# GLM-4.7-Flash vs GLM-5.3-Flash

> GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 38.8 on the Noometry Index. GLM-4.7-Flash costs 1.6× less per token, which makes it the better buy when GLM-5.3-Flash's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/glm-4-7-flash-vs-glm-5-3-flash
- Last updated: 2026-10-10
- Shared benchmarks: 19

## Summary

- They share 19 benchmarks with published results for both. GLM-4.7-Flash scores higher in 0 categories and GLM-5.3-Flash in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.3-Flash leads 48.0 to 20.9.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 58.3% for GLM-4.7-Flash and 93.9% for GLM-5.3-Flash.
- GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $0.15 / $0.50 for GLM-5.3-Flash.
- GLM-5.3-Flash accepts more context: 1M tokens versus 200K.

## Snapshot

| | GLM-4.7-Flash | GLM-5.3-Flash |
|---|---|---|
| Provider | Z.ai (Zhipu) | Z.ai (Zhipu) |
| Noometry Index | 38.8 | 51.8 |
| Rank | 180 | 41 |
| Context | 200K | 1M |
| Input $/M | $0.06 | $0.15 |
| Output $/M | $0.40 | $0.50 |
| Weights | Open | Open |

## Coding

- GLM-4.7-Flash: 40.6 (#135)
- GLM-5.3-Flash: 53.1 (#31)

| Benchmark | GLM-4.7-Flash | GLM-5.3-Flash |
|---|---|---|
| LMArena Coding | 1383 | 1508 |
| DeepSWE | — | 63.4% |
| FrontierCode | — | 31.8% |
| CursorBench | — | 36.8% |
| LMArena WebDev | — | 1609 |
| FrontierSWE | — | 18.1% |
| SciCode | — | 51.6% |
| ALE-Bench | — | 303.55 |

## Agentic & Tool Use

- GLM-4.7-Flash: —
- GLM-5.3-Flash: 34.2 (#47)

| Benchmark | GLM-4.7-Flash | GLM-5.3-Flash |
|---|---|---|
| APEX-Agents | — | 52.8% |
| GDP.pdf | — | 14% |

## Reasoning

- GLM-4.7-Flash: 20.9 (#229)
- GLM-5.3-Flash: 48.0 (#42)

| Benchmark | GLM-4.7-Flash | GLM-5.3-Flash |
|---|---|---|
| Chess Puzzles | 0% | 14% |
| LMArena Hard Prompts | 1356 | 1491 |
| ARC-AGI-2 | — | 65.8% |
| ARC-AGI-1 | — | 91% |
| CritPt | — | 15.4% |
| Mystery Game Puzzles | — | 8% |
| Surface Evolver Bench | — | 52.5% |
| Bench to the Future 3 | — | 0.15 |
| Epoch Capabilities Index | — | 151.88 |

## Math

- GLM-4.7-Flash: 36.1 (#173)
- GLM-5.3-Flash: 53.3 (#47)

| Benchmark | GLM-4.7-Flash | GLM-5.3-Flash |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 58.3% | 93.9% |
| LMArena Math | 1355 | 1500 |
| FrontierMath (Tiers 1-3) | — | 55.8% |
| FrontierMath Tier 4 | — | 17.1% |
| ProofBench | — | 21% |

## Knowledge

- GLM-4.7-Flash: 35.5 (#184)
- GLM-5.3-Flash: 58.4 (#36)

| Benchmark | GLM-4.7-Flash | GLM-5.3-Flash |
|---|---|---|
| GPQA Diamond | 60.5% | 90.2% |
| LMArena Expert | 1357 | 1513 |
| Vectara Hallucination Rate | 9.3% | — |

## Multimodal

- GLM-4.7-Flash: —
- GLM-5.3-Flash: 42.8 (#27)

| Benchmark | GLM-4.7-Flash | GLM-5.3-Flash |
|---|---|---|
| LMArena Vision | — | 1296 |

## Multilingual

- GLM-4.7-Flash: 46.5 (#158)
- GLM-5.3-Flash: 56.0 (#25)

| Benchmark | GLM-4.7-Flash | GLM-5.3-Flash |
|---|---|---|
| LMArena Non-English | 1330 | 1462 |
| LMArena Chinese | 1403 | 1527 |
| LMArena French | 1332 | 1496 |
| LMArena German | 1337 | 1470 |
| LMArena Korean | 1283 | 1446 |
| LMArena Russian | 1332 | 1469 |
| LMArena Spanish | 1350 | 1471 |
| LMArena Japanese | — | 1429 |

## Instruction Following

- GLM-4.7-Flash: 70.1 (#167)
- GLM-5.3-Flash: 77.5 (#20)

| Benchmark | GLM-4.7-Flash | GLM-5.3-Flash |
|---|---|---|
| LMArena Instruction Following | 1327 | 1478 |

## Long Context

- GLM-4.7-Flash: 40.9 (#148)
- GLM-5.3-Flash: 45.4 (#39)

| Benchmark | GLM-4.7-Flash | GLM-5.3-Flash |
|---|---|---|
| LMArena Longer Query | 1345 | 1482 |

## Writing & Preference

- GLM-4.7-Flash: 47.4 (#210)
- GLM-5.3-Flash: 65.3 (#50)

| Benchmark | GLM-4.7-Flash | GLM-5.3-Flash |
|---|---|---|
| LMArena Text | 1351 | 1471 |
| LMArena Creative Writing | 1297 | 1442 |
| LMArena Multi-Turn | 1342 | 1467 |
| EQ-Bench Creative Writing | 1125 | — |

## FAQ

### Is GLM-4.7-Flash better than GLM-5.3-Flash?

GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 38.8 on the Noometry Index. GLM-4.7-Flash costs 1.6× less per token, which makes it the better buy when GLM-5.3-Flash's lead doesn't matter for your workload.

### Which is cheaper, GLM-4.7-Flash or GLM-5.3-Flash?

GLM-4.7-Flash is cheaper. It lists at $0.06 per million input tokens and $0.40 per million output tokens; GLM-5.3-Flash lists at $0.15 and $0.50.

### Is GLM-4.7-Flash or GLM-5.3-Flash better for coding?

GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 40.6 in the Noometry coding category.

### Which has the bigger context window?

GLM-5.3-Flash does, with 1M tokens against 200K.

### How many benchmarks do GLM-4.7-Flash and GLM-5.3-Flash share?

19 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and GLM-5.3-Flash has 40.