Model comparison
DeepSeek-V3.2-Speciale vs o1
o1 is the stronger model overall, scoring 40.9 to 39.7 on the Noometry Index. DeepSeek-V3.2-Speciale costs 31× less per token, which makes it the better buy when o1's lead doesn't matter for your workload.
Last verified . 2 shared benchmarks.
DeepSeekSummary
- They share 2 benchmarks with published results for both. DeepSeek-V3.2-Speciale scores higher in 1 category and o1 in 2 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where o1 leads 55.6 to 46.0.
- The biggest single-benchmark swing is SimpleBench: 52.6% for DeepSeek-V3.2-Speciale and 41.7% for o1.
- DeepSeek-V3.2-Speciale is cheaper at $0.58 / $1.68 per million input/output tokens, against $15 / $60 for o1.
- o1 accepts more context: 200K tokens versus 128K.
- DeepSeek-V3.2-Speciale has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3.2-Speciale | o1 | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 39.7 | 40.9 |
| Released | 2025-12-01 | 2024-09-12 |
| Weights | Open | Proprietary |
| Context window | 128K | 200K |
| Max output | 128K | 100K |
| Input $ / M tokens | $0.58 | $15 |
| Output $ / M tokens | $1.68 | $60 |
| Results tracked | 3 | 52 |
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Category by category
Coding o1 leads
DeepSeek-V3.2-Speciale: 40.4 (#140), o1: 46.1 (#70)
| Benchmark | DeepSeek-V3.2-Speciale | o1 |
|---|---|---|
| WeirdML | 46.7% | 47.6% |
| Aider Polyglot | — | 61.7% |
| LiveBench Coding | — | 69.7% |
| LMArena Coding | — | 1367 |
| CadEval | — | 56% |
| HumanEval+ | — | 89% |
| MBPP+ | — | 80.2% |
Agentic & Tool Use Not comparable
DeepSeek-V3.2-Speciale: —, o1: 24.6 (#117)
| Benchmark | DeepSeek-V3.2-Speciale | o1 |
|---|---|---|
| Cybench | — | 10% |
| METR Time Horizons | — | 51.1% |
Reasoning DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 32.9 (#73), o1: 27.9 (#111)
| Benchmark | DeepSeek-V3.2-Speciale | o1 |
|---|---|---|
| SimpleBench | 52.6% | 41.7% |
| ARC-AGI-1 | — | 30.7% |
| Chess Puzzles | — | 15% |
| EnigmaEval | — | 5.7% |
| LiveBench Reasoning | — | 91.6% |
| LMArena Hard Prompts | — | 1371 |
| DTBench | — | 74.7% |
| LiveBench Data Analysis | — | 65.5% |
| LMCA | — | 22.3% |
| Epoch Capabilities Index | — | 141.91 |
| LiveBench | — | 75.7% |
Math Not comparable
DeepSeek-V3.2-Speciale: —, o1: 36.1 (#175)
| Benchmark | DeepSeek-V3.2-Speciale | o1 |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 14.7% |
| OTIS Mock AIME 2024-2025 | — | 73.3% |
| LiveBench Math | — | 80.3% |
| LMArena Math | — | 1388 |
| MATH Level 5 | — | 94.7% |
| FrontierMath (Feb 2025 set) | — | 9.3% |
Knowledge Not comparable
DeepSeek-V3.2-Speciale: —, o1: 41.5 (#110)
| Benchmark | DeepSeek-V3.2-Speciale | o1 |
|---|---|---|
| GPQA Diamond | — | 76.8% |
| Humanity's Last Exam | — | 8% |
| SimpleQA Verified | — | 41.1% |
| Confabulations | — | 11.7% |
| LMArena Expert | — | 1361 |
Multimodal Not comparable
DeepSeek-V3.2-Speciale: —, o1: 34.2 (#93)
| Benchmark | DeepSeek-V3.2-Speciale | o1 |
|---|---|---|
| LMArena Vision | — | 1168 |
| GeoBench | — | 80% |
| VPCT | — | 37% |
| SpatialViz-Bench | — | 41.4% |
Multilingual Not comparable
DeepSeek-V3.2-Speciale: —, o1: 48.6 (#142)
| Benchmark | DeepSeek-V3.2-Speciale | o1 |
|---|---|---|
| LMArena Non-English | — | 1358 |
| LMArena Chinese | — | 1394 |
| LMArena French | — | 1344 |
| LMArena German | — | 1337 |
| LMArena Japanese | — | 1346 |
| LMArena Korean | — | 1396 |
| LMArena Russian | — | 1356 |
| LMArena Spanish | — | 1345 |
Instruction Following Not comparable
DeepSeek-V3.2-Speciale: —, o1: 74.8 (#86)
| Benchmark | DeepSeek-V3.2-Speciale | o1 |
|---|---|---|
| LiveBench Instruction Following | — | 81.5% |
| LMArena Instruction Following | — | 1367 |
Long Context Not comparable
DeepSeek-V3.2-Speciale: —, o1: 50.3 (#9)
| Benchmark | DeepSeek-V3.2-Speciale | o1 |
|---|---|---|
| Fiction.LiveBench | — | 83.3% |
| LMArena Longer Query | — | 1378 |
Writing & Preference o1 leads
DeepSeek-V3.2-Speciale: 46.0 (#222), o1: 55.6 (#144)
| Benchmark | DeepSeek-V3.2-Speciale | o1 |
|---|---|---|
| LMArena Text | — | 1366 |
| LMArena Creative Writing | — | 1348 |
| Short-Story Creative Writing | — | 70.2% |
| EQ-Bench Creative Writing | 1276 | — |
| LMArena Multi-Turn | — | 1369 |
| LiveBench Language | — | 65.4% |
Frequently asked questions
Is DeepSeek-V3.2-Speciale better than o1?
o1 is the stronger model overall, scoring 40.9 to 39.7 on the Noometry Index. DeepSeek-V3.2-Speciale costs 31× less per token, which makes it the better buy when o1's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3.2-Speciale or o1?
DeepSeek-V3.2-Speciale is cheaper. It lists at $0.58 per million input tokens and $1.68 per million output tokens; o1 lists at $15 and $60.
Is DeepSeek-V3.2-Speciale or o1 better for coding?
o1 scores higher on coding benchmarks: 46.1 versus 40.4 in the Noometry coding category.
Which has the bigger context window?
o1 does, with 200K tokens against 128K.
How many benchmarks do DeepSeek-V3.2-Speciale and o1 share?
2 benchmarks have published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and o1 has 52.