# GLM-4.7-Flash vs GLM-5.2

> GLM-5.2 is the stronger model overall, scoring 51.1 to 38.8 on the Noometry Index. GLM-4.7-Flash costs 15× 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-4-7-flash-vs-glm-5-2
- Last updated: 2026-10-10
- Shared benchmarks: 20

## Summary

- They share 20 benchmarks with published results for both. GLM-4.7-Flash scores higher in 0 categories and GLM-5.2 in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-5.2 leads 70.4 to 47.4.
- The biggest single-benchmark swing is GPQA Diamond: 60.5% for GLM-4.7-Flash and 91.9% for GLM-5.2.
- GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $1.40 / $4.40 for GLM-5.2.
- GLM-5.2 accepts more context: 1M tokens versus 200K.

## Snapshot

| | GLM-4.7-Flash | GLM-5.2 |
|---|---|---|
| Provider | Z.ai (Zhipu) | Z.ai (Zhipu) |
| Noometry Index | 38.8 | 51.1 |
| Rank | 180 | 44 |
| Context | 200K | 1M |
| Input $/M | $0.06 | $1.40 |
| Output $/M | $0.40 | $4.40 |
| Weights | Open | Open |

## Coding

- GLM-4.7-Flash: 40.6 (#135)
- GLM-5.2: 51.3 (#41)

| Benchmark | GLM-4.7-Flash | GLM-5.2 |
|---|---|---|
| LMArena Coding | 1383 | 1485 |
| SWE-bench Verified | — | 78.7% |
| DeepSWE | — | 43.8% |
| FrontierCode | — | 24.5% |
| LMArena WebDev | — | 1603 |
| SciCode | — | 50.5% |
| WeirdML | — | 70.1% |
| ALE-Bench | — | 1,047 |

## Agentic & Tool Use

- GLM-4.7-Flash: —
- GLM-5.2: 32.4 (#63)

| Benchmark | GLM-4.7-Flash | GLM-5.2 |
|---|---|---|
| APEX-Agents | — | 45.2% |
| τ²-bench Banking | — | 37.1% |
| PostTrainBench | — | 31.7% |
| GBAEval | — | 0% |
| Vending-Bench 2 | — | 8,314 |

## Reasoning

- GLM-4.7-Flash: 20.9 (#229)
- GLM-5.2: 42.3 (#52)

| Benchmark | GLM-4.7-Flash | GLM-5.2 |
|---|---|---|
| Chess Puzzles | 0% | 21% |
| LMArena Hard Prompts | 1356 | 1480 |
| ARC-AGI-2 | — | 22.8% |
| SimpleBench | — | 58.8% |
| Kagi LLM Benchmark | — | 62.6% |
| NYT Connections (extended) | — | 74.3% |
| ARC-AGI-1 | — | 77% |
| CritPt | — | 20.9% |
| EBR-Bench | — | 9.5% |
| Mystery Game Puzzles | — | 19% |
| DTBench | — | 93.6% |
| LMCA | — | 45.8% |
| Surface Evolver Bench | — | 55.6% |
| Epoch Capabilities Index | — | 151.78 |

## Math

- GLM-4.7-Flash: 36.1 (#173)
- GLM-5.2: 55.7 (#43)

| Benchmark | GLM-4.7-Flash | GLM-5.2 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 58.3% | 86.4% |
| LMArena Math | 1355 | 1482 |
| FrontierMath (Tiers 1-3) | — | 59.2% |
| FrontierMath Tier 4 | — | 29.3% |
| MathArena Final-Answer Competitions | — | 67.6% |
| ProofBench | — | 35% |

## Knowledge

- GLM-4.7-Flash: 35.5 (#184)
- GLM-5.2: 57.1 (#40)

| Benchmark | GLM-4.7-Flash | GLM-5.2 |
|---|---|---|
| GPQA Diamond | 60.5% | 91.9% |
| LMArena Expert | 1357 | 1486 |
| SimpleQA Verified | — | 34.2% |
| Vectara Hallucination Rate | 9.3% | — |

## Multilingual

- GLM-4.7-Flash: 46.5 (#158)
- GLM-5.2: 55.8 (#26)

| Benchmark | GLM-4.7-Flash | GLM-5.2 |
|---|---|---|
| LMArena Non-English | 1330 | 1459 |
| LMArena Chinese | 1403 | 1519 |
| LMArena French | 1332 | 1479 |
| LMArena German | 1337 | 1468 |
| LMArena Korean | 1283 | 1445 |
| LMArena Russian | 1332 | 1466 |
| LMArena Spanish | 1350 | 1477 |
| LMArena Japanese | — | 1451 |

## Instruction Following

- GLM-4.7-Flash: 70.1 (#167)
- GLM-5.2: 76.9 (#34)

| Benchmark | GLM-4.7-Flash | GLM-5.2 |
|---|---|---|
| LMArena Instruction Following | 1327 | 1465 |

## Long Context

- GLM-4.7-Flash: 40.9 (#148)
- GLM-5.2: 45.3 (#43)

| Benchmark | GLM-4.7-Flash | GLM-5.2 |
|---|---|---|
| LMArena Longer Query | 1345 | 1479 |

## Writing & Preference

- GLM-4.7-Flash: 47.4 (#210)
- GLM-5.2: 70.4 (#21)

| Benchmark | GLM-4.7-Flash | GLM-5.2 |
|---|---|---|
| LMArena Text | 1351 | 1470 |
| LMArena Creative Writing | 1297 | 1462 |
| EQ-Bench Creative Writing | 1125 | 1757 |
| LMArena Multi-Turn | 1342 | 1469 |
| EQ-Bench 4 | — | 1222 |

## FAQ

### Is GLM-4.7-Flash better than GLM-5.2?

GLM-5.2 is the stronger model overall, scoring 51.1 to 38.8 on the Noometry Index. GLM-4.7-Flash costs 15× 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-4.7-Flash or GLM-5.2?

GLM-4.7-Flash is cheaper. It lists at $0.06 per million input tokens and $0.40 per million output tokens; GLM-5.2 lists at $1.40 and $4.40.

### Is GLM-4.7-Flash or GLM-5.2 better for coding?

GLM-5.2 scores higher on coding benchmarks: 51.3 versus 40.6 in the Noometry coding category.

### Which has the bigger context window?

GLM-5.2 does, with 1M tokens against 200K.

### How many benchmarks do GLM-4.7-Flash and GLM-5.2 share?

20 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and GLM-5.2 has 51.