# GLM-5.3 vs Phi-4

> GLM-5.3 is the stronger model overall, scoring 54.6 to 30.9 on the Noometry Index. Phi-4 costs 25× less per token, which makes it the better buy when GLM-5.3's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/glm-5-3-vs-phi-4
- Last updated: 2026-10-10
- Shared benchmarks: 21

## Summary

- They share 21 benchmarks with published results for both. GLM-5.3 scores higher in 9 categories and Phi-4 in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.3 leads 62.1 to 19.6.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 91.1% for GLM-5.3 and 13.8% for Phi-4.
- Phi-4 is cheaper at $0.07 / $0.14 per million input/output tokens, against $1.40 / $4.40 for GLM-5.3.
- GLM-5.3 accepts more context: 1M tokens versus 128K.

## Snapshot

| | GLM-5.3 | Phi-4 |
|---|---|---|
| Provider | Z.ai (Zhipu) | Microsoft |
| Noometry Index | 54.6 | 30.9 |
| Rank | 25 | 269 |
| Context | 1M | 128K |
| Input $/M | $1.40 | $0.07 |
| Output $/M | $4.40 | $0.14 |
| Weights | Open | Open |

## Coding

- GLM-5.3: 59.0 (#12)
- Phi-4: 33.8 (#237)

| Benchmark | GLM-5.3 | Phi-4 |
|---|---|---|
| LMArena Coding | 1499 | 1231 |
| DeepSWE | 69% | — |
| FrontierCode | 40.1% | — |
| LMArena WebDev | 1623 | — |
| FrontierSWE | 30.2% | — |
| SciCode | 59% | — |
| WeirdML | 75.4% | — |
| BigCodeBench Instruct | — | 45.5% |
| LiveBench Coding | — | 30.7% |
| BigCodeBench Complete | — | 55.4% |
| ALE-Bench | 1,317 | — |

## Agentic & Tool Use

- GLM-5.3: 36.3 (#34)
- Phi-4: 22.6 (#120)

| Benchmark | GLM-5.3 | Phi-4 |
|---|---|---|
| APEX-Agents | 56.6% | — |
| Berkeley Function Calling Leaderboard | — | 28.8% |
| BALROG | — | 11.6% |
| Vending-Bench 2 | 8,164 | — |

## Reasoning

- GLM-5.3: 45.2 (#42)
- Phi-4: 17.0 (#287)

| Benchmark | GLM-5.3 | Phi-4 |
|---|---|---|
| Chess Puzzles | 21% | 1% |
| LMArena Hard Prompts | 1491 | 1220 |
| Epoch Capabilities Index | 155.56 | 130.43 |
| NYT Connections (extended) | 74.2% | — |
| CritPt | 19.1% | — |
| LiveBench Reasoning | — | 47.8% |
| Mystery Game Puzzles | 33% | — |
| DTBench | 87.7% | — |
| LiveBench Data Analysis | — | 45.2% |
| LMCA | 55.5% | — |
| Bench to the Future 3 | 0.15 | — |
| LiveBench | — | 41.6% |

## Math

- GLM-5.3: 62.1 (#27)
- Phi-4: 19.6 (#279)

| Benchmark | GLM-5.3 | Phi-4 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 91.1% | 13.8% |
| LMArena Math | 1493 | 1246 |
| FrontierMath (Tiers 1-3) | 68.8% | — |
| FrontierMath Tier 4 | 29.3% | — |
| ProofBench | 49% | — |
| LiveBench Math | — | 42% |
| MATH Level 5 | — | 64.9% |

## Knowledge

- GLM-5.3: 58.3 (#34)
- Phi-4: 32.5 (#204)

| Benchmark | GLM-5.3 | Phi-4 |
|---|---|---|
| GPQA Diamond | 90.9% | 56.1% |
| LMArena Expert | 1512 | 1203 |
| SimpleQA Verified | 41% | — |
| Confabulations | — | 29.4% |
| Vectara Hallucination Rate | — | 3.7% |
| MMLU | — | 84.8% |

## Multilingual

- GLM-5.3: 55.9 (#27)
- Phi-4: 37.4 (#232)

| Benchmark | GLM-5.3 | Phi-4 |
|---|---|---|
| LMArena Non-English | 1457 | 1197 |
| LMArena Chinese | 1523 | 1211 |
| LMArena French | 1499 | 1223 |
| LMArena German | 1505 | 1222 |
| LMArena Japanese | 1438 | 1158 |
| LMArena Korean | 1478 | 1151 |
| LMArena Russian | 1460 | 1209 |
| LMArena Spanish | 1444 | 1234 |

## Instruction Following

- GLM-5.3: 77.8 (#15)
- Phi-4: 60.6 (#246)

| Benchmark | GLM-5.3 | Phi-4 |
|---|---|---|
| LMArena Instruction Following | 1481 | 1201 |
| LiveBench Instruction Following | — | 58.4% |

## Long Context

- GLM-5.3: 45.5 (#32)
- Phi-4: 36.9 (#221)

| Benchmark | GLM-5.3 | Phi-4 |
|---|---|---|
| LMArena Longer Query | 1485 | 1217 |

## Writing & Preference

- GLM-5.3: 75.9 (#6)
- Phi-4: 40.7 (#239)

| Benchmark | GLM-5.3 | Phi-4 |
|---|---|---|
| LMArena Text | 1473 | 1217 |
| LMArena Creative Writing | 1459 | 1182 |
| LMArena Multi-Turn | 1473 | 1206 |
| Short-Story Creative Writing | — | 62.6% |
| EQ-Bench Creative Writing | 2075 | — |
| LiveBench Language | — | 25.6% |

## FAQ

### Is GLM-5.3 better than Phi-4?

GLM-5.3 is the stronger model overall, scoring 54.6 to 30.9 on the Noometry Index. Phi-4 costs 25× less per token, which makes it the better buy when GLM-5.3's lead doesn't matter for your workload.

### Which is cheaper, GLM-5.3 or Phi-4?

Phi-4 is cheaper. It lists at $0.07 per million input tokens and $0.14 per million output tokens; GLM-5.3 lists at $1.40 and $4.40.

### Is GLM-5.3 or Phi-4 better for coding?

GLM-5.3 scores higher on coding benchmarks: 59.0 versus 33.8 in the Noometry coding category.

### Which has the bigger context window?

GLM-5.3 does, with 1M tokens against 128K.

### How many benchmarks do GLM-5.3 and Phi-4 share?

21 benchmarks have published results for both models. GLM-5.3 has 41 scored results on Noometry and Phi-4 has 37.