# GLM-4.7-Flash vs Kimi K2 (Jul 2025)

> Kimi K2 (Jul 2025) is the stronger model overall, scoring 40.9 to 38.4 on the Noometry Index. GLM-4.7-Flash costs 6.9× less per token, which makes it the better buy when Kimi K2 (Jul 2025)'s lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/glm-4-7-flash-vs-kimi-k2
- Last updated: 2026-10-10
- Shared benchmarks: 18

## Summary

- They share 18 benchmarks with published results for both. GLM-4.7-Flash scores higher in 0 categories and Kimi K2 (Jul 2025) in 8 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Kimi K2 (Jul 2025) leads 62.5 to 47.7.
- The biggest single-benchmark swing is Vectara Hallucination Rate: 9.3% for GLM-4.7-Flash and 17.9% for Kimi K2 (Jul 2025).
- GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $0.57 / $2.30 for Kimi K2 (Jul 2025).
- Kimi K2 (Jul 2025) accepts more context: 262K tokens versus 200K.

## Snapshot

| | GLM-4.7-Flash | Kimi K2 (Jul 2025) |
|---|---|---|
| Provider | Z.ai (Zhipu) | Moonshot AI |
| Noometry Index | 38.4 | 40.9 |
| Rank | 175 | 134 |
| Context | 200K | 262K |
| Input $/M | $0.06 | $0.57 |
| Output $/M | $0.40 | $2.30 |
| Weights | Open | Open |

## Coding

- GLM-4.7-Flash: 40.0 (#133)
- Kimi K2 (Jul 2025): 43.1 (#83)

| Benchmark | GLM-4.7-Flash | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Coding | 1383 | 1399 |
| SWE-bench Verified (bash only) | — | 63.4% |
| Aider Polyglot | — | 59.1% |
| GSO | — | 4.9% |
| WeirdML | — | 42.8% |
| ALE-Bench | — | 597.5 |

## Agentic & Tool Use

- GLM-4.7-Flash: —
- Kimi K2 (Jul 2025): 32.2 (#59)

| Benchmark | GLM-4.7-Flash | Kimi K2 (Jul 2025) |
|---|---|---|
| Terminal-Bench | — | 35.7% |
| Berkeley Function Calling Leaderboard | — | 59.1% |
| METR Time Horizons | — | 59.2% |

## Reasoning

- GLM-4.7-Flash: 20.2 (#224)
- Kimi K2 (Jul 2025): 22.7 (#173)

| Benchmark | GLM-4.7-Flash | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Hard Prompts | 1356 | 1384 |
| SimpleBench | — | 26.3% |
| Kagi LLM Benchmark | — | 64.4% |
| Chess Puzzles | 0% | — |
| Epoch Capabilities Index | — | 146.02 |
| ForecastBench | — | 60.2 |

## Math

- GLM-4.7-Flash: 34.4 (#159)
- Kimi K2 (Jul 2025): 40.1 (#81)

| Benchmark | GLM-4.7-Flash | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Math | 1354 | 1396 |
| OTIS Mock AIME 2024-2025 | 58.3% | — |
| Omni-MATH | — | 65.4% |
| FrontierMath (Feb 2025 set) | — | 21.4% |
| FrontierMath Tier 4 (v1) | — | 0% |

## Knowledge

- GLM-4.7-Flash: 35.4 (#177)
- Kimi K2 (Jul 2025): 36.9 (#153)

| Benchmark | GLM-4.7-Flash | Kimi K2 (Jul 2025) |
|---|---|---|
| Vectara Hallucination Rate | 9.3% | 17.9% |
| LMArena Expert | 1357 | 1363 |
| GPQA Diamond | 60.5% | — |
| MMLU-Pro | — | 81.9% |
| Confabulations | — | 20.4% |
| GPQA (HELM) | — | 65.3% |

## Multilingual

- GLM-4.7-Flash: 46.8 (#152)
- Kimi K2 (Jul 2025): 49.8 (#125)

| Benchmark | GLM-4.7-Flash | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Non-English | 1331 | 1372 |
| LMArena Chinese | 1403 | 1416 |
| LMArena French | 1332 | 1379 |
| LMArena German | 1336 | 1386 |
| LMArena Korean | 1284 | 1324 |
| LMArena Russian | 1333 | 1384 |
| LMArena Spanish | 1351 | 1383 |
| LMArena Japanese | — | 1349 |

## Instruction Following

- GLM-4.7-Flash: 70.3 (#162)
- Kimi K2 (Jul 2025): 71.3 (#151)

| Benchmark | GLM-4.7-Flash | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Instruction Following | 1327 | 1347 |
| IFEval | — | 85% |

## Long Context

- GLM-4.7-Flash: 40.9 (#143)
- Kimi K2 (Jul 2025): 41.2 (#140)

| Benchmark | GLM-4.7-Flash | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Longer Query | 1345 | 1353 |
| Fiction.LiveBench | — | 66.7% |
| CL-bench | — | 17.6% |

## Writing & Preference

- GLM-4.7-Flash: 47.7 (#206)
- Kimi K2 (Jul 2025): 62.5 (#76)

| Benchmark | GLM-4.7-Flash | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Text | 1351 | 1380 |
| LMArena Creative Writing | 1297 | 1349 |
| EQ-Bench Creative Writing | 1125 | 1666 |
| LMArena Multi-Turn | 1342 | 1371 |
| Short-Story Creative Writing | — | 85.6% |
| WildBench | — | 86.2% |

## FAQ

### Is GLM-4.7-Flash better than Kimi K2 (Jul 2025)?

Kimi K2 (Jul 2025) is the stronger model overall, scoring 40.9 to 38.4 on the Noometry Index. GLM-4.7-Flash costs 6.9× less per token, which makes it the better buy when Kimi K2 (Jul 2025)'s lead doesn't matter for your workload.

### Which is cheaper, GLM-4.7-Flash or Kimi K2 (Jul 2025)?

GLM-4.7-Flash is cheaper. It lists at $0.06 per million input tokens and $0.40 per million output tokens; Kimi K2 (Jul 2025) lists at $0.57 and $2.30.

### Is GLM-4.7-Flash or Kimi K2 (Jul 2025) better for coding?

Kimi K2 (Jul 2025) scores higher on coding benchmarks: 43.1 versus 40.0 in the Noometry coding category.

### Which has the bigger context window?

Kimi K2 (Jul 2025) does, with 262K tokens against 200K.

### How many benchmarks do GLM-4.7-Flash and Kimi K2 (Jul 2025) share?

18 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and Kimi K2 (Jul 2025) has 42.