Model comparison
DeepSeek-V2.5 (Sep 2024) vs o3
o3 is the stronger model overall, scoring 47.5 to 37.6 on the Noometry Index.
Last verified . 18 shared benchmarks.
DeepSeekSummary
- They share 18 benchmarks with published results for both. DeepSeek-V2.5 (Sep 2024) scores higher in 0 categories and o3 in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where o3 leads 54.6 to 34.8.
- The biggest single-benchmark swing is Aider Polyglot: 17.8% for DeepSeek-V2.5 (Sep 2024) and 81.3% for o3.
- DeepSeek-V2.5 (Sep 2024) has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V2.5 (Sep 2024) | o3 | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 37.6 | 47.5 |
| Released | 2024-09-06 | 2025-04-16 |
| Weights | Open | Proprietary |
| Context window | — | 200K |
| Max output | — | 100K |
| Input $ / M tokens | — | $2 |
| Output $ / M tokens | — | $8 |
| Results tracked | 22 | 63 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding o3 leads
DeepSeek-V2.5 (Sep 2024): 31.7 (#281), o3: 46.8 (#64)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | o3 |
|---|---|---|
| Aider Polyglot | 17.8% | 81.3% |
| LMArena Coding | 1309 | 1408 |
| SWE-bench Verified | — | 62.3% |
| SWE-bench Verified (bash only) | — | 58.4% |
| GSO | — | 8.8% |
| WeirdML | — | 52.4% |
| BigCodeBench Instruct | 48.6% | — |
| BigCodeBench Complete | 53.2% | — |
| CadEval | — | 74% |
| ALE-Bench | — | 933.55 |
| HumanEval+ | 83.5% | — |
| MBPP+ | 74.1% | — |
Agentic & Tool Use Not comparable
DeepSeek-V2.5 (Sep 2024): —, o3: 34.5 (#44)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | o3 |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 63% |
| GDPval | — | 30.8% |
| DeepResearch Bench | — | 45.2% |
| OSWorld | — | 23% |
| LMArena Search | — | 1144 |
| METR Time Horizons | — | 65.4% |
Reasoning o3 leads
DeepSeek-V2.5 (Sep 2024): 25.6 (#145), o3: 32.0 (#78)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | o3 |
|---|---|---|
| LMArena Hard Prompts | 1289 | 1402 |
| ARC-AGI-2 | — | 6.5% |
| SimpleBench | — | 53.1% |
| Kagi LLM Benchmark | — | 67.6% |
| ARC-AGI-1 | — | 60.8% |
| CritPt | — | 1.4% |
| Chess Puzzles | — | 38% |
| EnigmaEval | — | 13.1% |
| Mystery Game Puzzles | — | 29% |
| DTBench | — | 84.8% |
| LMCA | — | 39.7% |
| Epoch Capabilities Index | — | 146.86 |
| ForecastBench | — | 62.5 |
Math o3 leads
DeepSeek-V2.5 (Sep 2024): 35.9 (#177), o3: 50.2 (#58)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | o3 |
|---|---|---|
| LMArena Math | 1288 | 1426 |
| FrontierMath (Tiers 1-3) | — | 33.3% |
| OTIS Mock AIME 2024-2025 | — | 84.4% |
| Omni-MATH | — | 71.4% |
| MATH Level 5 | — | 97.8% |
| FrontierMath (Feb 2025 set) | — | 18.7% |
| FrontierMath Tier 4 (v1) | — | 2.1% |
Knowledge o3 leads
DeepSeek-V2.5 (Sep 2024): 34.8 (#193), o3: 54.6 (#52)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | o3 |
|---|---|---|
| LMArena Expert | 1266 | 1402 |
| GPQA Diamond | — | 81.8% |
| Humanity's Last Exam | — | 20.3% |
| SimpleQA Verified | — | 49.4% |
| MMLU-Pro | — | 85.9% |
| Confabulations | — | 14.4% |
| GPQA (HELM) | — | 75.3% |
Multimodal Not comparable
DeepSeek-V2.5 (Sep 2024): —, o3: 41.4 (#36)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | o3 |
|---|---|---|
| LMArena Vision | — | 1214 |
| GeoBench | — | 74% |
| VPCT | — | 52% |
Multilingual o3 leads
DeepSeek-V2.5 (Sep 2024): 42.5 (#193), o3: 51.7 (#105)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | o3 |
|---|---|---|
| LMArena Non-English | 1273 | 1401 |
| LMArena Chinese | 1318 | 1437 |
| LMArena French | 1289 | 1430 |
| LMArena German | 1258 | 1420 |
| LMArena Japanese | 1228 | 1403 |
| LMArena Korean | 1209 | 1370 |
| LMArena Russian | 1289 | 1406 |
| LMArena Spanish | 1248 | 1395 |
Instruction Following o3 leads
DeepSeek-V2.5 (Sep 2024): 67.5 (#194), o3: 72.8 (#127)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | o3 |
|---|---|---|
| LMArena Instruction Following | 1280 | 1368 |
| IFEval | — | 86.9% |
Long Context o3 leads
DeepSeek-V2.5 (Sep 2024): 39.5 (#174), o3: 53.3 (#6)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | o3 |
|---|---|---|
| LMArena Longer Query | 1301 | 1372 |
| Fiction.LiveBench | — | 88.9% |
| CL-bench | — | 17.8% |
Writing & Preference o3 leads
DeepSeek-V2.5 (Sep 2024): 49.8 (#187), o3: 63.5 (#64)
| Benchmark | DeepSeek-V2.5 (Sep 2024) | o3 |
|---|---|---|
| LMArena Text | 1294 | 1410 |
| LMArena Creative Writing | 1285 | 1359 |
| LMArena Multi-Turn | 1297 | 1405 |
| Short-Story Creative Writing | — | 83.9% |
| EQ-Bench Creative Writing | — | 1676 |
| WildBench | — | 86.1% |
Frequently asked questions
Is DeepSeek-V2.5 (Sep 2024) better than o3?
o3 is the stronger model overall, scoring 47.5 to 37.6 on the Noometry Index.
Is DeepSeek-V2.5 (Sep 2024) or o3 better for coding?
o3 scores higher on coding benchmarks: 46.8 versus 31.7 in the Noometry coding category.
How many benchmarks do DeepSeek-V2.5 (Sep 2024) and o3 share?
18 benchmarks have published results for both models. DeepSeek-V2.5 (Sep 2024) has 22 scored results on Noometry and o3 has 63.