Model comparison
DeepSeek-V3.2-Speciale vs GPT-5 Mini
GPT-5 Mini is the stronger model overall, scoring 41.8 to 39.7 on the Noometry Index.
Last verified . 2 shared benchmarks.
DeepSeekSummary
- They share 2 benchmarks with published results for both. DeepSeek-V3.2-Speciale scores higher in 2 categories and GPT-5 Mini in 1 category; 2 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GPT-5 Mini leads 55.2 to 46.0.
- The biggest single-benchmark swing is WeirdML: 46.7% for DeepSeek-V3.2-Speciale and 52.7% for GPT-5 Mini.
- GPT-5 Mini is cheaper at $0.25 / $2 per million input/output tokens, against $0.58 / $1.68 for DeepSeek-V3.2-Speciale.
- GPT-5 Mini accepts more context: 400K 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 Mini | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 39.7 | 41.8 |
| Released | 2025-12-01 | 2025-08-07 |
| Weights | Open | Proprietary |
| Context window | 128K | 400K |
| Max output | 128K | 128K |
| Input $ / M tokens | $0.58 | $0.25 |
| Output $ / M tokens | $1.68 | $2 |
| Results tracked | 3 | 60 |
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Category by category
Coding Too close to call
DeepSeek-V3.2-Speciale: 40.4 (#140), GPT-5 Mini: 40.1 (#146)
| Benchmark | DeepSeek-V3.2-Speciale | GPT-5 Mini |
|---|---|---|
| WeirdML | 46.7% | 52.7% |
| SWE-bench Verified | — | 64.7% |
| SWE-bench Verified (bash only) | — | 59.8% |
| SWE-bench Multilingual | — | 39.7% |
| SciCode | — | 39.2% |
| LMArena Coding | — | 1406 |
| ALE-Bench | — | 799.77 |
| AlgoTune | — | 1.38 |
Agentic & Tool Use Not comparable
DeepSeek-V3.2-Speciale: —, GPT-5 Mini: 31.1 (#70)
| Benchmark | DeepSeek-V3.2-Speciale | GPT-5 Mini |
|---|---|---|
| Terminal-Bench | — | 34.8% |
| Berkeley Function Calling Leaderboard | — | 55.5% |
| Vending-Bench 2 | — | -31.18 |
Reasoning DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 32.9 (#73), GPT-5 Mini: 23.9 (#168)
| Benchmark | DeepSeek-V3.2-Speciale | GPT-5 Mini |
|---|---|---|
| ARC-AGI-2 | — | 4.4% |
| SimpleBench | 52.6% | — |
| Kagi LLM Benchmark | — | 70.3% |
| ARC-AGI-1 | — | 54.3% |
| CritPt | — | 0% |
| Chess Puzzles | — | 30% |
| EnigmaEval | — | 8.2% |
| LMArena Hard Prompts | — | 1380 |
| Mystery Game Puzzles | — | 10% |
| DTBench | — | 80.5% |
| LMCA | — | 34.2% |
| Epoch Capabilities Index | — | 145.52 |
| ForecastBench | — | 61 |
Math Not comparable
DeepSeek-V3.2-Speciale: —, GPT-5 Mini: 46.7 (#69)
| Benchmark | DeepSeek-V3.2-Speciale | GPT-5 Mini |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 46.7% |
| FrontierMath Tier 4 | — | 12.2% |
| OTIS Mock AIME 2024-2025 | — | 86.7% |
| ProofBench | — | 9% |
| Omni-MATH | — | 72.2% |
| LMArena Math | — | 1378 |
| MATH Level 5 | — | 97.8% |
| FrontierMath (Feb 2025 set) | — | 27.2% |
| FrontierMath Tier 4 (v1) | — | 6.3% |
Knowledge Not comparable
DeepSeek-V3.2-Speciale: —, GPT-5 Mini: 45.6 (#86)
| Benchmark | DeepSeek-V3.2-Speciale | GPT-5 Mini |
|---|---|---|
| GPQA Diamond | — | 75% |
| Humanity's Last Exam | — | 19.4% |
| SimpleQA Verified | — | 21.6% |
| MMLU-Pro | — | 83.5% |
| Confabulations | — | 13.3% |
| Vectara Hallucination Rate | — | 12.9% |
| GPQA (HELM) | — | 75.6% |
| LMArena Expert | — | 1379 |
Multimodal Not comparable
DeepSeek-V3.2-Speciale: —, GPT-5 Mini: 35.6 (#85)
| Benchmark | DeepSeek-V3.2-Speciale | GPT-5 Mini |
|---|---|---|
| LMArena Vision | — | 1202 |
| VPCT | — | 40.2% |
Multilingual Not comparable
DeepSeek-V3.2-Speciale: —, GPT-5 Mini: 48.9 (#137)
| Benchmark | DeepSeek-V3.2-Speciale | GPT-5 Mini |
|---|---|---|
| LMArena Non-English | — | 1363 |
| LMArena Chinese | — | 1385 |
| LMArena French | — | 1386 |
| LMArena German | — | 1366 |
| LMArena Japanese | — | 1341 |
| LMArena Korean | — | 1308 |
| LMArena Russian | — | 1362 |
| LMArena Spanish | — | 1355 |
Instruction Following Not comparable
DeepSeek-V3.2-Speciale: —, GPT-5 Mini: 76.2 (#46)
| Benchmark | DeepSeek-V3.2-Speciale | GPT-5 Mini |
|---|---|---|
| IFEval | — | 92.7% |
| LMArena Instruction Following | — | 1357 |
Long Context Not comparable
DeepSeek-V3.2-Speciale: —, GPT-5 Mini: 41.9 (#132)
| Benchmark | DeepSeek-V3.2-Speciale | GPT-5 Mini |
|---|---|---|
| Fiction.LiveBench | — | 69.4% |
| LMArena Longer Query | — | 1355 |
Writing & Preference GPT-5 Mini leads
DeepSeek-V3.2-Speciale: 46.0 (#222), GPT-5 Mini: 55.2 (#148)
| Benchmark | DeepSeek-V3.2-Speciale | GPT-5 Mini |
|---|---|---|
| EQ-Bench Creative Writing | 1276 | 1313 |
| LMArena Text | — | 1373 |
| LMArena Creative Writing | — | 1325 |
| Short-Story Creative Writing | — | 83.1% |
| WildBench | — | 85.5% |
| LMArena Multi-Turn | — | 1363 |
Frequently asked questions
Is DeepSeek-V3.2-Speciale better than GPT-5 Mini?
GPT-5 Mini is the stronger model overall, scoring 41.8 to 39.7 on the Noometry Index.
Which is cheaper, DeepSeek-V3.2-Speciale or GPT-5 Mini?
GPT-5 Mini is cheaper. It lists at $0.25 per million input tokens and $2 per million output tokens; DeepSeek-V3.2-Speciale lists at $0.58 and $1.68.
Is DeepSeek-V3.2-Speciale or GPT-5 Mini better for coding?
They score almost the same on coding (40.4 vs 40.1); test both on your own repository before choosing.
Which has the bigger context window?
GPT-5 Mini does, with 400K tokens against 128K.
How many benchmarks do DeepSeek-V3.2-Speciale and GPT-5 Mini share?
2 benchmarks have published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and GPT-5 Mini has 60.