Model comparison
DeepSeek-V3.2-Exp vs o1-pro
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 31.5 on the Noometry Index.
Last verified . 1 shared benchmarks.
DeepSeekSummary
- They share 1 benchmark with published results for both. DeepSeek-V3.2-Exp scores higher in 2 categories and o1-pro in 0 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where DeepSeek-V3.2-Exp leads 51.7 to 29.7.
- The biggest single-benchmark swing is ARC-AGI-1: 57% for DeepSeek-V3.2-Exp and 23.3% for o1-pro.
- DeepSeek-V3.2-Exp is cheaper at $0.26 / $0.38 per million input/output tokens, against $150 / $600 for o1-pro.
- o1-pro accepts more context: 200K tokens versus 164K.
- DeepSeek-V3.2-Exp has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3.2-Exp | o1-pro | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 44.3 | 31.5 |
| Released | 2025-09-29 | 2025-03-19 |
| Weights | Open | Proprietary |
| Context window | 164K | 200K |
| Max output | 66K | 100K |
| Input $ / M tokens | $0.26 | $150 |
| Output $ / M tokens | $0.38 | $600 |
| Results tracked | 49 | 3 |
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Category by category
Coding Not comparable
DeepSeek-V3.2-Exp: 46.5 (#65), o1-pro: —
| Benchmark | DeepSeek-V3.2-Exp | o1-pro |
|---|---|---|
| SWE-bench Verified (bash only) | 70% | — |
| Aider Polyglot | 74.2% | — |
| LMArena WebDev | 1362 | — |
| SWE-bench Multilingual | 59% | — |
| SciCode | 38.9% | — |
| WeirdML | 39.5% | — |
| LMArena Coding | 1454 | — |
Agentic & Tool Use Not comparable
DeepSeek-V3.2-Exp: 32.7 (#59), o1-pro: —
| Benchmark | DeepSeek-V3.2-Exp | o1-pro |
|---|---|---|
| Terminal-Bench | 39.6% | — |
| APEX-Agents | 21.3% | — |
| Berkeley Function Calling Leaderboard | 56.7% | — |
| TheAgentCompany | 42.9% | — |
| Vending-Bench 2 | 1,034 | — |
Reasoning DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 22.1 (#208), o1-pro: 20.4 (#239)
| Benchmark | DeepSeek-V3.2-Exp | o1-pro |
|---|---|---|
| ARC-AGI-1 | 57% | 23.3% |
| ARC-AGI-2 | 4% | — |
| Kagi LLM Benchmark | 52.2% | — |
| NYT Connections (extended) | 36.7% | — |
| CritPt | 2.9% | — |
| Chess Puzzles | 14% | — |
| EnigmaEval | — | 6.1% |
| Thematic Generalization | 65% | — |
| LMArena Hard Prompts | 1434 | — |
| DTBench | 87.7% | — |
| LMCA | 29.1% | — |
| Epoch Capabilities Index | 146.27 | — |
Math Not comparable
DeepSeek-V3.2-Exp: 41.7 (#87), o1-pro: —
| Benchmark | DeepSeek-V3.2-Exp | o1-pro |
|---|---|---|
| MathArena Final-Answer Competitions | 57.7% | — |
| OTIS Mock AIME 2024-2025 | 87.8% | — |
| ProofBench | 8% | — |
| LMArena Math | 1435 | — |
| FrontierMath (Feb 2025 set) | 22.1% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 51.7 (#66), o1-pro: 29.7 (#234)
| Benchmark | DeepSeek-V3.2-Exp | o1-pro |
|---|---|---|
| GPQA Diamond | 83.4% | — |
| Humanity's Last Exam | — | 8.1% |
| Vectara Hallucination Rate | 5.3% | — |
| LMArena Expert | 1436 | — |
Multilingual Not comparable
DeepSeek-V3.2-Exp: 52.2 (#90), o1-pro: —
| Benchmark | DeepSeek-V3.2-Exp | o1-pro |
|---|---|---|
| LMArena Non-English | 1409 | — |
| LMArena Chinese | 1461 | — |
| LMArena French | 1433 | — |
| LMArena German | 1440 | — |
| LMArena Japanese | 1374 | — |
| LMArena Korean | 1371 | — |
| LMArena Russian | 1424 | — |
| LMArena Spanish | 1440 | — |
Instruction Following Not comparable
DeepSeek-V3.2-Exp: 74.5 (#93), o1-pro: —
| Benchmark | DeepSeek-V3.2-Exp | o1-pro |
|---|---|---|
| LMArena Instruction Following | 1413 | — |
Long Context Not comparable
DeepSeek-V3.2-Exp: 47.6 (#16), o1-pro: —
| Benchmark | DeepSeek-V3.2-Exp | o1-pro |
|---|---|---|
| Fiction.LiveBench | 83.3% | — |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |
| LMArena Longer Query | 1428 | — |
Writing & Preference Not comparable
DeepSeek-V3.2-Exp: 62.4 (#77), o1-pro: —
| Benchmark | DeepSeek-V3.2-Exp | o1-pro |
|---|---|---|
| LMArena Text | 1425 | — |
| LMArena Creative Writing | 1403 | — |
| EQ-Bench Creative Writing | 1515 | — |
| LMArena Multi-Turn | 1427 | — |
Frequently asked questions
Is DeepSeek-V3.2-Exp better than o1-pro?
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 31.5 on the Noometry Index.
Which is cheaper, DeepSeek-V3.2-Exp or o1-pro?
DeepSeek-V3.2-Exp is cheaper. It lists at $0.26 per million input tokens and $0.38 per million output tokens; o1-pro lists at $150 and $600.
Which has the bigger context window?
o1-pro does, with 200K tokens against 164K.
How many benchmarks do DeepSeek-V3.2-Exp and o1-pro share?
1 benchmark has published results for both models. DeepSeek-V3.2-Exp has 49 scored results on Noometry and o1-pro has 3.