GLM-4.7 | Qwen3 14B |
|---|---|---|
| GPQA Diamond | 83.3% | 63.8% |
| Vectara Hallucination Rate | 11.7% | 5.4% |
| SimpleQA Verified | 32.2% | — |
| LMArena Expert | 1424 | — |

## Multilingual

- GLM-4.7: 53.0 (#75)
- Qwen3 14B: —

| Benchmark | GLM-4.7 | Qwen3 14B |
|---|---|---|
| LMArena Non-English | 1417 | — |
| LMArena Chinese | 1495 | — |
| LMArena French | 1432 | — |
| LMArena German | 1426 | — |
| LMArena Japanese | 1439 | — |
| LMArena Korean | 1401 | — |
| LMArena Russian | 1423 | — |
| LMArena Spanish | 1433 | — |

## Instruction Following

- GLM-4.7: 74.6 (#90)
- Qwen3 14B: —

| Benchmark | GLM-4.7 | Qwen3 14B |
|---|---|---|
| LMArena Instruction Following | 1411 | — |

## Long Context

- GLM-4.7: 42.8 (#111)
- Qwen3 14B: 38.0 (#201)

| Benchmark | GLM-4.7 | Qwen3 14B |
|---|---|---|
| Fiction.LiveBench | — | 62.5% |
| CL-bench | 15.9% | — |
| CL-bench Life | 10.9% | — |
| LMArena Longer Query | 1432 | — |

## Writing & Preference

- GLM-4.7: 61.1 (#89)
- Qwen3 14B: —

| Benchmark | GLM-4.7 | Qwen3 14B |
|---|---|---|
| LMArena Text | 1436 | — |
| LMArena Creative Writing | 1401 | — |
| EQ-Bench Creative Writing | 1413 | — |
| LMArena Multi-Turn | 1446 | — |

## FAQ

### Is GLM-4.7 better than Qwen3 14B?

GLM-4.7 is the stronger model overall, scoring 41.7 to 35.0 on the Noometry Index. Qwen3 14B costs 1.6× less per token, which makes it the better buy when GLM-4.7's lead doesn't matter for your workload.

### Which is cheaper, GLM-4.7 or Qwen3 14B?

Qwen3 14B is cheaper. It lists at $0.35 per million input tokens and $1.40 per million output tokens; GLM-4.7 lists at $0.60 and $2.20.

### Is GLM-4.7 or Qwen3 14B better for coding?

GLM-4.7 scores higher on coding benchmarks: 43.6 versus 36.7 in the Noometry coding category.

### Which has the bigger context window?

GLM-4.7 does, with 205K tokens against 131K.

### How many benchmarks do GLM-4.7 and Qwen3 14B share?

7 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and Qwen3 14B has 12.