# GLM-5V-Turbo vs Step 3.5 Flash

> GLM-5V-Turbo is the stronger model overall, scoring 43.1 to 41.7 on the Noometry Index. Step 3.5 Flash costs 13× less per token, which makes it the better buy when GLM-5V-Turbo's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/glm-5v-turbo-vs-step-3-5-flash
- Last updated: 2026-10-10
- Shared benchmarks: 16

## Summary

- They share 16 benchmarks with published results for both. GLM-5V-Turbo scores higher in 7 categories and Step 3.5 Flash in 1 category; 6 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5V-Turbo leads 28.5 to 21.2.
- Step 3.5 Flash is cheaper at $0.10 / $0.30 per million input/output tokens, against $1.20 / $4 for GLM-5V-Turbo.
- Step 3.5 Flash accepts more context: 256K tokens versus 200K.
- Step 3.5 Flash has downloadable open weights; the other is API-only.

## Snapshot

| | GLM-5V-Turbo | Step 3.5 Flash |
|---|---|---|
| Provider | Z.ai (Zhipu) | StepFun |
| Noometry Index | 43.1 | 41.7 |
| Rank | 86 | 116 |
| Context | 200K | 256K |
| Input $/M | $1.20 | $0.10 |
| Output $/M | $4 | $0.30 |
| Weights | Proprietary | Open |

## Coding

- GLM-5V-Turbo: 41.8 (#105)
- Step 3.5 Flash: 41.7 (#106)

| Benchmark | GLM-5V-Turbo | Step 3.5 Flash |
|---|---|---|
| LMArena Coding | 1467 | 1436 |
| LMArena WebDev | 1400 | — |

## Reasoning

- GLM-5V-Turbo: 28.5 (#87)
- Step 3.5 Flash: 21.2 (#202)

| Benchmark | GLM-5V-Turbo | Step 3.5 Flash |
|---|---|---|
| LMArena Hard Prompts | 1443 | 1411 |
| NYT Connections (extended) | — | 28.4% |

## Math

- GLM-5V-Turbo: 36.6 (#109)
- Step 3.5 Flash: 40.6 (#77)

| Benchmark | GLM-5V-Turbo | Step 3.5 Flash |
|---|---|---|
| LMArena Math | 1440 | 1408 |
| MathArena Final-Answer Competitions | — | 66.8% |

## Knowledge

- GLM-5V-Turbo: 39.9 (#113)
- Step 3.5 Flash: 39.0 (#127)

| Benchmark | GLM-5V-Turbo | Step 3.5 Flash |
|---|---|---|
| LMArena Expert | 1451 | 1422 |

## Multimodal

- GLM-5V-Turbo: 40.1 (#41)
- Step 3.5 Flash: —

| Benchmark | GLM-5V-Turbo | Step 3.5 Flash |
|---|---|---|
| LMArena Vision | 1264 | — |
| LMArena Document | 1416 | — |

## Multilingual

- GLM-5V-Turbo: 53.2 (#69)
- Step 3.5 Flash: 50.7 (#115)

| Benchmark | GLM-5V-Turbo | Step 3.5 Flash |
|---|---|---|
| LMArena Non-English | 1420 | 1386 |
| LMArena Chinese | 1488 | 1448 |
| LMArena French | 1443 | 1421 |
| LMArena German | 1422 | 1404 |
| LMArena Korean | 1399 | 1350 |
| LMArena Russian | 1432 | 1385 |
| LMArena Spanish | 1446 | 1419 |
| LMArena Japanese | — | 1353 |

## Instruction Following

- GLM-5V-Turbo: 75.1 (#75)
- Step 3.5 Flash: 73.3 (#119)

| Benchmark | GLM-5V-Turbo | Step 3.5 Flash |
|---|---|---|
| LMArena Instruction Following | 1422 | 1385 |

## Long Context

- GLM-5V-Turbo: 44.0 (#76)
- Step 3.5 Flash: 42.8 (#112)

| Benchmark | GLM-5V-Turbo | Step 3.5 Flash |
|---|---|---|
| LMArena Longer Query | 1439 | 1402 |

## Writing & Preference

- GLM-5V-Turbo: 62.6 (#69)
- Step 3.5 Flash: 59.0 (#108)

| Benchmark | GLM-5V-Turbo | Step 3.5 Flash |
|---|---|---|
| LMArena Text | 1437 | 1403 |
| LMArena Creative Writing | 1414 | 1357 |
| LMArena Multi-Turn | 1432 | 1405 |

## FAQ

### Is GLM-5V-Turbo better than Step 3.5 Flash?

GLM-5V-Turbo is the stronger model overall, scoring 43.1 to 41.7 on the Noometry Index. Step 3.5 Flash costs 13× less per token, which makes it the better buy when GLM-5V-Turbo's lead doesn't matter for your workload.

### Which is cheaper, GLM-5V-Turbo or Step 3.5 Flash?

Step 3.5 Flash is cheaper. It lists at $0.10 per million input tokens and $0.30 per million output tokens; GLM-5V-Turbo lists at $1.20 and $4.

### Is GLM-5V-Turbo or Step 3.5 Flash better for coding?

They score almost the same on coding (41.8 vs 41.7); test both on your own repository before choosing.

### Which has the bigger context window?

Step 3.5 Flash does, with 256K tokens against 200K.

### How many benchmarks do GLM-5V-Turbo and Step 3.5 Flash share?

16 benchmarks have published results for both models. GLM-5V-Turbo has 19 scored results on Noometry and Step 3.5 Flash has 19.
