Model comparison
DeepSeek-V3.2-Speciale vs o4-mini
o4-mini is the stronger model overall, scoring 41.6 to 39.7 on the Noometry Index. DeepSeek-V3.2-Speciale costs 2.3× less per token, which makes it the better buy when o4-mini'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 o4-mini in 2 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where DeepSeek-V3.2-Speciale leads 32.9 to 24.6.
- The biggest single-benchmark swing is SimpleBench: 52.6% for DeepSeek-V3.2-Speciale and 38.7% for o4-mini.
- DeepSeek-V3.2-Speciale is cheaper at $0.58 / $1.68 per million input/output tokens, against $1.10 / $4.40 for o4-mini.
- o4-mini 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 | o4-mini | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 39.7 | 41.6 |
| Released | 2025-12-01 | 2025-04-16 |
| Weights | Open | Proprietary |
| Context window | 128K | 200K |
| Max output | 128K | 100K |
| Input $ / M tokens | $0.58 | $1.10 |
| Output $ / M tokens | $1.68 | $4.40 |
| Results tracked | 3 | 60 |
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Category by category
Coding Too close to call
DeepSeek-V3.2-Speciale: 40.4 (#140), o4-mini: 40.9 (#127)
| Benchmark | DeepSeek-V3.2-Speciale | o4-mini |
|---|---|---|
| WeirdML | 46.7% | 52.6% |
| SWE-bench Verified (bash only) | — | 45% |
| Aider Polyglot | — | 72% |
| GSO | — | 3.6% |
| LMArena Coding | — | 1368 |
| CadEval | — | 62% |
| ALE-Bench | — | 826.17 |
| AlgoTune | — | 1.72 |
Agentic & Tool Use Not comparable
DeepSeek-V3.2-Speciale: —, o4-mini: 32.6 (#61)
| Benchmark | DeepSeek-V3.2-Speciale | o4-mini |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 53.2% |
| GDPval | — | 25.3% |
| METR Time Horizons | — | 63.9% |
Reasoning DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 32.9 (#73), o4-mini: 24.6 (#162)
| Benchmark | DeepSeek-V3.2-Speciale | o4-mini |
|---|---|---|
| SimpleBench | 52.6% | 38.7% |
| ARC-AGI-2 | — | 6.1% |
| Kagi LLM Benchmark | — | 67.6% |
| ARC-AGI-1 | — | 58.7% |
| CritPt | — | 0.6% |
| Chess Puzzles | — | 26% |
| EnigmaEval | — | 9.2% |
| LMArena Hard Prompts | — | 1351 |
| Mystery Game Puzzles | — | 5% |
| DTBench | — | 77.6% |
| LMCA | — | 26.5% |
| Epoch Capabilities Index | — | 145.64 |
| ForecastBench | — | 61.8 |
Math Not comparable
DeepSeek-V3.2-Speciale: —, o4-mini: 40.8 (#89)
| Benchmark | DeepSeek-V3.2-Speciale | o4-mini |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 36.1% |
| FrontierMath Tier 4 | — | 4.9% |
| OTIS Mock AIME 2024-2025 | — | 81.7% |
| Omni-MATH | — | 72% |
| LMArena Math | — | 1389 |
| MATH Level 5 | — | 97.8% |
| FrontierMath (Feb 2025 set) | — | 24.8% |
| FrontierMath Tier 4 (v1) | — | 6.3% |
Knowledge Not comparable
DeepSeek-V3.2-Speciale: —, o4-mini: 43.6 (#91)
| Benchmark | DeepSeek-V3.2-Speciale | o4-mini |
|---|---|---|
| GPQA Diamond | — | 79.6% |
| Humanity's Last Exam | — | 18.1% |
| SimpleQA Verified | — | 19.6% |
| MMLU-Pro | — | 82% |
| Confabulations | — | 15.8% |
| Vectara Hallucination Rate | — | 18.6% |
| GPQA (HELM) | — | 73.5% |
| LMArena Expert | — | 1343 |
Multimodal Not comparable
DeepSeek-V3.2-Speciale: —, o4-mini: 40.2 (#49)
| Benchmark | DeepSeek-V3.2-Speciale | o4-mini |
|---|---|---|
| LMArena Vision | — | 1194 |
| GeoBench | — | 64% |
| VPCT | — | 57.5% |
Multilingual Not comparable
DeepSeek-V3.2-Speciale: —, o4-mini: 47.0 (#154)
| Benchmark | DeepSeek-V3.2-Speciale | o4-mini |
|---|---|---|
| LMArena Non-English | — | 1337 |
| LMArena Chinese | — | 1354 |
| LMArena French | — | 1364 |
| LMArena German | — | 1336 |
| LMArena Japanese | — | 1308 |
| LMArena Korean | — | 1312 |
| LMArena Russian | — | 1334 |
| LMArena Spanish | — | 1347 |
Instruction Following Not comparable
DeepSeek-V3.2-Speciale: —, o4-mini: 75.2 (#68)
| Benchmark | DeepSeek-V3.2-Speciale | o4-mini |
|---|---|---|
| IFEval | — | 92.8% |
| LMArena Instruction Following | — | 1321 |
Long Context Not comparable
DeepSeek-V3.2-Speciale: —, o4-mini: 45.5 (#33)
| Benchmark | DeepSeek-V3.2-Speciale | o4-mini |
|---|---|---|
| Fiction.LiveBench | — | 77.8% |
| LMArena Longer Query | — | 1315 |
Writing & Preference o4-mini leads
DeepSeek-V3.2-Speciale: 46.0 (#222), o4-mini: 54.0 (#152)
| Benchmark | DeepSeek-V3.2-Speciale | o4-mini |
|---|---|---|
| LMArena Text | — | 1353 |
| LMArena Creative Writing | — | 1294 |
| Short-Story Creative Writing | — | 75% |
| EQ-Bench Creative Writing | 1276 | — |
| WildBench | — | 85.4% |
| LMArena Multi-Turn | — | 1350 |
Frequently asked questions
Is DeepSeek-V3.2-Speciale better than o4-mini?
o4-mini is the stronger model overall, scoring 41.6 to 39.7 on the Noometry Index. DeepSeek-V3.2-Speciale costs 2.3× less per token, which makes it the better buy when o4-mini's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3.2-Speciale or o4-mini?
DeepSeek-V3.2-Speciale is cheaper. It lists at $0.58 per million input tokens and $1.68 per million output tokens; o4-mini lists at $1.10 and $4.40.
Is DeepSeek-V3.2-Speciale or o4-mini better for coding?
They score almost the same on coding (40.4 vs 40.9); test both on your own repository before choosing.
Which has the bigger context window?
o4-mini does, with 200K tokens against 128K.
How many benchmarks do DeepSeek-V3.2-Speciale and o4-mini share?
2 benchmarks have published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and o4-mini has 60.