# GLM-5.2 vs Kimi K2 (Jul 2025)

> GLM-5.2 is the stronger model overall, scoring 50.9 to 40.9 on the Noometry Index. Kimi K2 (Jul 2025) costs 2.1× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/glm-5-2-vs-kimi-k2
- Last updated: 2026-10-10
- Shared benchmarks: 23

## Summary

- They share 23 benchmarks with published results for both. GLM-5.2 scores higher in 8 categories and Kimi K2 (Jul 2025) in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-5.2 leads 57.1 to 36.9.
- The biggest single-benchmark swing is SimpleBench: 58.8% for GLM-5.2 and 26.3% for Kimi K2 (Jul 2025).
- Kimi K2 (Jul 2025) is cheaper at $0.57 / $2.30 per million input/output tokens, against $1.40 / $4.40 for GLM-5.2.
- GLM-5.2 accepts more context: 1M tokens versus 262K.

## Snapshot

| | GLM-5.2 | Kimi K2 (Jul 2025) |
|---|---|---|
| Provider | Z.ai (Zhipu) | Moonshot AI |
| Noometry Index | 50.9 | 40.9 |
| Rank | 38 | 134 |
| Context | 1M | 262K |
| Input $/M | $1.40 | $0.57 |
| Output $/M | $4.40 | $2.30 |
| Weights | Open | Open |

## Coding

- GLM-5.2: 50.9 (#37)
- Kimi K2 (Jul 2025): 43.1 (#83)

| Benchmark | GLM-5.2 | Kimi K2 (Jul 2025) |
|---|---|---|
| WeirdML | 70.1% | 42.8% |
| LMArena Coding | 1484 | 1399 |
| ALE-Bench | 1,047 | 597.5 |
| SWE-bench Verified | 78.7% | — |
| DeepSWE | 43.8% | — |
| FrontierCode | 24.5% | — |
| SWE-bench Verified (bash only) | — | 63.4% |
| Aider Polyglot | — | 59.1% |
| LMArena WebDev | 1604 | — |
| SciCode | 50.5% | — |
| GSO | — | 4.9% |

## Agentic & Tool Use

- GLM-5.2: 31.6 (#63)
- Kimi K2 (Jul 2025): 32.2 (#59)

| Benchmark | GLM-5.2 | Kimi K2 (Jul 2025) |
|---|---|---|
| Terminal-Bench | — | 35.7% |
| Berkeley Function Calling Leaderboard | — | 59.1% |
| τ²-bench Banking | 37.1% | — |
| PostTrainBench | 31.7% | — |
| GBAEval | 0% | — |
| METR Time Horizons | — | 59.2% |
| Vending-Bench 2 | 8,314 | — |

## Reasoning

- GLM-5.2: 42.1 (#46)
- Kimi K2 (Jul 2025): 22.7 (#173)

| Benchmark | GLM-5.2 | Kimi K2 (Jul 2025) |
|---|---|---|
| SimpleBench | 58.8% | 26.3% |
| Kagi LLM Benchmark | 62.6% | 64.4% |
| LMArena Hard Prompts | 1480 | 1384 |
| Epoch Capabilities Index | 151.76 | 146.02 |
| ARC-AGI-2 | 22.8% | — |
| NYT Connections (extended) | 74.3% | — |
| ARC-AGI-1 | 77% | — |
| CritPt | 20.9% | — |
| Chess Puzzles | 21% | — |
| EBR-Bench | 9.5% | — |
| Mystery Game Puzzles | 19% | — |
| DTBench | 93.6% | — |
| LMCA | 45.8% | — |
| Surface Evolver Bench | 55.6% | — |
| ForecastBench | — | 60.2 |

## Math

- GLM-5.2: 55.4 (#35)
- Kimi K2 (Jul 2025): 40.1 (#81)

| Benchmark | GLM-5.2 | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Math | 1482 | 1396 |
| FrontierMath (Tiers 1-3) | 59.2% | — |
| FrontierMath Tier 4 | 29.3% | — |
| MathArena Final-Answer Competitions | 67.6% | — |
| OTIS Mock AIME 2024-2025 | 86.4% | — |
| ProofBench | 35% | — |
| Omni-MATH | — | 65.4% |
| FrontierMath (Feb 2025 set) | — | 21.4% |
| FrontierMath Tier 4 (v1) | — | 0% |

## Knowledge

- GLM-5.2: 57.1 (#36)
- Kimi K2 (Jul 2025): 36.9 (#153)

| Benchmark | GLM-5.2 | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Expert | 1484 | 1363 |
| GPQA Diamond | 91.9% | — |
| SimpleQA Verified | 34.2% | — |
| MMLU-Pro | — | 81.9% |
| Confabulations | — | 20.4% |
| Vectara Hallucination Rate | — | 17.9% |
| GPQA (HELM) | — | 65.3% |

## Multilingual

- GLM-5.2: 56.0 (#25)
- Kimi K2 (Jul 2025): 49.8 (#125)

| Benchmark | GLM-5.2 | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Non-English | 1459 | 1372 |
| LMArena Chinese | 1518 | 1416 |
| LMArena French | 1476 | 1379 |
| LMArena German | 1470 | 1386 |
| LMArena Japanese | 1451 | 1349 |
| LMArena Korean | 1445 | 1324 |
| LMArena Russian | 1467 | 1384 |
| LMArena Spanish | 1477 | 1383 |

## Instruction Following

- GLM-5.2: 77.1 (#32)
- Kimi K2 (Jul 2025): 71.3 (#151)

| Benchmark | GLM-5.2 | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Instruction Following | 1465 | 1347 |
| IFEval | — | 85% |

## Long Context

- GLM-5.2: 45.3 (#43)
- Kimi K2 (Jul 2025): 41.2 (#140)

| Benchmark | GLM-5.2 | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Longer Query | 1479 | 1353 |
| Fiction.LiveBench | — | 66.7% |
| CL-bench | — | 17.6% |

## Writing & Preference

- GLM-5.2: 70.7 (#20)
- Kimi K2 (Jul 2025): 62.5 (#76)

| Benchmark | GLM-5.2 | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Text | 1470 | 1380 |
| LMArena Creative Writing | 1465 | 1349 |
| EQ-Bench Creative Writing | 1757 | 1666 |
| LMArena Multi-Turn | 1468 | 1371 |
| Short-Story Creative Writing | — | 85.6% |
| WildBench | — | 86.2% |
| EQ-Bench 4 | 1222 | — |

## FAQ

### Is GLM-5.2 better than Kimi K2 (Jul 2025)?

GLM-5.2 is the stronger model overall, scoring 50.9 to 40.9 on the Noometry Index. Kimi K2 (Jul 2025) costs 2.1× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.

### Which is cheaper, GLM-5.2 or Kimi K2 (Jul 2025)?

Kimi K2 (Jul 2025) is cheaper. It lists at $0.57 per million input tokens and $2.30 per million output tokens; GLM-5.2 lists at $1.40 and $4.40.

### Is GLM-5.2 or Kimi K2 (Jul 2025) better for coding?

GLM-5.2 scores higher on coding benchmarks: 50.9 versus 43.1 in the Noometry coding category.

### Which has the bigger context window?

GLM-5.2 does, with 1M tokens against 262K.

### How many benchmarks do GLM-5.2 and Kimi K2 (Jul 2025) share?

23 benchmarks have published results for both models. GLM-5.2 has 50 scored results on Noometry and Kimi K2 (Jul 2025) has 42.