# Gemini 2.5 Flash vs GLM-5V-Turbo

> GLM-5V-Turbo is the stronger model overall, scoring 43.8 to 39.3 on the Noometry Index. Gemini 2.5 Flash costs 2.2× 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/gemini-2-5-flash-vs-glm-5v-turbo
- Last updated: 2026-10-10
- Shared benchmarks: 17

## Summary

- They share 17 benchmarks with published results for both. Gemini 2.5 Flash scores higher in 4 categories and GLM-5V-Turbo in 5 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5V-Turbo leads 29.7 to 18.1.
- Gemini 2.5 Flash is cheaper at $0.30 / $2.50 per million input/output tokens, against $1.20 / $4 for GLM-5V-Turbo.
- Gemini 2.5 Flash accepts more context: 1.05M tokens versus 200K.

## Snapshot

| | Gemini 2.5 Flash | GLM-5V-Turbo |
|---|---|---|
| Provider | Google | Z.ai (Zhipu) |
| Noometry Index | 39.3 | 43.8 |
| Rank | 170 | 84 |
| Context | 1.05M | 200K |
| Input $/M | $0.30 | $1.20 |
| Output $/M | $2.50 | $4 |
| Weights | Proprietary | Proprietary |

## Coding

- Gemini 2.5 Flash: 35.8 (#220)
- GLM-5V-Turbo: 42.1 (#111)

| Benchmark | Gemini 2.5 Flash | GLM-5V-Turbo |
|---|---|---|
| LMArena Coding | 1424 | 1466 |
| SWE-bench Verified (bash only) | 28.7% | — |
| Aider Polyglot | 55.1% | — |
| LMArena WebDev | — | 1401 |
| WeirdML | 41.9% | — |
| ALE-Bench | 661.88 | — |

## Agentic & Tool Use

- Gemini 2.5 Flash: 30.8 (#74)
- GLM-5V-Turbo: —

| Benchmark | Gemini 2.5 Flash | GLM-5V-Turbo |
|---|---|---|
| Terminal-Bench | 17.1% | — |
| Berkeley Function Calling Leaderboard | 56.2% | — |
| TheAgentCompany | 41.1% | — |
| BALROG | 33.5% | — |
| Vending-Bench 2 | 548.84 | — |

## Reasoning

- Gemini 2.5 Flash: 18.1 (#286)
- GLM-5V-Turbo: 29.7 (#89)

| Benchmark | Gemini 2.5 Flash | GLM-5V-Turbo |
|---|---|---|
| LMArena Hard Prompts | 1422 | 1443 |
| ARC-AGI-2 | 2.5% | — |
| SimpleBench | 41.2% | — |
| Kagi LLM Benchmark | 56.8% | — |
| ARC-AGI-1 | 33.3% | — |
| CritPt | 1.1% | — |
| EnigmaEval | 2.7% | — |
| DTBench | 76.5% | — |
| LMCA | 27.5% | — |
| Epoch Capabilities Index | 143.03 | — |
| ForecastBench | 60.6 | — |

## Math

- Gemini 2.5 Flash: 39.9 (#98)
- GLM-5V-Turbo: 39.4 (#106)

| Benchmark | Gemini 2.5 Flash | GLM-5V-Turbo |
|---|---|---|
| LMArena Math | 1415 | 1441 |
| OTIS Mock AIME 2024-2025 | 73.1% | — |
| Omni-MATH | 38.5% | — |
| FrontierMath (Feb 2025 set) | 4.8% | — |
| FrontierMath Tier 4 (v1) | 4.2% | — |

## Knowledge

- Gemini 2.5 Flash: 36.4 (#168)
- GLM-5V-Turbo: 40.6 (#117)

| Benchmark | Gemini 2.5 Flash | GLM-5V-Turbo |
|---|---|---|
| LMArena Expert | 1426 | 1452 |
| Humanity's Last Exam | 12.1% | — |
| MMLU-Pro | 63.9% | — |
| Confabulations | 16.8% | — |
| Vectara Hallucination Rate | 7.8% | — |
| GPQA (HELM) | 39% | — |

## Multimodal

- Gemini 2.5 Flash: 41.8 (#32)
- GLM-5V-Turbo: 40.9 (#42)

| Benchmark | Gemini 2.5 Flash | GLM-5V-Turbo |
|---|---|---|
| LMArena Vision | 1253 | 1264 |
| GeoBench | 76% | — |
| VPCT | 46.2% | — |
| LMArena Document | — | 1416 |
| SpatialViz-Bench | 36.9% | — |

## Multilingual

- Gemini 2.5 Flash: 52.3 (#88)
- GLM-5V-Turbo: 53.0 (#73)

| Benchmark | Gemini 2.5 Flash | GLM-5V-Turbo |
|---|---|---|
| LMArena Non-English | 1409 | 1420 |
| LMArena Chinese | 1450 | 1488 |
| LMArena French | 1433 | 1444 |
| LMArena German | 1418 | 1423 |
| LMArena Korean | 1385 | 1396 |
| LMArena Russian | 1415 | 1431 |
| LMArena Spanish | 1421 | 1450 |
| LMArena Japanese | 1405 | — |

## Instruction Following

- Gemini 2.5 Flash: 75.7 (#54)
- GLM-5V-Turbo: 75.0 (#80)

| Benchmark | Gemini 2.5 Flash | GLM-5V-Turbo |
|---|---|---|
| LMArena Instruction Following | 1405 | 1423 |
| IFEval | 89.8% | — |

## Long Context

- Gemini 2.5 Flash: 47.5 (#17)
- GLM-5V-Turbo: 44.0 (#80)

| Benchmark | Gemini 2.5 Flash | GLM-5V-Turbo |
|---|---|---|
| LMArena Longer Query | 1419 | 1438 |
| Fiction.LiveBench | 77.8% | — |

## Writing & Preference

- Gemini 2.5 Flash: 53.8 (#157)
- GLM-5V-Turbo: 62.5 (#73)

| Benchmark | Gemini 2.5 Flash | GLM-5V-Turbo |
|---|---|---|
| LMArena Text | 1417 | 1437 |
| LMArena Creative Writing | 1400 | 1416 |
| LMArena Multi-Turn | 1408 | 1432 |
| Short-Story Creative Writing | 76.5% | — |
| EQ-Bench Creative Writing | 1137 | — |
| WildBench | 81.7% | — |

## FAQ

### Is Gemini 2.5 Flash better than GLM-5V-Turbo?

GLM-5V-Turbo is the stronger model overall, scoring 43.8 to 39.3 on the Noometry Index. Gemini 2.5 Flash costs 2.2× less per token, which makes it the better buy when GLM-5V-Turbo's lead doesn't matter for your workload.

### Which is cheaper, Gemini 2.5 Flash or GLM-5V-Turbo?

Gemini 2.5 Flash is cheaper. It lists at $0.30 per million input tokens and $2.50 per million output tokens; GLM-5V-Turbo lists at $1.20 and $4.

### Is Gemini 2.5 Flash or GLM-5V-Turbo better for coding?

GLM-5V-Turbo scores higher on coding benchmarks: 42.1 versus 35.8 in the Noometry coding category.

### Which has the bigger context window?

Gemini 2.5 Flash does, with 1.05M tokens against 200K.

### How many benchmarks do Gemini 2.5 Flash and GLM-5V-Turbo share?

17 benchmarks have published results for both models. Gemini 2.5 Flash has 54 scored results on Noometry and GLM-5V-Turbo has 19.