# DeepSeek-V3.2-Speciale vs GLM-5.2

> GLM-5.2 is the stronger model overall, scoring 50.9 to 39.1 on the Noometry Index. DeepSeek-V3.2-Speciale costs 2.5× 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/deepseek-v3-2-speciale-vs-glm-5-2
- Last updated: 2026-10-10
- Shared benchmarks: 3

## Summary

- They share 3 benchmarks with published results for both. DeepSeek-V3.2-Speciale scores higher in 0 categories and GLM-5.2 in 3 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-5.2 leads 70.7 to 46.1.
- The biggest single-benchmark swing is WeirdML: 46.7% for DeepSeek-V3.2-Speciale and 70.1% for GLM-5.2.
- DeepSeek-V3.2-Speciale is cheaper at $0.58 / $1.68 per million input/output tokens, against $1.40 / $4.40 for GLM-5.2.
- GLM-5.2 accepts more context: 1M tokens versus 128K.

## Snapshot

| | DeepSeek-V3.2-Speciale | GLM-5.2 |
|---|---|---|
| Provider | DeepSeek | Z.ai (Zhipu) |
| Noometry Index | 39.1 | 50.9 |
| Rank | 159 | 38 |
| Context | 128K | 1M |
| Input $/M | $0.58 | $1.40 |
| Output $/M | $1.68 | $4.40 |
| Weights | Open | Open |

## Coding

- DeepSeek-V3.2-Speciale: 39.9 (#136)
- GLM-5.2: 50.9 (#37)

| Benchmark | DeepSeek-V3.2-Speciale | GLM-5.2 |
|---|---|---|
| WeirdML | 46.7% | 70.1% |
| SWE-bench Verified | — | 78.7% |
| DeepSWE | — | 43.8% |
| FrontierCode | — | 24.5% |
| LMArena WebDev | — | 1604 |
| SciCode | — | 50.5% |
| LMArena Coding | — | 1484 |
| ALE-Bench | — | 1,047 |

## Agentic & Tool Use

- DeepSeek-V3.2-Speciale: —
- GLM-5.2: 31.6 (#63)

| Benchmark | DeepSeek-V3.2-Speciale | GLM-5.2 |
|---|---|---|
| τ²-bench Banking | — | 37.1% |
| PostTrainBench | — | 31.7% |
| GBAEval | — | 0% |
| Vending-Bench 2 | — | 8,314 |

## Reasoning

- DeepSeek-V3.2-Speciale: 32.1 (#65)
- GLM-5.2: 42.1 (#46)

| Benchmark | DeepSeek-V3.2-Speciale | GLM-5.2 |
|---|---|---|
| SimpleBench | 52.6% | 58.8% |
| ARC-AGI-2 | — | 22.8% |
| Kagi LLM Benchmark | — | 62.6% |
| NYT Connections (extended) | — | 74.3% |
| ARC-AGI-1 | — | 77% |
| CritPt | — | 20.9% |
| Chess Puzzles | — | 21% |
| EBR-Bench | — | 9.5% |
| LMArena Hard Prompts | — | 1480 |
| Mystery Game Puzzles | — | 19% |
| DTBench | — | 93.6% |
| LMCA | — | 45.8% |
| Surface Evolver Bench | — | 55.6% |
| Epoch Capabilities Index | — | 151.76 |

## Math

- DeepSeek-V3.2-Speciale: —
- GLM-5.2: 55.4 (#35)

| Benchmark | DeepSeek-V3.2-Speciale | GLM-5.2 |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 59.2% |
| FrontierMath Tier 4 | — | 29.3% |
| MathArena Final-Answer Competitions | — | 67.6% |
| OTIS Mock AIME 2024-2025 | — | 86.4% |
| ProofBench | — | 35% |
| LMArena Math | — | 1482 |

## Knowledge

- DeepSeek-V3.2-Speciale: —
- GLM-5.2: 57.1 (#36)

| Benchmark | DeepSeek-V3.2-Speciale | GLM-5.2 |
|---|---|---|
| GPQA Diamond | — | 91.9% |
| SimpleQA Verified | — | 34.2% |
| LMArena Expert | — | 1484 |

## Multilingual

- DeepSeek-V3.2-Speciale: —
- GLM-5.2: 56.0 (#25)

| Benchmark | DeepSeek-V3.2-Speciale | GLM-5.2 |
|---|---|---|
| LMArena Non-English | — | 1459 |
| LMArena Chinese | — | 1518 |
| LMArena French | — | 1476 |
| LMArena German | — | 1470 |
| LMArena Japanese | — | 1451 |
| LMArena Korean | — | 1445 |
| LMArena Russian | — | 1467 |
| LMArena Spanish | — | 1477 |

## Instruction Following

- DeepSeek-V3.2-Speciale: —
- GLM-5.2: 77.1 (#32)

| Benchmark | DeepSeek-V3.2-Speciale | GLM-5.2 |
|---|---|---|
| LMArena Instruction Following | — | 1465 |

## Long Context

- DeepSeek-V3.2-Speciale: —
- GLM-5.2: 45.3 (#43)

| Benchmark | DeepSeek-V3.2-Speciale | GLM-5.2 |
|---|---|---|
| LMArena Longer Query | — | 1479 |

## Writing & Preference

- DeepSeek-V3.2-Speciale: 46.1 (#217)
- GLM-5.2: 70.7 (#20)

| Benchmark | DeepSeek-V3.2-Speciale | GLM-5.2 |
|---|---|---|
| EQ-Bench Creative Writing | 1276 | 1757 |
| LMArena Text | — | 1470 |
| LMArena Creative Writing | — | 1465 |
| EQ-Bench 4 | — | 1222 |
| LMArena Multi-Turn | — | 1468 |

## FAQ

### Is DeepSeek-V3.2-Speciale better than GLM-5.2?

GLM-5.2 is the stronger model overall, scoring 50.9 to 39.1 on the Noometry Index. DeepSeek-V3.2-Speciale costs 2.5× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek-V3.2-Speciale or GLM-5.2?

DeepSeek-V3.2-Speciale is cheaper. It lists at $0.58 per million input tokens and $1.68 per million output tokens; GLM-5.2 lists at $1.40 and $4.40.

### Is DeepSeek-V3.2-Speciale or GLM-5.2 better for coding?

GLM-5.2 scores higher on coding benchmarks: 50.9 versus 39.9 in the Noometry coding category.

### Which has the bigger context window?

GLM-5.2 does, with 1M tokens against 128K.

### How many benchmarks do DeepSeek-V3.2-Speciale and GLM-5.2 share?

3 benchmarks have published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and GLM-5.2 has 50.