Model comparison
DeepSeek-V3.2-Exp vs GPT-4.1 nano
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.9 to 27.7 on the Noometry Index. GPT-4.1 nano costs 1.7× less per token, which makes it the better buy when DeepSeek-V3.2-Exp's lead doesn't matter for your workload.
Last verified . 30 shared benchmarks.
DeepSeekSummary
- They share 30 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 9 categories and GPT-4.1 nano in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where DeepSeek-V3.2-Exp leads 51.6 to 21.8.
- The biggest single-benchmark swing is Aider Polyglot: 74.2% for DeepSeek-V3.2-Exp and 8.9% for GPT-4.1 nano.
- GPT-4.1 nano is cheaper at $0.10 / $0.40 per million input/output tokens, against $0.26 / $0.38 for DeepSeek-V3.2-Exp.
- GPT-4.1 nano accepts more context: 1.05M tokens versus 164K.
- DeepSeek-V3.2-Exp has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3.2-Exp | GPT-4.1 nano | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 44.9 | 27.7 |
| Released | 2025-09-29 | 2025-04-14 |
| Weights | Open | Proprietary |
| Context window | 164K | 1.05M |
| Max output | 66K | 33K |
| Input $ / M tokens | $0.26 | $0.10 |
| Output $ / M tokens | $0.38 | $0.40 |
| Results tracked | 48 | 38 |
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Category by category
Coding DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 46.4 (#61), GPT-4.1 nano: 23.8 (#325)
| Benchmark | DeepSeek-V3.2-Exp | GPT-4.1 nano |
|---|---|---|
| Aider Polyglot | 74.2% | 8.9% |
| SciCode | 38.9% | 25.9% |
| WeirdML | 39.5% | 19% |
| LMArena Coding | 1454 | 1306 |
| SWE-bench Verified (bash only) | 70% | — |
| LMArena WebDev | 1362 | — |
| SWE-bench Multilingual | 59% | — |
Agentic & Tool Use DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 37.9 (#31), GPT-4.1 nano: 26.3 (#98)
| Benchmark | DeepSeek-V3.2-Exp | GPT-4.1 nano |
|---|---|---|
| Berkeley Function Calling Leaderboard | 56.7% | 33% |
| Terminal-Bench | 39.6% | — |
| TheAgentCompany | 42.9% | — |
| Vending-Bench 2 | 1,034 | — |
Reasoning DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 22.0 (#185), GPT-4.1 nano: 8.6 (#344)
| Benchmark | DeepSeek-V3.2-Exp | GPT-4.1 nano |
|---|---|---|
| ARC-AGI-2 | 4% | 0% |
| Kagi LLM Benchmark | 52.2% | 33.3% |
| ARC-AGI-1 | 57% | 0% |
| CritPt | 2.9% | 0% |
| LMArena Hard Prompts | 1433 | 1286 |
| DTBench | 87.7% | 52.5% |
| LMCA | 29.1% | 5.5% |
| Epoch Capabilities Index | 146.28 | 129.62 |
| NYT Connections (extended) | 36.7% | — |
| Chess Puzzles | 14% | — |
| Thematic Generalization | 65% | — |
Math DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 40.8 (#75), GPT-4.1 nano: 25.5 (#246)
| Benchmark | DeepSeek-V3.2-Exp | GPT-4.1 nano |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 87.8% | 28.9% |
| LMArena Math | 1435 | 1274 |
| FrontierMath (Feb 2025 set) | 22.1% | 1% |
| MathArena Final-Answer Competitions | 57.7% | — |
| ProofBench | 8% | — |
| Omni-MATH | — | 36.7% |
| MATH Level 5 | — | 70% |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 51.6 (#61), GPT-4.1 nano: 21.8 (#268)
| Benchmark | DeepSeek-V3.2-Exp | GPT-4.1 nano |
|---|---|---|
| GPQA Diamond | 83.4% | 48.9% |
| LMArena Expert | 1436 | 1272 |
| SimpleQA Verified | — | 6% |
| MMLU-Pro | — | 55% |
| Vectara Hallucination Rate | 5.3% | — |
| GPQA (HELM) | — | 50.7% |
Multimodal Not comparable
DeepSeek-V3.2-Exp: —, GPT-4.1 nano: 28.6 (#108)
| Benchmark | DeepSeek-V3.2-Exp | GPT-4.1 nano |
|---|---|---|
| LMArena Vision | — | 1063 |
Multilingual DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 52.4 (#85), GPT-4.1 nano: 41.8 (#200)
| Benchmark | DeepSeek-V3.2-Exp | GPT-4.1 nano |
|---|---|---|
| LMArena Non-English | 1409 | 1260 |
| LMArena Chinese | 1461 | 1270 |
| LMArena German | 1440 | 1288 |
| LMArena Japanese | 1377 | 1199 |
| LMArena Russian | 1423 | 1261 |
| LMArena French | 1435 | — |
| LMArena Korean | 1372 | — |
| LMArena Spanish | 1441 | — |
Instruction Following DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 74.7 (#89), GPT-4.1 nano: 68.0 (#188)
| Benchmark | DeepSeek-V3.2-Exp | GPT-4.1 nano |
|---|---|---|
| LMArena Instruction Following | 1413 | 1267 |
| IFEval | — | 84.3% |
Long Context DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 47.6 (#15), GPT-4.1 nano: 23.7 (#291)
| Benchmark | DeepSeek-V3.2-Exp | GPT-4.1 nano |
|---|---|---|
| Fiction.LiveBench | 83.3% | 25% |
| LMArena Longer Query | 1428 | 1283 |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |
Writing & Preference DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 62.5 (#73), GPT-4.1 nano: 40.7 (#238)
| Benchmark | DeepSeek-V3.2-Exp | GPT-4.1 nano |
|---|---|---|
| LMArena Text | 1425 | 1285 |
| LMArena Creative Writing | 1403 | 1260 |
| EQ-Bench Creative Writing | 1515 | 946 |
| LMArena Multi-Turn | 1427 | 1277 |
| WildBench | — | 81.2% |
Frequently asked questions
Is DeepSeek-V3.2-Exp better than GPT-4.1 nano?
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.9 to 27.7 on the Noometry Index. GPT-4.1 nano costs 1.7× less per token, which makes it the better buy when DeepSeek-V3.2-Exp's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3.2-Exp or GPT-4.1 nano?
GPT-4.1 nano is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; DeepSeek-V3.2-Exp lists at $0.26 and $0.38.
Is DeepSeek-V3.2-Exp or GPT-4.1 nano better for coding?
DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.4 versus 23.8 in the Noometry coding category.
Which has the bigger context window?
GPT-4.1 nano does, with 1.05M tokens against 164K.
How many benchmarks do DeepSeek-V3.2-Exp and GPT-4.1 nano share?
30 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 48 scored results on Noometry and GPT-4.1 nano has 38.