# DeepSeek-V3.2-Speciale vs GPT-5.5

> GPT-5.5 is the stronger model overall, scoring 63.4 to 39.7 on the Noometry Index. DeepSeek-V3.2-Speciale costs 13× less per token, which makes it the better buy when GPT-5.5's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-v3-2-speciale-vs-gpt-5-5
- Last updated: 2026-10-12
- Shared benchmarks: 3

## Summary

- They share 3 benchmarks with published results for both. DeepSeek-V3.2-Speciale scores higher in 0 categories and GPT-5.5 in 3 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.5 leads 72.8 to 32.9.
- The biggest single-benchmark swing is WeirdML: 46.7% for DeepSeek-V3.2-Speciale and 84.9% for GPT-5.5.
- DeepSeek-V3.2-Speciale is cheaper at $0.58 / $1.68 per million input/output tokens, against $5 / $30 for GPT-5.5.
- GPT-5.5 accepts more context: 1.05M tokens versus 128K.
- DeepSeek-V3.2-Speciale has downloadable open weights; the other is API-only.

## Snapshot

| | DeepSeek-V3.2-Speciale | GPT-5.5 |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 39.7 | 63.4 |
| Rank | 162 | 9 |
| Context | 128K | 1.05M |
| Input $/M | $0.58 | $5 |
| Output $/M | $1.68 | $30 |
| Weights | Open | Proprietary |

## Coding

- DeepSeek-V3.2-Speciale: 40.4 (#140)
- GPT-5.5: 58.2 (#17)

| Benchmark | DeepSeek-V3.2-Speciale | GPT-5.5 |
|---|---|---|
| WeirdML | 46.7% | 84.9% |
| SWE-bench Verified | — | 80.6% |
| DeepSWE | — | 67% |
| FrontierCode | — | 43% |
| LMArena WebDev | — | 1513 |
| SciCode | — | 56.1% |
| GSO | — | 40.2% |
| LMArena Coding | — | 1494 |
| MirrorCode | — | 10% |
| ALE-Bench | — | 1,943 |

## Agentic & Tool Use

- DeepSeek-V3.2-Speciale: —
- GPT-5.5: 50.7 (#6)

| Benchmark | DeepSeek-V3.2-Speciale | GPT-5.5 |
|---|---|---|
| Terminal-Bench | — | 84.7% |
| APEX-Agents | — | 55.1% |
| OSWorld 2.0 | — | 13% |
| Remote Labor Index | — | 6.3% |
| τ²-bench Banking | — | 44.6% |
| DeepResearch Bench | — | 54% |
| PostTrainBench | — | 27.2% |
| ExploitBench | — | 47.4% |
| GBAEval | — | 53.2% |
| GDP.pdf | — | 26% |
| LMArena Search | — | 1242 |
| Vending-Bench 2 | — | 7,524 |

## Reasoning

- DeepSeek-V3.2-Speciale: 32.9 (#73)
- GPT-5.5: 72.8 (#11)

| Benchmark | DeepSeek-V3.2-Speciale | GPT-5.5 |
|---|---|---|
| SimpleBench | 52.6% | 69% |
| ARC-AGI-2 | — | 85% |
| Kagi LLM Benchmark | — | 88.8% |
| NYT Connections (extended) | — | 96.2% |
| ARC-AGI-1 | — | 95% |
| CritPt | — | 27.1% |
| Chess Puzzles | — | 54% |
| EBR-Bench | — | 34.3% |
| LMArena Hard Prompts | — | 1489 |
| Mystery Game Puzzles | — | 56% |
| DTBench | — | 96% |
| LMCA | — | 54.3% |
| Surface Evolver Bench | — | 88.1% |
| Bench to the Future 3 | — | 0.14 |
| Epoch Capabilities Index | — | 159.1 |
| ForecastBench | — | 60.6 |

