# GLM-4.6 vs Kimi K2.6

> Kimi K2.6 is the stronger model overall, scoring 47.5 to 40.9 on the Noometry Index. GLM-4.6 costs 1.7× less per token, which makes it the better buy when Kimi K2.6's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/glm-4-6-vs-kimi-k2-6
- Last updated: 2026-10-10
- Shared benchmarks: 25

## Summary

- They share 25 benchmarks with published results for both. GLM-4.6 scores higher in 1 category and Kimi K2.6 in 8 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where Kimi K2.6 leads 56.8 to 36.4.
- The biggest single-benchmark swing is SciCode: 38.4% for GLM-4.6 and 53.5% for Kimi K2.6.
- GLM-4.6 is cheaper at $0.60 / $2.20 per million input/output tokens, against $0.95 / $4 for Kimi K2.6.
- Kimi K2.6 accepts more context: 262K tokens versus 205K.

## Snapshot

| | GLM-4.6 | Kimi K2.6 |
|---|---|---|
| Provider | Z.ai (Zhipu) | Moonshot AI |
| Noometry Index | 40.9 | 47.5 |
| Rank | 132 | 52 |
| Context | 205K | 262K |
| Input $/M | $0.60 | $0.95 |
| Output $/M | $2.20 | $4 |
| Weights | Open | Open |

## Coding

- GLM-4.6: 39.9 (#137)
- Kimi K2.6: 50.5 (#41)

| Benchmark | GLM-4.6 | Kimi K2.6 |
|---|---|---|
| LMArena WebDev | 1339 | 1509 |
| SciCode | 38.4% | 53.5% |
| LMArena Coding | 1449 | 1488 |
| ALE-Bench | 340.82 | 1,093 |
| SWE-bench Verified | — | 76.7% |
| SWE-bench Verified (bash only) | 55.4% | — |
| WeirdML | — | 55.9% |

## Agentic & Tool Use

- GLM-4.6: 32.1 (#60)
- Kimi K2.6: 21.8 (#128)

| Benchmark | GLM-4.6 | Kimi K2.6 |
|---|---|---|
| Terminal-Bench | 24.5% | — |
| Berkeley Function Calling Leaderboard | 72.4% | — |
| OSWorld 2.0 | — | 4.6% |
| ExploitBench | — | 18.4% |
| GBAEval | — | 0.9% |
| GDP.pdf | — | 12% |
| Vending-Bench 2 | — | 6,205 |

## Reasoning

- GLM-4.6: 23.1 (#168)
- Kimi K2.6: 39.6 (#51)

| Benchmark | GLM-4.6 | Kimi K2.6 |
|---|---|---|
| CritPt | 1.1% | 8% |
| LMArena Hard Prompts | 1440 | 1470 |
| Kagi LLM Benchmark | 47.4% | — |
| NYT Connections (extended) | — | 87.2% |
| Chess Puzzles | — | 26% |
| EBR-Bench | — | 2.4% |
| Mystery Game Puzzles | — | 18% |
| DTBench | — | 90.9% |
| LMCA | — | 37.3% |
| Epoch Capabilities Index | — | 151.03 |

## Math

- GLM-4.6: 36.4 (#115)
- Kimi K2.6: 56.8 (#34)

| Benchmark | GLM-4.6 | Kimi K2.6 |
|---|---|---|
| LMArena Math | 1431 | 1475 |
| FrontierMath (Feb 2025 set) | 3.8% | 39% |
| FrontierMath Tier 4 (v1) | 2.1% | 14.6% |
| FrontierMath (Tiers 1-3) | — | 57.2% |
| FrontierMath Tier 4 | — | 25.6% |
| MathArena Final-Answer Competitions | — | 72.9% |
| OTIS Mock AIME 2024-2025 | — | 96.1% |
| ProofBench | — | 16% |

## Knowledge

- GLM-4.6: 39.6 (#118)
- Kimi K2.6: 53.9 (#48)

| Benchmark | GLM-4.6 | Kimi K2.6 |
|---|---|---|
| Vectara Hallucination Rate | 9.5% | 10.8% |
| LMArena Expert | 1432 | 1491 |
| GPQA Diamond | — | 90.8% |
| SimpleQA Verified | — | 34.9% |

## Multimodal

- GLM-4.6: —
- Kimi K2.6: 31.8 (#95)

| Benchmark | GLM-4.6 | Kimi K2.6 |
|---|---|---|
| LMArena Vision | — | 1282 |
| Blueprint-Bench 2 | — | 3.9% |
| Furniture Assembly | — | 21.7% |
| LMArena Document | — | 1451 |

## Multilingual

- GLM-4.6: 53.6 (#62)
- Kimi K2.6: 55.1 (#35)

| Benchmark | GLM-4.6 | Kimi K2.6 |
|---|---|---|
| LMArena Non-English | 1426 | 1446 |
| LMArena Chinese | 1500 | 1521 |
| LMArena French | 1459 | 1471 |
| LMArena German | 1447 | 1449 |
| LMArena Japanese | 1394 | 1442 |
| LMArena Korean | 1401 | 1428 |
| LMArena Russian | 1418 | 1446 |
| LMArena Spanish | 1436 | 1463 |

## Instruction Following

- GLM-4.6: 74.5 (#93)
- Kimi K2.6: 76.5 (#40)

| Benchmark | GLM-4.6 | Kimi K2.6 |
|---|---|---|
| LMArena Instruction Following | 1410 | 1451 |

## Long Context

- GLM-4.6: 43.4 (#91)
- Kimi K2.6: 44.9 (#50)

| Benchmark | GLM-4.6 | Kimi K2.6 |
|---|---|---|
| LMArena Longer Query | 1423 | 1468 |

## Writing & Preference

- GLM-4.6: 61.3 (#86)
- Kimi K2.6: 68.7 (#25)

| Benchmark | GLM-4.6 | Kimi K2.6 |
|---|---|---|
| LMArena Text | 1440 | 1455 |
| LMArena Creative Writing | 1411 | 1434 |
| EQ-Bench Creative Writing | 1411 | 1725 |
| LMArena Multi-Turn | 1427 | 1453 |
| EQ-Bench 4 | — | 1202 |

## FAQ

### Is GLM-4.6 better than Kimi K2.6?

Kimi K2.6 is the stronger model overall, scoring 47.5 to 40.9 on the Noometry Index. GLM-4.6 costs 1.7× less per token, which makes it the better buy when Kimi K2.6's lead doesn't matter for your workload.

### Which is cheaper, GLM-4.6 or Kimi K2.6?

GLM-4.6 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; Kimi K2.6 lists at $0.95 and $4.

### Is GLM-4.6 or Kimi K2.6 better for coding?

Kimi K2.6 scores higher on coding benchmarks: 50.5 versus 39.9 in the Noometry coding category.

### Which has the bigger context window?

Kimi K2.6 does, with 262K tokens against 205K.

### How many benchmarks do GLM-4.6 and Kimi K2.6 share?

25 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Kimi K2.6 has 51.