# GLM-4.6 vs phi-3-medium 14B

> GLM-4.6 is the stronger model overall, scoring 41.4 to 29.7 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-4-6-vs-phi-3-medium-14b
- Last updated: 2026-10-11
- Shared benchmarks: 0

## Summary

- The widest gap is in knowledge, where GLM-4.6 leads 40.2 to 9.1.

## Snapshot

| | GLM-4.6 | phi-3-medium 14B |
|---|---|---|
| Provider | Z.ai (Zhipu) | Microsoft |
| Noometry Index | 41.4 | 29.7 |
| Rank | 135 | 306 |
| Context | 205K | — |
| Input $/M | $0.60 | — |
| Output $/M | $2.20 | — |
| Weights | Open | Open |

## Coding

- GLM-4.6: 40.1 (#148)
- phi-3-medium 14B: 36.8 (#201)

| Benchmark | GLM-4.6 | phi-3-medium 14B |
|---|---|---|
| SWE-bench Verified (bash only) | 55.4% | — |
| LMArena WebDev | 1340 | — |
| SciCode | 38.4% | — |
| BigCodeBench Instruct | — | 37.6% |
| LMArena Coding | 1449 | — |
| BigCodeBench Complete | — | 48.7% |
| ALE-Bench | 340.82 | — |

## Agentic & Tool Use

- GLM-4.6: 32.3 (#66)
- phi-3-medium 14B: —

| Benchmark | GLM-4.6 | phi-3-medium 14B |
|---|---|---|
| Terminal-Bench | 24.5% | — |
| Berkeley Function Calling Leaderboard | 72.4% | — |

## Reasoning

- GLM-4.6: 23.7 (#172)
- phi-3-medium 14B: —

| Benchmark | GLM-4.6 | phi-3-medium 14B |
|---|---|---|
| Kagi LLM Benchmark | 47.4% | — |
| CritPt | 1.1% | — |
| LMArena Hard Prompts | 1440 | — |
| Adversarial NLI | — | 55.8% |
| BIG-Bench Hard | — | 81.4% |
| Epoch Capabilities Index | — | 121.23 |
| HellaSwag | — | 82.4% |
| WinoGrande | — | 81.5% |

## Math

- GLM-4.6: 39.1 (#111)
- phi-3-medium 14B: 27.3 (#250)

| Benchmark | GLM-4.6 | phi-3-medium 14B |
|---|---|---|
| LMArena Math | 1432 | — |
| MATH Level 5 | — | 17.6% |
| FrontierMath (Feb 2025 set) | 3.8% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |

## Knowledge

- GLM-4.6: 40.2 (#124)
- phi-3-medium 14B: 9.1 (#306)

| Benchmark | GLM-4.6 | phi-3-medium 14B |
|---|---|---|
| GPQA Diamond | — | 27.6% |
| Vectara Hallucination Rate | 9.5% | — |
| LMArena Expert | 1431 | — |
| ARC (AI2) Challenge | — | 91.6% |
| MMLU | — | 78% |
| OpenBookQA | — | 87.4% |
| TriviaQA | — | 73.9% |

## Multilingual

- GLM-4.6: 53.5 (#66)
- phi-3-medium 14B: —

| Benchmark | GLM-4.6 | phi-3-medium 14B |
|---|---|---|
| LMArena Non-English | 1426 | — |
| LMArena Chinese | 1499 | — |
| LMArena French | 1459 | — |
| LMArena German | 1447 | — |
| LMArena Japanese | 1393 | — |
| LMArena Korean | 1400 | — |
| LMArena Russian | 1419 | — |
| LMArena Spanish | 1436 | — |

## Instruction Following

- GLM-4.6: 74.3 (#98)
- phi-3-medium 14B: —

| Benchmark | GLM-4.6 | phi-3-medium 14B |
|---|---|---|
| LMArena Instruction Following | 1410 | — |

## Long Context

- GLM-4.6: 43.4 (#94)
- phi-3-medium 14B: —

| Benchmark | GLM-4.6 | phi-3-medium 14B |
|---|---|---|
| LMArena Longer Query | 1422 | — |

## Writing & Preference

- GLM-4.6: 61.1 (#90)
- phi-3-medium 14B: —

| Benchmark | GLM-4.6 | phi-3-medium 14B |
|---|---|---|
| LMArena Text | 1440 | — |
| LMArena Creative Writing | 1411 | — |
| EQ-Bench Creative Writing | 1411 | — |
| LMArena Multi-Turn | 1427 | — |

## FAQ

### Is GLM-4.6 better than phi-3-medium 14B?

GLM-4.6 is the stronger model overall, scoring 41.4 to 29.7 on the Noometry Index.

### Is GLM-4.6 or phi-3-medium 14B better for coding?

GLM-4.6 scores higher on coding benchmarks: 40.1 versus 36.8 in the Noometry coding category.

### How many benchmarks do GLM-4.6 and phi-3-medium 14B share?

0 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and phi-3-medium 14B has 13.