Model comparison
DeepSeek-V3.2-Speciale vs GPT-5.6 Sol
GPT-5.6 Sol is the stronger model overall, scoring 64.4 to 39.1 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.
Last verified . 3 shared benchmarks.
DeepSeekSummary
- 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 74.7 to 32.1.
- 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.
Side by side
| DeepSeek-V3.2-Speciale | GPT-5.6 Sol | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 39.1 | 64.4 |
| Released | 2025-12-01 | 2026-07-09 |
| Weights | Open | Proprietary |
| Context window | 128K | 1.05M |
| Max output | 128K | 128K |
| Input $ / M tokens | $0.58 | $4 |
| Output $ / M tokens | $1.68 | $20 |
| Results tracked | 3 | 64 |
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Category by category
Coding GPT-5.6 Sol leads
DeepSeek-V3.2-Speciale: 39.9 (#136), GPT-5.6 Sol: 61.1 (#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 | — | 1619 |
| FrontierSWE | — | 32.2% |
| SciCode | — | 57.1% |
| LMArena Coding | — | 1493 |
| MirrorCode | — | 20% |
| ALE-Bench | — | 2,177 |
Agentic & Tool Use Not comparable
DeepSeek-V3.2-Speciale: —, GPT-5.6 Sol: 50.2 (#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 GPT-5.6 Sol leads
DeepSeek-V3.2-Speciale: 32.1 (#65), GPT-5.6 Sol: 74.7 (#6)
| 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 | — | 1481 |
| Mystery Game Puzzles | — | 58% |
| DTBench | — | 96% |
| LMCA | — | 59.2% |
| Surface Evolver Bench | — | 93.1% |
| Bench to the Future 3 | — | 0.14 |
| Epoch Capabilities Index | — | 161.99 |
Math Not comparable
DeepSeek-V3.2-Speciale: —, GPT-5.6 Sol: 85.5 (#7)
| 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 | — | 1472 |
| FrontierMath Erdős | — | 0% |
Knowledge Not comparable
DeepSeek-V3.2-Speciale: —, GPT-5.6 Sol: 64.3 (#16)
| Benchmark | DeepSeek-V3.2-Speciale | GPT-5.6 Sol |
|---|---|---|
| GPQA Diamond | — | 93.5% |
| SimpleQA Verified | — | 69.7% |
| Vectara Hallucination Rate | — | 12.4% |
| LMArena Expert | — | 1517 |
Multimodal Not comparable
DeepSeek-V3.2-Speciale: —, GPT-5.6 Sol: 48.9 (#8)
| 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 Not comparable
DeepSeek-V3.2-Speciale: —, GPT-5.6 Sol: 55.4 (#32)
| Benchmark | DeepSeek-V3.2-Speciale | GPT-5.6 Sol |
|---|---|---|
| LMArena Non-English | — | 1450 |
| LMArena Chinese | — | 1523 |
| LMArena French | — | 1475 |
| LMArena German | — | 1476 |
| LMArena Japanese | — | 1475 |
| LMArena Korean | — | 1434 |
| LMArena Russian | — | 1466 |
| LMArena Spanish | — | 1449 |
Instruction Following Not comparable
DeepSeek-V3.2-Speciale: —, GPT-5.6 Sol: 77.8 (#16)
| Benchmark | DeepSeek-V3.2-Speciale | GPT-5.6 Sol |
|---|---|---|
| LMArena Instruction Following | — | 1480 |
Long Context Not comparable
DeepSeek-V3.2-Speciale: —, GPT-5.6 Sol: 45.3 (#42)
| Benchmark | DeepSeek-V3.2-Speciale | GPT-5.6 Sol |
|---|---|---|
| LMArena Longer Query | — | 1479 |
Writing & Preference GPT-5.6 Sol leads
DeepSeek-V3.2-Speciale: 46.1 (#217), GPT-5.6 Sol: 73.5 (#12)
| Benchmark | DeepSeek-V3.2-Speciale | GPT-5.6 Sol |
|---|---|---|
| EQ-Bench Creative Writing | 1276 | 1972 |
| LMArena Text | — | 1456 |
| LMArena Creative Writing | — | 1450 |
| EQ-Bench 4 | — | 1250 |
| LMArena Multi-Turn | — | 1456 |
Frequently asked questions
Is DeepSeek-V3.2-Speciale better than GPT-5.6 Sol?
GPT-5.6 Sol is the stronger model overall, scoring 64.4 to 39.1 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: 61.1 versus 39.9 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 64.