# DeepSeek-V3.2-Exp vs GLM-4.5V

> DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.9 to 39.2 on the Noometry Index.

- Canonical page: https://noometry.com/compare/deepseek-v3-2-exp-vs-glm-4-5v
- Last updated: 2026-10-10
- Shared benchmarks: 14

## Summary

- They share 14 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 7 categories and GLM-4.5V in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where DeepSeek-V3.2-Exp leads 51.6 to 36.8.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 52.2% for DeepSeek-V3.2-Exp and 59.8% for GLM-4.5V.
- DeepSeek-V3.2-Exp is cheaper at $0.26 / $0.38 per million input/output tokens, against $0.60 / $1.80 for GLM-4.5V.
- DeepSeek-V3.2-Exp accepts more context: 164K tokens versus 64K.

## Snapshot

| | DeepSeek-V3.2-Exp | GLM-4.5V |
|---|---|---|
| Provider | DeepSeek | Z.ai (Zhipu) |
| Noometry Index | 44.9 | 39.2 |
| Rank | 68 | 158 |
| Context | 164K | 64K |
| Input $/M | $0.26 | $0.60 |
| Output $/M | $0.38 | $1.80 |
| Weights | Open | Open |

## Coding

- DeepSeek-V3.2-Exp: 46.4 (#61)
- GLM-4.5V: 38.9 (#152)

| Benchmark | DeepSeek-V3.2-Exp | GLM-4.5V |
|---|---|---|
| LMArena Coding | 1454 | 1347 |
| SWE-bench Verified (bash only) | 70% | — |
| Aider Polyglot | 74.2% | — |
| LMArena WebDev | 1362 | — |
| SWE-bench Multilingual | 59% | — |
| SciCode | 38.9% | — |
| WeirdML | 39.5% | — |

## Agentic & Tool Use

- DeepSeek-V3.2-Exp: 37.9 (#31)
- GLM-4.5V: —

| Benchmark | DeepSeek-V3.2-Exp | GLM-4.5V |
|---|---|---|
| Terminal-Bench | 39.6% | — |
| Berkeley Function Calling Leaderboard | 56.7% | — |
| TheAgentCompany | 42.9% | — |
| Vending-Bench 2 | 1,034 | — |

## Reasoning

- DeepSeek-V3.2-Exp: 22.0 (#185)
- GLM-4.5V: 26.5 (#118)

| Benchmark | DeepSeek-V3.2-Exp | GLM-4.5V |
|---|---|---|
| Kagi LLM Benchmark | 52.2% | 59.8% |
| LMArena Hard Prompts | 1433 | 1335 |
| ARC-AGI-2 | 4% | — |
| NYT Connections (extended) | 36.7% | — |
| ARC-AGI-1 | 57% | — |
| CritPt | 2.9% | — |
| Chess Puzzles | 14% | — |
| Thematic Generalization | 65% | — |
| DTBench | 87.7% | — |
| LMCA | 29.1% | — |
| Epoch Capabilities Index | 146.28 | — |

## Math

- DeepSeek-V3.2-Exp: 40.8 (#75)
- GLM-4.5V: 34.7 (#157)

| Benchmark | DeepSeek-V3.2-Exp | GLM-4.5V |
|---|---|---|
| LMArena Math | 1435 | 1353 |
| MathArena Final-Answer Competitions | 57.7% | — |
| OTIS Mock AIME 2024-2025 | 87.8% | — |
| ProofBench | 8% | — |
| FrontierMath (Feb 2025 set) | 22.1% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |

## Knowledge

- DeepSeek-V3.2-Exp: 51.6 (#61)
- GLM-4.5V: 36.8 (#155)

| Benchmark | DeepSeek-V3.2-Exp | GLM-4.5V |
|---|---|---|
| LMArena Expert | 1436 | 1353 |
| GPQA Diamond | 83.4% | — |
| Vectara Hallucination Rate | 5.3% | — |

## Multimodal

- DeepSeek-V3.2-Exp: —
- GLM-4.5V: 33.6 (#86)

| Benchmark | DeepSeek-V3.2-Exp | GLM-4.5V |
|---|---|---|
| LMArena Vision | — | 1154 |

## Multilingual

- DeepSeek-V3.2-Exp: 52.4 (#85)
- GLM-4.5V: 44.9 (#172)

| Benchmark | DeepSeek-V3.2-Exp | GLM-4.5V |
|---|---|---|
| LMArena Non-English | 1409 | 1304 |
| LMArena Chinese | 1461 | 1338 |
| LMArena Russian | 1423 | 1299 |
| LMArena Spanish | 1441 | 1335 |
| LMArena French | 1435 | — |
| LMArena German | 1440 | — |
| LMArena Japanese | 1377 | — |
| LMArena Korean | 1372 | — |

## Instruction Following

- DeepSeek-V3.2-Exp: 74.7 (#89)
- GLM-4.5V: 69.4 (#170)

| Benchmark | DeepSeek-V3.2-Exp | GLM-4.5V |
|---|---|---|
| LMArena Instruction Following | 1413 | 1311 |

## Long Context

- DeepSeek-V3.2-Exp: 47.6 (#15)
- GLM-4.5V: 39.6 (#166)

| Benchmark | DeepSeek-V3.2-Exp | GLM-4.5V |
|---|---|---|
| LMArena Longer Query | 1428 | 1305 |
| Fiction.LiveBench | 83.3% | — |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |

## Writing & Preference

- DeepSeek-V3.2-Exp: 62.5 (#73)
- GLM-4.5V: 52.7 (#162)

| Benchmark | DeepSeek-V3.2-Exp | GLM-4.5V |
|---|---|---|
| LMArena Text | 1425 | 1333 |
| LMArena Creative Writing | 1403 | 1295 |
| LMArena Multi-Turn | 1427 | 1333 |
| EQ-Bench Creative Writing | 1515 | — |

## FAQ

### Is DeepSeek-V3.2-Exp better than GLM-4.5V?

DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.9 to 39.2 on the Noometry Index.

### Which is cheaper, DeepSeek-V3.2-Exp or GLM-4.5V?

DeepSeek-V3.2-Exp is cheaper. It lists at $0.26 per million input tokens and $0.38 per million output tokens; GLM-4.5V lists at $0.60 and $1.80.

### Is DeepSeek-V3.2-Exp or GLM-4.5V better for coding?

DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.4 versus 38.9 in the Noometry coding category.

### Which has the bigger context window?

DeepSeek-V3.2-Exp does, with 164K tokens against 64K.

### How many benchmarks do DeepSeek-V3.2-Exp and GLM-4.5V share?

14 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 48 scored results on Noometry and GLM-4.5V has 15.