Model comparison
DeepSeek-V3.2-Speciale vs GPT-5 Nano
DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.7 to 33.5 on the Noometry Index. GPT-5 Nano costs 6.2× less per token, which makes it the better buy when DeepSeek-V3.2-Speciale'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 3 categories and GPT-5 Nano in 0 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where DeepSeek-V3.2-Speciale leads 32.9 to 16.3.
- The biggest single-benchmark swing is WeirdML: 46.7% for DeepSeek-V3.2-Speciale and 38.1% for GPT-5 Nano.
- GPT-5 Nano is cheaper at $0.05 / $0.40 per million input/output tokens, against $0.58 / $1.68 for DeepSeek-V3.2-Speciale.
- GPT-5 Nano 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 Nano | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 39.7 | 33.5 |
| Released | 2025-12-01 | 2025-08-07 |
| Weights | Open | Proprietary |
| Context window | 128K | 400K |
| Max output | 128K | 128K |
| Input $ / M tokens | $0.58 | $0.05 |
| Output $ / M tokens | $1.68 | $0.40 |
| Results tracked | 3 | 49 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 40.4 (#140), GPT-5 Nano: 33.6 (#254)
| Benchmark | DeepSeek-V3.2-Speciale | GPT-5 Nano |
|---|---|---|
| WeirdML | 46.7% | 38.1% |
| SWE-bench Verified (bash only) | — | 34.8% |
| LMArena Coding | — | 1351 |
| ALE-Bench | — | 718.67 |
Agentic & Tool Use Not comparable
DeepSeek-V3.2-Speciale: —, GPT-5 Nano: 25.8 (#106)
| Benchmark | DeepSeek-V3.2-Speciale | GPT-5 Nano |
|---|---|---|
| Terminal-Bench | — | 21.8% |
| Berkeley Function Calling Leaderboard | — | 51.5% |
Reasoning DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 32.9 (#73), GPT-5 Nano: 16.3 (#306)
| Benchmark | DeepSeek-V3.2-Speciale | GPT-5 Nano |
|---|---|---|
| ARC-AGI-2 | — | 2.6% |
| SimpleBench | 52.6% | — |
| Kagi LLM Benchmark | — | 62.2% |
| ARC-AGI-1 | — | 20.7% |
| Chess Puzzles | — | 27% |
| LMArena Hard Prompts | — | 1328 |
| Mystery Game Puzzles | — | 9% |
| DTBench | — | 62.7% |
| LMCA | — | 7.9% |
| Epoch Capabilities Index | — | 139.38 |
| ForecastBench | — | 59.1 |
Math Not comparable
DeepSeek-V3.2-Speciale: —, GPT-5 Nano: 29.4 (#241)
| Benchmark | DeepSeek-V3.2-Speciale | GPT-5 Nano |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 20% |
| FrontierMath Tier 4 | — | 2.4% |
| OTIS Mock AIME 2024-2025 | — | 81.1% |
| ProofBench | — | 12% |
| Omni-MATH | — | 54.6% |
| LMArena Math | — | 1317 |
| MATH Level 5 | — | 95.2% |
| FrontierMath (Feb 2025 set) | — | 8.3% |
| FrontierMath Tier 4 (v1) | — | 2.1% |
Knowledge Not comparable
DeepSeek-V3.2-Speciale: —, GPT-5 Nano: 35.9 (#178)
| Benchmark | DeepSeek-V3.2-Speciale | GPT-5 Nano |
|---|---|---|
| GPQA Diamond | — | 69.4% |
| SimpleQA Verified | — | 11.7% |
| MMLU-Pro | — | 77.8% |
| Vectara Hallucination Rate | — | 10.5% |
| GPQA (HELM) | — | 67.9% |
| LMArena Expert | — | 1321 |
Multimodal Not comparable
DeepSeek-V3.2-Speciale: —, GPT-5 Nano: 31.3 (#108)
| Benchmark | DeepSeek-V3.2-Speciale | GPT-5 Nano |
|---|---|---|
| LMArena Vision | — | 1159 |
| VPCT | — | 37.2% |
Multilingual Not comparable
DeepSeek-V3.2-Speciale: —, GPT-5 Nano: 45.3 (#172)
| Benchmark | DeepSeek-V3.2-Speciale | GPT-5 Nano |
|---|---|---|
| LMArena Non-English | — | 1313 |
| LMArena Chinese | — | 1356 |
| LMArena German | — | 1327 |
| LMArena Japanese | — | 1226 |
| LMArena Korean | — | 1269 |
| LMArena Russian | — | 1296 |
| LMArena Spanish | — | 1360 |
Instruction Following Not comparable
DeepSeek-V3.2-Speciale: —, GPT-5 Nano: 75.0 (#79)
| Benchmark | DeepSeek-V3.2-Speciale | GPT-5 Nano |
|---|---|---|
| IFEval | — | 93.2% |
| LMArena Instruction Following | — | 1306 |
Long Context Not comparable
DeepSeek-V3.2-Speciale: —, GPT-5 Nano: 31.3 (#281)
| Benchmark | DeepSeek-V3.2-Speciale | GPT-5 Nano |
|---|---|---|
| Fiction.LiveBench | — | 44.4% |
| LMArena Longer Query | — | 1312 |
Writing & Preference DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 46.0 (#222), GPT-5 Nano: 39.1 (#249)
| Benchmark | DeepSeek-V3.2-Speciale | GPT-5 Nano |
|---|---|---|
| EQ-Bench Creative Writing | 1276 | 705 |
| LMArena Text | — | 1320 |
| LMArena Creative Writing | — | 1249 |
| WildBench | — | 80.6% |
| LMArena Multi-Turn | — | 1311 |
Frequently asked questions
Is DeepSeek-V3.2-Speciale better than GPT-5 Nano?
DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.7 to 33.5 on the Noometry Index. GPT-5 Nano costs 6.2× less per token, which makes it the better buy when DeepSeek-V3.2-Speciale's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3.2-Speciale or GPT-5 Nano?
GPT-5 Nano is cheaper. It lists at $0.05 per million input tokens and $0.40 per million output tokens; DeepSeek-V3.2-Speciale lists at $0.58 and $1.68.
Is DeepSeek-V3.2-Speciale or GPT-5 Nano better for coding?
DeepSeek-V3.2-Speciale scores higher on coding benchmarks: 40.4 versus 33.6 in the Noometry coding category.
Which has the bigger context window?
GPT-5 Nano does, with 400K tokens against 128K.
How many benchmarks do DeepSeek-V3.2-Speciale and GPT-5 Nano share?
2 benchmarks have published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and GPT-5 Nano has 49.