Model comparison
DeepSeek-V3 vs o3-pro
o3-pro is the stronger model overall, scoring 42.9 to 39.5 on the Noometry Index. DeepSeek-V3 costs 86× less per token, which makes it the better buy when o3-pro's lead doesn't matter for your workload.
Last verified . 10 shared benchmarks.
DeepSeekSummary
- They share 10 benchmarks with published results for both. DeepSeek-V3 scores higher in 2 categories and o3-pro in 3 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in long context, where o3-pro leads 72.2 to 34.0.
- The biggest single-benchmark swing is Fiction.LiveBench: 50% for DeepSeek-V3 and 97.2% for o3-pro.
- DeepSeek-V3 is cheaper at $0.24 / $0.90 per million input/output tokens, against $20 / $80 for o3-pro.
- o3-pro accepts more context: 200K tokens versus 164K.
- DeepSeek-V3 has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3 | o3-pro | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 39.5 | 42.9 |
| Released | 2024-12-26 | 2025-06-10 |
| Weights | Open | Proprietary |
| Context window | 164K | 200K |
| Max output | 164K | 100K |
| Input $ / M tokens | $0.24 | $20 |
| Output $ / M tokens | $0.90 | $80 |
| Results tracked | 60 | 12 |
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Category by category
Coding o3-pro leads
DeepSeek-V3: 42.3 (#106), o3-pro: 55.5 (#24)
| Benchmark | DeepSeek-V3 | o3-pro |
|---|---|---|
| Aider Polyglot | 55.1% | 84.9% |
| WeirdML | 36.1% | 58.2% |
| SciCode | 35.8% | — |
| BigCodeBench Instruct | 50% | — |
| LiveBench Coding | 70.9% | — |
| LMArena Coding | 1368 | — |
| BigCodeBench Complete | 62.2% | — |
| HumanEval+ | 86.6% | — |
| MBPP+ | 73% | — |
Agentic & Tool Use Not comparable
DeepSeek-V3: —, o3-pro: —
| Benchmark | DeepSeek-V3 | o3-pro |
|---|---|---|
| METR Time Horizons | 49.6% | — |
Reasoning o3-pro leads
DeepSeek-V3: 20.5 (#236), o3-pro: 23.8 (#171)
| Benchmark | DeepSeek-V3 | o3-pro |
|---|---|---|
| Kagi LLM Benchmark | 52.3% | 72.1% |
| DTBench | 64.8% | 86.9% |
| LMCA | 15.5% | 38.5% |
| Epoch Capabilities Index | 135.94 | 147.42 |
| ARC-AGI-2 | — | 4.9% |
| SimpleBench | 27.2% | — |
| ARC-AGI-1 | — | 59.3% |
| CritPt | 0% | — |
| LiveBench Reasoning | 65.8% | — |
| LMArena Hard Prompts | 1365 | — |
| LiveBench Data Analysis | 60.9% | — |
| BIG-Bench Hard | 87.5% | — |
| ForecastBench | 59.1 | — |
| HellaSwag | 88.9% | — |
| LiveBench | 66.9% | — |
| PIQA | 84.7% | — |
| WinoGrande | 85.2% | — |
Math Not comparable
DeepSeek-V3: 32.1 (#219), o3-pro: —
| Benchmark | DeepSeek-V3 | o3-pro |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 37.8% | — |
| Omni-MATH | 40.3% | — |
| LiveBench Math | 73.5% | — |
| LMArena Math | 1373 | — |
| MATH Level 5 | 75.5% | — |
| FrontierMath (Feb 2025 set) | 1.7% | — |
Knowledge DeepSeek-V3 leads
DeepSeek-V3: 37.5 (#155), o3-pro: 29.5 (#238)
| Benchmark | DeepSeek-V3 | o3-pro |
|---|---|---|
| Confabulations | 26.1% | 14.2% |
| Vectara Hallucination Rate | 6.1% | 23.3% |
| GPQA Diamond | 67.6% | — |
| MMLU-Pro | 72.3% | — |
| GPQA (HELM) | 53.8% | — |
| LMArena Expert | 1351 | — |
| ARC (AI2) Challenge | 95.3% | — |
| MMLU | 87.2% | — |
| TriviaQA | 82.9% | — |
Multilingual Not comparable
DeepSeek-V3: 48.5 (#143), o3-pro: —
| Benchmark | DeepSeek-V3 | o3-pro |
|---|---|---|
| LMArena Non-English | 1358 | — |
| LMArena Chinese | 1391 | — |
| LMArena French | 1385 | — |
| LMArena German | 1374 | — |
| LMArena Japanese | 1333 | — |
| LMArena Korean | 1319 | — |
| LMArena Russian | 1373 | — |
| LMArena Spanish | 1358 | — |
Instruction Following Not comparable
DeepSeek-V3: 72.8 (#130), o3-pro: —
| Benchmark | DeepSeek-V3 | o3-pro |
|---|---|---|
| LiveBench Instruction Following | 81.5% | — |
| IFEval | 83.2% | — |
| LMArena Instruction Following | 1345 | — |
Long Context o3-pro leads
DeepSeek-V3: 34.0 (#253), o3-pro: 72.2 (#1)
| Benchmark | DeepSeek-V3 | o3-pro |
|---|---|---|
| Fiction.LiveBench | 50% | 97.2% |
| LMArena Longer Query | 1352 | — |
Writing & Preference Too close to call
DeepSeek-V3: 57.4 (#130), o3-pro: 57.1 (#133)
| Benchmark | DeepSeek-V3 | o3-pro |
|---|---|---|
| Short-Story Creative Writing | 77% | 84.4% |
| LMArena Text | 1375 | — |
| LMArena Creative Writing | 1364 | — |
| EQ-Bench Creative Writing | 1472 | — |
| WildBench | 83% | — |
| LMArena Multi-Turn | 1389 | — |
| LiveBench Language | 49.1% | — |
Frequently asked questions
Is DeepSeek-V3 better than o3-pro?
o3-pro is the stronger model overall, scoring 42.9 to 39.5 on the Noometry Index. DeepSeek-V3 costs 86× less per token, which makes it the better buy when o3-pro's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3 or o3-pro?
DeepSeek-V3 is cheaper. It lists at $0.24 per million input tokens and $0.90 per million output tokens; o3-pro lists at $20 and $80.
Is DeepSeek-V3 or o3-pro better for coding?
o3-pro scores higher on coding benchmarks: 55.5 versus 42.3 in the Noometry coding category.
Which has the bigger context window?
o3-pro does, with 200K tokens against 164K.
How many benchmarks do DeepSeek-V3 and o3-pro share?
10 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and o3-pro has 12.