# GLM-5.3-Flash vs Kimi K2.7 Code

> GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 43.0 on the Noometry Index.

- Canonical page: https://noometry.com/compare/glm-5-3-flash-vs-kimi-k2-7-code
- Last updated: 2026-10-10
- Shared benchmarks: 14

## Summary

- They share 14 benchmarks with published results for both. GLM-5.3-Flash scores higher in 4 categories and Kimi K2.7 Code in 1 category; 4 gaps are clear of the uncertainty.
- The widest gap is in coding, where GLM-5.3-Flash leads 53.7 to 42.4.
- The biggest single-benchmark swing is DeepSWE: 63.4% for GLM-5.3-Flash and 30.5% for Kimi K2.7 Code.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $0.95 / $4 for Kimi K2.7 Code.
- GLM-5.3-Flash accepts more context: 1M tokens versus 262K.

## Snapshot

| | GLM-5.3-Flash | Kimi K2.7 Code |
|---|---|---|
| Provider | Z.ai (Zhipu) | Moonshot AI |
| Noometry Index | 49.6 | 43.0 |
| Rank | 45 | 90 |
| Context | 1M | 262K |
| Input $/M | $0.15 | $0.95 |
| Output $/M | $0.50 | $4 |
| Weights | Open | Open |

## Coding

- GLM-5.3-Flash: 53.7 (#26)
- Kimi K2.7 Code: 42.4 (#92)

| Benchmark | GLM-5.3-Flash | Kimi K2.7 Code |
|---|---|---|
| DeepSWE | 63.4% | 30.5% |
| FrontierCode | 31.8% | 30.1% |
| LMArena WebDev | 1614 | 1473 |
| SciCode | 51.6% | 47.5% |
| ALE-Bench | 303.55 | 886.23 |
| WeirdML | — | 54.1% |
| LMArena Coding | 1506 | — |

## Agentic & Tool Use

- GLM-5.3-Flash: 34.3 (#41)
- Kimi K2.7 Code: 23.9 (#115)

| Benchmark | GLM-5.3-Flash | Kimi K2.7 Code |
|---|---|---|
| APEX-Agents | 52.8% | 37.6% |
| GBAEval | — | 0.9% |
| GDP.pdf | 14% | — |
| Vending-Bench 2 | — | 5,083 |

## Reasoning

- GLM-5.3-Flash: 31.7 (#69)
- Kimi K2.7 Code: 38.2 (#56)

| Benchmark | GLM-5.3-Flash | Kimi K2.7 Code |
|---|---|---|
| CritPt | 15.4% | 10% |
| Chess Puzzles | 14% | 21% |
| Surface Evolver Bench | 52.5% | 48.8% |
| Epoch Capabilities Index | 151.9 | 150.01 |
| SimpleBench | — | 57.9% |
| LMArena Hard Prompts | 1490 | — |
| Mystery Game Puzzles | 8% | — |
| Bench to the Future 3 | 0.15 | — |

## Math

- GLM-5.3-Flash: 53.1 (#38)
- Kimi K2.7 Code: 52.6 (#39)

| Benchmark | GLM-5.3-Flash | Kimi K2.7 Code |
|---|---|---|
| FrontierMath (Tiers 1-3) | 55.8% | 54% |
| FrontierMath Tier 4 | 17.1% | 12.2% |
| OTIS Mock AIME 2024-2025 | 93.9% | 95.6% |
| ProofBench | 21% | — |
| LMArena Math | 1499 | — |

## Knowledge

- GLM-5.3-Flash: 58.4 (#33)
- Kimi K2.7 Code: 53.6 (#51)

| Benchmark | GLM-5.3-Flash | Kimi K2.7 Code |
|---|---|---|
| GPQA Diamond | 90.2% | 87.9% |
| SimpleQA Verified | — | 36.5% |
| LMArena Expert | 1512 | — |

## Multimodal

- GLM-5.3-Flash: 42.6 (#25)
- Kimi K2.7 Code: —

| Benchmark | GLM-5.3-Flash | Kimi K2.7 Code |
|---|---|---|
| LMArena Vision | 1304 | — |

## Multilingual

- GLM-5.3-Flash: 56.2 (#24)
- Kimi K2.7 Code: —

| Benchmark | GLM-5.3-Flash | Kimi K2.7 Code |
|---|---|---|
| LMArena Non-English | 1462 | — |
| LMArena Chinese | 1529 | — |
| LMArena French | 1495 | — |
| LMArena German | 1457 | — |
| LMArena Japanese | 1420 | — |
| LMArena Korean | 1443 | — |
| LMArena Russian | 1472 | — |
| LMArena Spanish | 1471 | — |

## Instruction Following

- GLM-5.3-Flash: 77.8 (#17)
- Kimi K2.7 Code: —

| Benchmark | GLM-5.3-Flash | Kimi K2.7 Code |
|---|---|---|
| LMArena Instruction Following | 1480 | — |

## Long Context

- GLM-5.3-Flash: 45.3 (#41)
- Kimi K2.7 Code: —

| Benchmark | GLM-5.3-Flash | Kimi K2.7 Code |
|---|---|---|
| LMArena Longer Query | 1479 | — |

## Writing & Preference

- GLM-5.3-Flash: 65.4 (#49)
- Kimi K2.7 Code: —

| Benchmark | GLM-5.3-Flash | Kimi K2.7 Code |
|---|---|---|
| LMArena Text | 1471 | — |
| LMArena Creative Writing | 1440 | — |
| LMArena Multi-Turn | 1471 | — |

## FAQ

### Is GLM-5.3-Flash better than Kimi K2.7 Code?

GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 43.0 on the Noometry Index.

### Which is cheaper, GLM-5.3-Flash or Kimi K2.7 Code?

GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; Kimi K2.7 Code lists at $0.95 and $4.

### Is GLM-5.3-Flash or Kimi K2.7 Code better for coding?

GLM-5.3-Flash scores higher on coding benchmarks: 53.7 versus 42.4 in the Noometry coding category.

### Which has the bigger context window?

GLM-5.3-Flash does, with 1M tokens against 262K.

### How many benchmarks do GLM-5.3-Flash and Kimi K2.7 Code share?

14 benchmarks have published results for both models. GLM-5.3-Flash has 36 scored results on Noometry and Kimi K2.7 Code has 19.