## Math

- DeepSeek-V3.2-Speciale: —
- GPT-5.5: 81.7 (#11)

| Benchmark | DeepSeek-V3.2-Speciale | GPT-5.5 |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 85.3% |
| FrontierMath Tier 4 | — | 72.5% |
| MathArena Final-Answer Competitions | — | 94.3% |
| OTIS Mock AIME 2024-2025 | — | 100% |
| ProofBench | — | 50% |
| LMArena Math | — | 1486 |
| FrontierMath (Feb 2025 set) | — | 51.7% |
| FrontierMath Erdős | — | 0% |
| FrontierMath Tier 4 (v1) | — | 35.4% |

## Knowledge

- DeepSeek-V3.2-Speciale: —
- GPT-5.5: 64.4 (#17)

| Benchmark | DeepSeek-V3.2-Speciale | GPT-5.5 |
|---|---|---|
| GPQA Diamond | — | 94% |
| SimpleQA Verified | — | 63% |
| Vectara Hallucination Rate | — | 9.3% |
| LMArena Expert | — | 1508 |

## Multimodal

- DeepSeek-V3.2-Speciale: —
- GPT-5.5: 46.9 (#12)

| Benchmark | DeepSeek-V3.2-Speciale | GPT-5.5 |
|---|---|---|
| LMArena Vision | — | 1297 |
| Blueprint-Bench 2 | — | 36.2% |
| Furniture Assembly | — | 44.2% |
| LMArena Document | — | 1486 |

## Multilingual

- DeepSeek-V3.2-Speciale: —
- GPT-5.5: 56.4 (#20)

| Benchmark | DeepSeek-V3.2-Speciale | GPT-5.5 |
|---|---|---|
| LMArena Non-English | — | 1467 |
| LMArena Chinese | — | 1533 |
| LMArena French | — | 1486 |
| LMArena German | — | 1480 |
| LMArena Japanese | — | 1498 |
| LMArena Korean | — | 1460 |
| LMArena Russian | — | 1473 |
| LMArena Spanish | — | 1468 |

## Instruction Following

- DeepSeek-V3.2-Speciale: —
- GPT-5.5: 77.5 (#18)

| Benchmark | DeepSeek-V3.2-Speciale | GPT-5.5 |
|---|---|---|
| LMArena Instruction Following | — | 1479 |

## Long Context

- DeepSeek-V3.2-Speciale: —
- GPT-5.5: 48.3 (#12)

| Benchmark | DeepSeek-V3.2-Speciale | GPT-5.5 |
|---|---|---|
| CL-bench Life | — | 22.2% |
| LMArena Longer Query | — | 1484 |

## Writing & Preference

- DeepSeek-V3.2-Speciale: 46.0 (#222)
- GPT-5.5: 72.7 (#13)

| Benchmark | DeepSeek-V3.2-Speciale | GPT-5.5 |
|---|---|---|
| EQ-Bench Creative Writing | 1276 | 1844 |
| LMArena Text | — | 1472 |
| LMArena Creative Writing | — | 1455 |
| EQ-Bench 4 | — | 1315 |
| LMArena Multi-Turn | — | 1476 |

## FAQ

### Is DeepSeek-V3.2-Speciale better than GPT-5.5?

GPT-5.5 is the stronger model overall, scoring 63.4 to 39.7 on the Noometry Index. DeepSeek-V3.2-Speciale costs 13× less per token, which makes it the better buy when GPT-5.5's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek-V3.2-Speciale or GPT-5.5?

DeepSeek-V3.2-Speciale is cheaper. It lists at $0.58 per million input tokens and $1.68 per million output tokens; GPT-5.5 lists at $5 and $30.

### Is DeepSeek-V3.2-Speciale or GPT-5.5 better for coding?

GPT-5.5 scores higher on coding benchmarks: 58.2 versus 40.4 in the Noometry coding category.

### Which has the bigger context window?

GPT-5.5 does, with 1.05M tokens against 128K.

### How many benchmarks do DeepSeek-V3.2-Speciale and GPT-5.5 share?

3 benchmarks have published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and GPT-5.5 has 71.