# DeepSeek-V3.2-Speciale vs GPT-5.6 Sol

> GPT-5.6 Sol is the stronger model overall, scoring 65.0 to 39.7 on the Noometry Index. DeepSeek-V3.2-Speciale costs 9.4× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-v3-2-speciale-vs-gpt-5-6-sol
- 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-5.6 Sol in 3 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.6 Sol leads 74.8 to 32.9.
- The biggest single-benchmark swing is WeirdML: 46.7% for DeepSeek-V3.2-Speciale and 89.4% for GPT-5.6 Sol.
- DeepSeek-V3.2-Speciale is cheaper at $0.58 / $1.68 per million input/output tokens, against $4 / $20 for GPT-5.6 Sol.
- GPT-5.6 Sol 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-5.6 Sol |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 39.7 | 65.0 |
| Rank | 162 | 7 |
| Context | 128K | 1.05M |
| Input $/M | $0.58 | $4 |
| Output $/M | $1.68 | $20 |
| Weights | Open | Proprietary |

## Coding

- DeepSeek-V3.2-Speciale: 40.4 (#140)
- GPT-5.6 Sol: 65.1 (#7)

| Benchmark | DeepSeek-V3.2-Speciale | GPT-5.6 Sol |
|---|---|---|
| WeirdML | 46.7% | 89.4% |
| DeepSWE | — | 72.7% |
| FrontierCode | — | 47.5% |
| CursorBench | — | 41.7% |
| LMArena WebDev | — | 1618 |
| FrontierSWE | — | 32.2% |
| SciCode | — | 57.1% |
| GSO | — | 76.5% |
| LMArena Coding | — | 1498 |
| MirrorCode | — | 20% |
| ALE-Bench | — | 2,177 |

## Agentic & Tool Use

- DeepSeek-V3.2-Speciale: —
- GPT-5.6 Sol: 50.3 (#7)

| Benchmark | DeepSeek-V3.2-Speciale | GPT-5.6 Sol |
|---|---|---|
| APEX-Agents | — | 51.4% |
| OSWorld 2.0 | — | 27.3% |
| τ²-bench Banking | — | 46.9% |
| PostTrainBench | — | 36.2% |
| BALROG | — | 60% |
| GBAEval | — | 52.6% |
| GDP.pdf | — | 30.7% |
| LMArena Search | — | 1257 |
| Vending-Bench 2 | — | 9,619 |

## Reasoning

- DeepSeek-V3.2-Speciale: 32.9 (#73)
- GPT-5.6 Sol: 74.8 (#8)

| Benchmark | DeepSeek-V3.2-Speciale | GPT-5.6 Sol |
|---|---|---|
| SimpleBench | 52.6% | 71.7% |
| ARC-AGI-2 | — | 92.5% |
| Kagi LLM Benchmark | — | 67% |
| NYT Connections (extended) | — | 93.8% |
| ARC-AGI-1 | — | 97.5% |
| CritPt | — | 32.3% |
| Chess Puzzles | — | 64% |
| EnigmaEval | — | 37.1% |
| EBR-Bench | — | 44.8% |
| LMArena Hard Prompts | — | 1484 |
| Mystery Game Puzzles | — | 58% |
| DTBench | — | 96% |
| LMCA | — | 59.2% |
| Surface Evolver Bench | — | 93.1% |
| Bench to the Future 3 | — | 0.14 |
| Epoch Capabilities Index | — | 161.66 |

## Math

- DeepSeek-V3.2-Speciale: —
- GPT-5.6 Sol: 85.6 (#9)

| Benchmark | DeepSeek-V3.2-Speciale | GPT-5.6 Sol |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 89.1% |
| FrontierMath Tier 4 | — | 82.9% |
| OTIS Mock AIME 2024-2025 | — | 100% |
| ProofBench | — | 83% |
| LMArena Math | — | 1474 |
| FrontierMath Erdős | — | 0% |

## Knowledge

- DeepSeek-V3.2-Speciale: —
- GPT-5.6 Sol: 64.3 (#18)

| Benchmark | DeepSeek-V3.2-Speciale | GPT-5.6 Sol |
|---|---|---|
| GPQA Diamond | — | 93.5% |
| SimpleQA Verified | — | 69.7% |
| Vectara Hallucination Rate | — | 12.4% |
| LMArena Expert | — | 1516 |

## Multimodal

- DeepSeek-V3.2-Speciale: —
- GPT-5.6 Sol: 48.6 (#9)

| Benchmark | DeepSeek-V3.2-Speciale | GPT-5.6 Sol |
|---|---|---|
| LMArena Vision | — | 1281 |
| Blueprint-Bench 2 | — | 33.6% |
| Furniture Assembly | — | 56.7% |
| LMArena Document | — | 1483 |

## Multilingual

- DeepSeek-V3.2-Speciale: —
- GPT-5.6 Sol: 55.3 (#32)

| Benchmark | DeepSeek-V3.2-Speciale | GPT-5.6 Sol |
|---|---|---|
| LMArena Non-English | — | 1452 |
| LMArena Chinese | — | 1527 |
| LMArena French | — | 1477 |
| LMArena German | — | 1476 |
| LMArena Japanese | — | 1471 |
| LMArena Korean | — | 1442 |
| LMArena Russian | — | 1468 |
| LMArena Spanish | — | 1441 |

## Instruction Following

- DeepSeek-V3.2-Speciale: —
- GPT-5.6 Sol: 77.7 (#16)

| Benchmark | DeepSeek-V3.2-Speciale | GPT-5.6 Sol |
|---|---|---|
| LMArena Instruction Following | — | 1482 |

## Long Context

- DeepSeek-V3.2-Speciale: —
- GPT-5.6 Sol: 45.4 (#42)

| Benchmark | DeepSeek-V3.2-Speciale | GPT-5.6 Sol |
|---|---|---|
| LMArena Longer Query | — | 1480 |

## Writing & Preference

- DeepSeek-V3.2-Speciale: 46.0 (#222)
- GPT-5.6 Sol: 73.3 (#12)

| Benchmark | DeepSeek-V3.2-Speciale | GPT-5.6 Sol |
|---|---|---|
| EQ-Bench Creative Writing | 1276 | 1972 |
| LMArena Text | — | 1457 |
| LMArena Creative Writing | — | 1448 |
| EQ-Bench 4 | — | 1250 |
| LMArena Multi-Turn | — | 1460 |

## FAQ

### Is DeepSeek-V3.2-Speciale better than GPT-5.6 Sol?

GPT-5.6 Sol is the stronger model overall, scoring 65.0 to 39.7 on the Noometry Index. DeepSeek-V3.2-Speciale costs 9.4× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek-V3.2-Speciale or GPT-5.6 Sol?

DeepSeek-V3.2-Speciale is cheaper. It lists at $0.58 per million input tokens and $1.68 per million output tokens; GPT-5.6 Sol lists at $4 and $20.

### Is DeepSeek-V3.2-Speciale or GPT-5.6 Sol better for coding?

GPT-5.6 Sol scores higher on coding benchmarks: 65.1 versus 40.4 in the Noometry coding category.

### Which has the bigger context window?

GPT-5.6 Sol does, with 1.05M tokens against 128K.

### How many benchmarks do DeepSeek-V3.2-Speciale and GPT-5.6 Sol share?

3 benchmarks have published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and GPT-5.6 Sol has 65.