# GLM-4.5V vs Kimi K2.7 Code

> Kimi K2.7 Code is the stronger model overall, scoring 43.0 to 39.2 on the Noometry Index. GLM-4.5V costs 1.9× less per token, which makes it the better buy when Kimi K2.7 Code's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/glm-4-5v-vs-kimi-k2-7-code
- Last updated: 2026-10-10
- Shared benchmarks: 0

## Summary

- The widest gap is in math, where Kimi K2.7 Code leads 52.6 to 34.7.
- GLM-4.5V is cheaper at $0.60 / $1.80 per million input/output tokens, against $0.95 / $4 for Kimi K2.7 Code.
- Kimi K2.7 Code accepts more context: 262K tokens versus 64K.

## Snapshot

| | GLM-4.5V | Kimi K2.7 Code |
|---|---|---|
| Provider | Z.ai (Zhipu) | Moonshot AI |
| Noometry Index | 39.2 | 43.0 |
| Rank | 158 | 90 |
| Context | 64K | 262K |
| Input $/M | $0.60 | $0.95 |
| Output $/M | $1.80 | $4 |
| Weights | Open | Open |

## Coding

- GLM-4.5V: 38.9 (#152)
- Kimi K2.7 Code: 42.4 (#92)

| Benchmark | GLM-4.5V | Kimi K2.7 Code |
|---|---|---|
| DeepSWE | — | 30.5% |
| FrontierCode | — | 30.1% |
| LMArena WebDev | — | 1473 |
| SciCode | — | 47.5% |
| WeirdML | — | 54.1% |
| LMArena Coding | 1347 | — |
| ALE-Bench | — | 886.23 |

## Agentic & Tool Use

- GLM-4.5V: —
- Kimi K2.7 Code: 23.9 (#115)

| Benchmark | GLM-4.5V | Kimi K2.7 Code |
|---|---|---|
| APEX-Agents | — | 37.6% |
| GBAEval | — | 0.9% |
| Vending-Bench 2 | — | 5,083 |

## Reasoning

- GLM-4.5V: 26.5 (#118)
- Kimi K2.7 Code: 38.2 (#56)

| Benchmark | GLM-4.5V | Kimi K2.7 Code |
|---|---|---|
| SimpleBench | — | 57.9% |
| Kagi LLM Benchmark | 59.8% | — |
| CritPt | — | 10% |
| Chess Puzzles | — | 21% |
| LMArena Hard Prompts | 1335 | — |
| Surface Evolver Bench | — | 48.8% |
| Epoch Capabilities Index | — | 150.01 |

## Math

- GLM-4.5V: 34.7 (#157)
- Kimi K2.7 Code: 52.6 (#39)

| Benchmark | GLM-4.5V | Kimi K2.7 Code |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 54% |
| FrontierMath Tier 4 | — | 12.2% |
| OTIS Mock AIME 2024-2025 | — | 95.6% |
| LMArena Math | 1353 | — |

## Knowledge

- GLM-4.5V: 36.8 (#155)
- Kimi K2.7 Code: 53.6 (#51)

| Benchmark | GLM-4.5V | Kimi K2.7 Code |
|---|---|---|
| GPQA Diamond | — | 87.9% |
| SimpleQA Verified | — | 36.5% |
| LMArena Expert | 1353 | — |

## Multimodal

- GLM-4.5V: 33.6 (#86)
- Kimi K2.7 Code: —

| Benchmark | GLM-4.5V | Kimi K2.7 Code |
|---|---|---|
| LMArena Vision | 1154 | — |

## Multilingual

- GLM-4.5V: 44.9 (#172)
- Kimi K2.7 Code: —

| Benchmark | GLM-4.5V | Kimi K2.7 Code |
|---|---|---|
| LMArena Non-English | 1304 | — |
| LMArena Chinese | 1338 | — |
| LMArena Russian | 1299 | — |
| LMArena Spanish | 1335 | — |

## Instruction Following

- GLM-4.5V: 69.4 (#170)
- Kimi K2.7 Code: —

| Benchmark | GLM-4.5V | Kimi K2.7 Code |
|---|---|---|
| LMArena Instruction Following | 1311 | — |

## Long Context

- GLM-4.5V: 39.6 (#166)
- Kimi K2.7 Code: —

| Benchmark | GLM-4.5V | Kimi K2.7 Code |
|---|---|---|
| LMArena Longer Query | 1305 | — |

## Writing & Preference

- GLM-4.5V: 52.7 (#162)
- Kimi K2.7 Code: —

| Benchmark | GLM-4.5V | Kimi K2.7 Code |
|---|---|---|
| LMArena Text | 1333 | — |
| LMArena Creative Writing | 1295 | — |
| LMArena Multi-Turn | 1333 | — |

## FAQ

### Is GLM-4.5V better than Kimi K2.7 Code?

Kimi K2.7 Code is the stronger model overall, scoring 43.0 to 39.2 on the Noometry Index. GLM-4.5V costs 1.9× less per token, which makes it the better buy when Kimi K2.7 Code's lead doesn't matter for your workload.

### Which is cheaper, GLM-4.5V or Kimi K2.7 Code?

GLM-4.5V is cheaper. It lists at $0.60 per million input tokens and $1.80 per million output tokens; Kimi K2.7 Code lists at $0.95 and $4.

### Is GLM-4.5V or Kimi K2.7 Code better for coding?

Kimi K2.7 Code scores higher on coding benchmarks: 42.4 versus 38.9 in the Noometry coding category.

### Which has the bigger context window?

Kimi K2.7 Code does, with 262K tokens against 64K.

### How many benchmarks do GLM-4.5V and Kimi K2.7 Code share?

0 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and Kimi K2.7 Code has 19.
