# DeepSeek-V3 vs o1

> o1 is the stronger model overall, scoring 40.6 to 39.2 on the Noometry Index. DeepSeek-V3 costs 65× less per token, which makes it the better buy when o1's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/deepseek-v3-vs-o1
- Last updated: 2026-10-10
- Shared benchmarks: 40

## Summary

- They share 40 benchmarks with published results for both. DeepSeek-V3 scores higher in 1 category and o1 in 7 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in long context, where o1 leads 50.3 to 34.0.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 37.8% for DeepSeek-V3 and 73.3% for o1.
- DeepSeek-V3 is cheaper at $0.24 / $0.90 per million input/output tokens, against $15 / $60 for o1.
- o1 accepts more context: 200K tokens versus 164K.
- DeepSeek-V3 has downloadable open weights; the other is API-only.

## Snapshot

| | DeepSeek-V3 | o1 |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 39.2 | 40.6 |
| Rank | 157 | 140 |
| Context | 164K | 200K |
| Input $/M | $0.24 | $15 |
| Output $/M | $0.90 | $60 |
| Weights | Open | Proprietary |

## Coding

- DeepSeek-V3: 41.9 (#99)
- o1: 45.7 (#64)

| Benchmark | DeepSeek-V3 | o1 |
|---|---|---|
| Aider Polyglot | 55.1% | 61.7% |
| WeirdML | 36.1% | 47.6% |
| LiveBench Coding | 70.9% | 69.7% |
| LMArena Coding | 1368 | 1367 |
| HumanEval+ | 86.6% | 89% |
| MBPP+ | 73% | 80.2% |
| SciCode | 35.8% | — |
| BigCodeBench Instruct | 50% | — |
| BigCodeBench Complete | 62.2% | — |
| CadEval | — | 56% |

## Agentic & Tool Use

- DeepSeek-V3: —
- o1: 24.3 (#111)

| Benchmark | DeepSeek-V3 | o1 |
|---|---|---|
| METR Time Horizons | 49.6% | 51.1% |
| Cybench | — | 10% |

## Reasoning

- DeepSeek-V3: 20.0 (#226)
- o1: 27.5 (#103)

| Benchmark | DeepSeek-V3 | o1 |
|---|---|---|
| SimpleBench | 27.2% | 41.7% |
| LiveBench Reasoning | 65.8% | 91.6% |
| LMArena Hard Prompts | 1365 | 1372 |
| DTBench | 64.8% | 74.7% |
| LiveBench Data Analysis | 60.9% | 65.5% |
| LMCA | 15.5% | 22.3% |
| Epoch Capabilities Index | 135.95 | 141.92 |
| LiveBench | 66.9% | 75.7% |
| Kagi LLM Benchmark | 52.3% | — |
| ARC-AGI-1 | — | 30.7% |
| CritPt | 0% | — |
| Chess Puzzles | — | 15% |
| EnigmaEval | — | 5.7% |
| BIG-Bench Hard | 87.5% | — |
| ForecastBench | 59.1 | — |
| HellaSwag | 88.9% | — |
| PIQA | 84.7% | — |
| WinoGrande | 85.2% | — |

## Math

- DeepSeek-V3: 30.4 (#208)
- o1: 34.9 (#150)

| Benchmark | DeepSeek-V3 | o1 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 37.8% | 73.3% |
| LiveBench Math | 73.5% | 80.3% |
| LMArena Math | 1373 | 1388 |
| MATH Level 5 | 75.5% | 94.7% |
| FrontierMath (Feb 2025 set) | 1.7% | 9.3% |
| FrontierMath (Tiers 1-3) | — | 14.7% |
| Omni-MATH | 40.3% | — |

## Knowledge

- DeepSeek-V3: 37.5 (#147)
- o1: 41.5 (#101)

| Benchmark | DeepSeek-V3 | o1 |
|---|---|---|
| GPQA Diamond | 67.6% | 76.8% |
| Confabulations | 26.1% | 11.7% |
| LMArena Expert | 1352 | 1362 |
| Humanity's Last Exam | — | 8% |
| SimpleQA Verified | — | 41.1% |
| MMLU-Pro | 72.3% | — |
| Vectara Hallucination Rate | 6.1% | — |
| GPQA (HELM) | 53.8% | — |
| ARC (AI2) Challenge | 95.3% | — |
| MMLU | 87.2% | — |
| TriviaQA | 82.9% | — |

## Multimodal

- DeepSeek-V3: —
- o1: 33.6 (#87)

| Benchmark | DeepSeek-V3 | o1 |
|---|---|---|
| LMArena Vision | — | 1168 |
| GeoBench | — | 80% |
| VPCT | — | 37% |
| SpatialViz-Bench | — | 41.4% |

## Multilingual

- DeepSeek-V3: 48.8 (#138)
- o1: 48.8 (#137)

| Benchmark | DeepSeek-V3 | o1 |
|---|---|---|
| LMArena Non-English | 1358 | 1359 |
| LMArena Chinese | 1390 | 1394 |
| LMArena French | 1386 | 1343 |
| LMArena German | 1374 | 1337 |
| LMArena Japanese | 1333 | 1346 |
| LMArena Korean | 1321 | 1396 |
| LMArena Russian | 1372 | 1356 |
| LMArena Spanish | 1358 | 1345 |

## Instruction Following

- DeepSeek-V3: 73.0 (#125)
- o1: 75.0 (#82)

| Benchmark | DeepSeek-V3 | o1 |
|---|---|---|
| LiveBench Instruction Following | 81.5% | 81.5% |
| LMArena Instruction Following | 1345 | 1368 |
| IFEval | 83.2% | — |

## Long Context

- DeepSeek-V3: 34.0 (#248)
- o1: 50.3 (#9)

| Benchmark | DeepSeek-V3 | o1 |
|---|---|---|
| Fiction.LiveBench | 50% | 83.3% |
| LMArena Longer Query | 1353 | 1378 |

## Writing & Preference

- DeepSeek-V3: 57.6 (#124)
- o1: 55.8 (#139)

| Benchmark | DeepSeek-V3 | o1 |
|---|---|---|
| LMArena Text | 1375 | 1366 |
| LMArena Creative Writing | 1364 | 1347 |
| Short-Story Creative Writing | 77% | 70.2% |
| LMArena Multi-Turn | 1389 | 1368 |
| LiveBench Language | 49.1% | 65.4% |
| EQ-Bench Creative Writing | 1472 | — |
| WildBench | 83% | — |

## FAQ

### Is DeepSeek-V3 better than o1?

o1 is the stronger model overall, scoring 40.6 to 39.2 on the Noometry Index. DeepSeek-V3 costs 65× less per token, which makes it the better buy when o1's lead doesn't matter for your workload.

### Which is cheaper, DeepSeek-V3 or o1?

DeepSeek-V3 is cheaper. It lists at $0.24 per million input tokens and $0.90 per million output tokens; o1 lists at $15 and $60.

### Is DeepSeek-V3 or o1 better for coding?

o1 scores higher on coding benchmarks: 45.7 versus 41.9 in the Noometry coding category.

### Which has the bigger context window?

o1 does, with 200K tokens against 164K.

### How many benchmarks do DeepSeek-V3 and o1 share?

40 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and o1 has 52.