# GLM-4.7 vs Kimi K2.7 Code

> Kimi K2.7 Code is the stronger model overall, scoring 43.0 to 41.7 on the Noometry Index. GLM-4.7 costs 1.7× 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-4-7-vs-kimi-k2-7-code
- Last updated: 2026-10-10
- Shared benchmarks: 11

## Summary

- They share 11 benchmarks with published results for both. GLM-4.7 scores higher in 2 categories and Kimi K2.7 Code in 3 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in math, where Kimi K2.7 Code leads 52.6 to 37.7.
- The biggest single-benchmark swing is Chess Puzzles: 6% for GLM-4.7 and 21% for Kimi K2.7 Code.
- GLM-4.7 is cheaper at $0.60 / $2.20 per million input/output tokens, against $0.95 / $4 for Kimi K2.7 Code.
- Kimi K2.7 Code accepts more context: 262K tokens versus 205K.

## Snapshot

| | GLM-4.7 | Kimi K2.7 Code |
|---|---|---|
| Provider | Z.ai (Zhipu) | Moonshot AI |
| Noometry Index | 41.7 | 43.0 |
| Rank | 117 | 90 |
| Context | 205K | 262K |
| Input $/M | $0.60 | $0.95 |
| Output $/M | $2.20 | $4 |
| Weights | Open | Open |

## Coding

- GLM-4.7: 43.6 (#74)
- Kimi K2.7 Code: 42.4 (#92)

| Benchmark | GLM-4.7 | Kimi K2.7 Code |
|---|---|---|
| LMArena WebDev | 1435 | 1473 |
| SciCode | 45.1% | 47.5% |
| ALE-Bench | 399.48 | 886.23 |
| DeepSWE | — | 30.5% |
| FrontierCode | — | 30.1% |
| WeirdML | — | 54.1% |
| LMArena Coding | 1454 | — |

## Agentic & Tool Use

- GLM-4.7: 26.4 (#96)
- Kimi K2.7 Code: 23.9 (#115)

| Benchmark | GLM-4.7 | Kimi K2.7 Code |
|---|---|---|
| Vending-Bench 2 | 2,377 | 5,083 |
| Terminal-Bench | 33.4% | — |
| APEX-Agents | — | 37.6% |
| GBAEval | — | 0.9% |

## Reasoning

- GLM-4.7: 23.7 (#161)
- Kimi K2.7 Code: 38.2 (#56)

| Benchmark | GLM-4.7 | Kimi K2.7 Code |
|---|---|---|
| SimpleBench | 47.7% | 57.9% |
| CritPt | 1.7% | 10% |
| Chess Puzzles | 6% | 21% |
| Epoch Capabilities Index | 143.53 | 150.01 |
| LMArena Hard Prompts | 1443 | — |
| Surface Evolver Bench | — | 48.8% |

## Math

- GLM-4.7: 37.7 (#92)
- Kimi K2.7 Code: 52.6 (#39)

| Benchmark | GLM-4.7 | Kimi K2.7 Code |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 83.3% | 95.6% |
| FrontierMath (Tiers 1-3) | — | 54% |
| FrontierMath Tier 4 | — | 12.2% |
| ProofBench | 6% | — |
| LMArena Math | 1423 | — |
| FrontierMath (Feb 2025 set) | 2.4% | — |
| FrontierMath Tier 4 (v1) | 0% | — |

## Knowledge

- GLM-4.7: 47.0 (#74)
- Kimi K2.7 Code: 53.6 (#51)

| Benchmark | GLM-4.7 | Kimi K2.7 Code |
|---|---|---|
| GPQA Diamond | 83.3% | 87.9% |
| SimpleQA Verified | 32.2% | 36.5% |
| Vectara Hallucination Rate | 11.7% | — |
| LMArena Expert | 1424 | — |

## Multilingual

- GLM-4.7: 53.0 (#75)
- Kimi K2.7 Code: —

| Benchmark | GLM-4.7 | Kimi K2.7 Code |
|---|---|---|
| LMArena Non-English | 1417 | — |
| LMArena Chinese | 1495 | — |
| LMArena French | 1432 | — |
| LMArena German | 1426 | — |
| LMArena Japanese | 1439 | — |
| LMArena Korean | 1401 | — |
| LMArena Russian | 1423 | — |
| LMArena Spanish | 1433 | — |

## Instruction Following

- GLM-4.7: 74.6 (#90)
- Kimi K2.7 Code: —

| Benchmark | GLM-4.7 | Kimi K2.7 Code |
|---|---|---|
| LMArena Instruction Following | 1411 | — |

## Long Context

- GLM-4.7: 42.8 (#111)
- Kimi K2.7 Code: —

| Benchmark | GLM-4.7 | Kimi K2.7 Code |
|---|---|---|
| CL-bench | 15.9% | — |
| CL-bench Life | 10.9% | — |
| LMArena Longer Query | 1432 | — |

## Writing & Preference

- GLM-4.7: 61.1 (#89)
- Kimi K2.7 Code: —

| Benchmark | GLM-4.7 | Kimi K2.7 Code |
|---|---|---|
| LMArena Text | 1436 | — |
| LMArena Creative Writing | 1401 | — |
| EQ-Bench Creative Writing | 1413 | — |
| LMArena Multi-Turn | 1446 | — |

## FAQ

### Is GLM-4.7 better than Kimi K2.7 Code?

Kimi K2.7 Code is the stronger model overall, scoring 43.0 to 41.7 on the Noometry Index. GLM-4.7 costs 1.7× 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-4.7 or Kimi K2.7 Code?

GLM-4.7 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; Kimi K2.7 Code lists at $0.95 and $4.

### Is GLM-4.7 or Kimi K2.7 Code better for coding?

GLM-4.7 scores higher on coding benchmarks: 43.6 versus 42.4 in the Noometry coding category.

### Which has the bigger context window?

Kimi K2.7 Code does, with 262K tokens against 205K.

### How many benchmarks do GLM-4.7 and Kimi K2.7 Code share?

11 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and Kimi K2.7 Code has 19.