Model comparison
DeepSeek-V3.2-Speciale vs GPT-4 Turbo
DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.7 to 30.5 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-4 Turbo in 1 category; 3 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where DeepSeek-V3.2-Speciale leads 32.9 to 15.3.
- The biggest single-benchmark swing is WeirdML: 46.7% for DeepSeek-V3.2-Speciale and 18% for GPT-4 Turbo.
- DeepSeek-V3.2-Speciale is cheaper at $0.58 / $1.68 per million input/output tokens, against $10 / $30 for GPT-4 Turbo.
- DeepSeek-V3.2-Speciale has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3.2-Speciale | GPT-4 Turbo | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 39.7 | 30.5 |
| Released | 2025-12-01 | 2023-11-06 |
| Weights | Open | Proprietary |
| Context window | 128K | 128K |
| Max output | 128K | 4K |
| Input $ / M tokens | $0.58 | $10 |
| Output $ / M tokens | $1.68 | $30 |
| Results tracked | 3 | 36 |
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Category by category
Coding DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 40.4 (#140), GPT-4 Turbo: 33.8 (#249)
| Benchmark | DeepSeek-V3.2-Speciale | GPT-4 Turbo |
|---|---|---|
| WeirdML | 46.7% | 18% |
| BigCodeBench Instruct | — | 48.2% |
| LMArena Coding | — | 1268 |
| BigCodeBench Complete | — | 58.2% |
| HumanEval+ | — | 86.6% |
| MBPP+ | — | 73.3% |
Agentic & Tool Use Not comparable
DeepSeek-V3.2-Speciale: —, GPT-4 Turbo: —
| Benchmark | DeepSeek-V3.2-Speciale | GPT-4 Turbo |
|---|---|---|
| METR Time Horizons | — | 36.7% |
Reasoning DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 32.9 (#73), GPT-4 Turbo: 15.3 (#317)
| Benchmark | DeepSeek-V3.2-Speciale | GPT-4 Turbo |
|---|---|---|
| SimpleBench | 52.6% | 25.1% |
| Chess Puzzles | — | 6% |
| LMArena Hard Prompts | — | 1251 |
| DTBench | — | 61.6% |
| LMCA | — | 9.8% |
| Epoch Capabilities Index | — | 127.25 |
| ForecastBench | — | 59.4 |
Math Not comparable
DeepSeek-V3.2-Speciale: —, GPT-4 Turbo: 9.0 (#322)
| Benchmark | DeepSeek-V3.2-Speciale | GPT-4 Turbo |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 0.7% |
| OTIS Mock AIME 2024-2025 | — | 6.7% |
| LMArena Math | — | 1272 |
| MATH Level 5 | — | 46.7% |
Knowledge Not comparable
DeepSeek-V3.2-Speciale: —, GPT-4 Turbo: 24.3 (#268)
| Benchmark | DeepSeek-V3.2-Speciale | GPT-4 Turbo |
|---|---|---|
| GPQA Diamond | — | 46.6% |
| Confabulations | — | 28.4% |
| LMArena Expert | — | 1223 |
| MMLU | — | 81.3% |
Multimodal Not comparable
DeepSeek-V3.2-Speciale: —, GPT-4 Turbo: 30.6 (#110)
| Benchmark | DeepSeek-V3.2-Speciale | GPT-4 Turbo |
|---|---|---|
| LMArena Vision | — | 1090 |
Multilingual Not comparable
DeepSeek-V3.2-Speciale: —, GPT-4 Turbo: 40.5 (#216)
| Benchmark | DeepSeek-V3.2-Speciale | GPT-4 Turbo |
|---|---|---|
| LMArena Non-English | — | 1245 |
| LMArena Chinese | — | 1242 |
| LMArena French | — | 1276 |
| LMArena German | — | 1259 |
| LMArena Japanese | — | 1194 |
| LMArena Korean | — | 1187 |
| LMArena Russian | — | 1259 |
| LMArena Spanish | — | 1260 |
Instruction Following Not comparable
DeepSeek-V3.2-Speciale: —, GPT-4 Turbo: 65.8 (#216)
| Benchmark | DeepSeek-V3.2-Speciale | GPT-4 Turbo |
|---|---|---|
| LMArena Instruction Following | — | 1249 |
Long Context Not comparable
DeepSeek-V3.2-Speciale: —, GPT-4 Turbo: 38.0 (#206)
| Benchmark | DeepSeek-V3.2-Speciale | GPT-4 Turbo |
|---|---|---|
| LMArena Longer Query | — | 1254 |
Writing & Preference GPT-4 Turbo leads
DeepSeek-V3.2-Speciale: 46.0 (#222), GPT-4 Turbo: 47.7 (#206)
| Benchmark | DeepSeek-V3.2-Speciale | GPT-4 Turbo |
|---|---|---|
| LMArena Text | — | 1272 |
| LMArena Creative Writing | — | 1269 |
| EQ-Bench Creative Writing | 1276 | — |
| LMArena Multi-Turn | — | 1267 |
Frequently asked questions
Is DeepSeek-V3.2-Speciale better than GPT-4 Turbo?
DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.7 to 30.5 on the Noometry Index.
Which is cheaper, DeepSeek-V3.2-Speciale or GPT-4 Turbo?
DeepSeek-V3.2-Speciale is cheaper. It lists at $0.58 per million input tokens and $1.68 per million output tokens; GPT-4 Turbo lists at $10 and $30.
Is DeepSeek-V3.2-Speciale or GPT-4 Turbo better for coding?
DeepSeek-V3.2-Speciale scores higher on coding benchmarks: 40.4 versus 33.8 in the Noometry coding category.
Which has the bigger context window?
Both accept 128K tokens.
How many benchmarks do DeepSeek-V3.2-Speciale and GPT-4 Turbo share?
2 benchmarks have published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and GPT-4 Turbo has 36.