Model comparison
DeepSeek-V3.2-Exp vs GPT-4.1
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 35.9 on the Noometry Index.
Last verified . 35 shared benchmarks.
DeepSeekSummary
- They share 35 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 8 categories and GPT-4.1 in 1 category; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where DeepSeek-V3.2-Exp leads 41.7 to 22.3.
- The biggest single-benchmark swing is ARC-AGI-1: 57% for DeepSeek-V3.2-Exp and 5.5% for GPT-4.1.
- DeepSeek-V3.2-Exp is cheaper at $0.26 / $0.38 per million input/output tokens, against $2 / $8 for GPT-4.1.
- GPT-4.1 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 | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 44.3 | 35.9 |
| Released | 2025-09-29 | 2025-04-14 |
| Weights | Open | Proprietary |
| Context window | 164K | 1.05M |
| Max output | 66K | 33K |
| Input $ / M tokens | $0.26 | $2 |
| Output $ / M tokens | $0.38 | $8 |
| Results tracked | 49 | 52 |
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Category by category
Coding DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 46.5 (#65), GPT-4.1: 34.4 (#238)
| Benchmark | DeepSeek-V3.2-Exp | GPT-4.1 |
|---|---|---|
| SWE-bench Verified (bash only) | 70% | 39.6% |
| Aider Polyglot | 74.2% | 52.4% |
| WeirdML | 39.5% | 39% |
| LMArena Coding | 1454 | 1391 |
| SWE-bench Verified | — | 48.5% |
| LMArena WebDev | 1362 | — |
| SWE-bench Multilingual | 59% | — |
| SciCode | 38.9% | — |
| CadEval | — | 42% |
| ALE-Bench | — | 558.1 |
Agentic & Tool Use GPT-4.1 leads
DeepSeek-V3.2-Exp: 32.7 (#59), GPT-4.1: 34.7 (#43)
| Benchmark | DeepSeek-V3.2-Exp | GPT-4.1 |
|---|---|---|
| Berkeley Function Calling Leaderboard | 56.7% | 54% |
| Terminal-Bench | 39.6% | — |
| APEX-Agents | 21.3% | — |
| TheAgentCompany | 42.9% | — |
| Vending-Bench 2 | 1,034 | — |
Reasoning DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 22.1 (#208), GPT-4.1: 11.7 (#339)
| Benchmark | DeepSeek-V3.2-Exp | GPT-4.1 |
|---|---|---|
| ARC-AGI-2 | 4% | 0.4% |
| Kagi LLM Benchmark | 52.2% | 52.3% |
| ARC-AGI-1 | 57% | 5.5% |
| Chess Puzzles | 14% | 6% |
| LMArena Hard Prompts | 1434 | 1384 |
| DTBench | 87.7% | 68.3% |
| LMCA | 29.1% | 25.6% |
| Epoch Capabilities Index | 146.27 | 136.78 |
| SimpleBench | — | 27% |
| NYT Connections (extended) | 36.7% | — |
| CritPt | 2.9% | — |
| EnigmaEval | — | 2.2% |
| Thematic Generalization | 65% | — |
| ForecastBench | — | 61.5 |
Math DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 41.7 (#87), GPT-4.1: 22.3 (#280)
| Benchmark | DeepSeek-V3.2-Exp | GPT-4.1 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 87.8% | 38.3% |
| LMArena Math | 1435 | 1370 |
| FrontierMath (Feb 2025 set) | 22.1% | 5.5% |
| FrontierMath Tier 4 (v1) | 2.1% | 0% |
| FrontierMath (Tiers 1-3) | — | 6% |
| MathArena Final-Answer Competitions | 57.7% | — |
| ProofBench | 8% | — |
| Omni-MATH | — | 47.1% |
| MATH Level 5 | — | 83% |
Knowledge DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 51.7 (#66), GPT-4.1: 37.1 (#160)
| Benchmark | DeepSeek-V3.2-Exp | GPT-4.1 |
|---|---|---|
| GPQA Diamond | 83.4% | 66.9% |
| Vectara Hallucination Rate | 5.3% | 5.6% |
| LMArena Expert | 1436 | 1364 |
| Humanity's Last Exam | — | 5.4% |
| SimpleQA Verified | — | 31.1% |
| MMLU-Pro | — | 81.1% |
| GPQA (HELM) | — | 65.9% |
Multimodal Not comparable
DeepSeek-V3.2-Exp: —, GPT-4.1: 38.2 (#67)
| Benchmark | DeepSeek-V3.2-Exp | GPT-4.1 |
|---|---|---|
| LMArena Vision | — | 1211 |
| GeoBench | — | 72% |
Multilingual DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 52.2 (#90), GPT-4.1: 49.4 (#133)
| Benchmark | DeepSeek-V3.2-Exp | GPT-4.1 |
|---|---|---|
| LMArena Non-English | 1409 | 1370 |
| LMArena Chinese | 1461 | 1382 |
| LMArena French | 1433 | 1382 |
| LMArena German | 1440 | 1381 |
| LMArena Japanese | 1374 | 1319 |
| LMArena Korean | 1371 | 1339 |
| LMArena Russian | 1424 | 1377 |
| LMArena Spanish | 1440 | 1376 |
Instruction Following DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 74.5 (#93), GPT-4.1: 71.3 (#153)
| Benchmark | DeepSeek-V3.2-Exp | GPT-4.1 |
|---|---|---|
| LMArena Instruction Following | 1413 | 1367 |
| IFEval | — | 83.8% |
Long Context DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 47.6 (#16), GPT-4.1: 40.0 (#163)
| Benchmark | DeepSeek-V3.2-Exp | GPT-4.1 |
|---|---|---|
| Fiction.LiveBench | 83.3% | 63.9% |
| LMArena Longer Query | 1428 | 1385 |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |
Writing & Preference DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 62.4 (#77), GPT-4.1: 57.6 (#125)
| Benchmark | DeepSeek-V3.2-Exp | GPT-4.1 |
|---|---|---|
| LMArena Text | 1425 | 1383 |
| LMArena Creative Writing | 1403 | 1363 |
| EQ-Bench Creative Writing | 1515 | 1420 |
| LMArena Multi-Turn | 1427 | 1398 |
| WildBench | — | 85.4% |
Frequently asked questions
Is DeepSeek-V3.2-Exp better than GPT-4.1?
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 35.9 on the Noometry Index.
Which is cheaper, DeepSeek-V3.2-Exp or GPT-4.1?
DeepSeek-V3.2-Exp is cheaper. It lists at $0.26 per million input tokens and $0.38 per million output tokens; GPT-4.1 lists at $2 and $8.
Is DeepSeek-V3.2-Exp or GPT-4.1 better for coding?
DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.5 versus 34.4 in the Noometry coding category.
Which has the bigger context window?
GPT-4.1 does, with 1.05M tokens against 164K.
How many benchmarks do DeepSeek-V3.2-Exp and GPT-4.1 share?
35 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 49 scored results on Noometry and GPT-4.1 has 52.