DeepSeek
Model Categories
Pricing Range — $/1M input tokens
Open Source Ratio
All DeepSeek Models26 total
| #▲ | Model | Avg | aider edit | aider poly? | ANLI? | APEX-Agents? | ARC AI2? | ARC-AGI? | ARC-AGI-2? | aa agentic? | aa coding ? | aa quality | seal audio | seal audio | seal audio | Balrog? | BBH? | hf bbh | C-Eval | CadEval? | charxiv re? | charxiv re? | arena elo | arena elo | chess puzz? | CMMLU | CSQA2 | Cybench? | deepresear? | EnigmaEval | fiction li? | Fortress | frontierma? | frontierma? | GeoBench? | GPQA | GPQA diamond? | graphwalks? | GSM8K? | GSO-Bench? | HellaSwag? | HELM · GPQA | helm ifeva | helm mmlu | helm omni | helm wildb | HLE? | hle tools | seal human | seal human | IFEval | jp jcommon | JHumanEval | JMMLU | JNLI | JSQuAD | LAMBADA? | lech mazur? | livebench | livebench | livebench | livebench | livebench | livebench | livebench | livebench | jp overall | MASK | MATH level 5? | MATH Level 5 | MCP Atlas | MMLU? | MMLU-PRO | MMMLU | mmmlu ar | mmmlu bn | mmmlu zh | mmmlu fr | mmmlu de | mmmlu hi | mmmlu id | mmmlu it | mmmlu ja | mmmlu ko | mmmlu pt | mmmlu es | mmmlu sw | mmmlu yo | seal multi | MultiNRC | MUSR | OpenBookQA? | oc aime202 | oc gpqa di | oc hle | oc ifeval | oc livecod | oc mmlu pr | OSWorld? | otis mock ? | PIQA? | posttrainb | seal pro r | seal pro r | seal prope | seal remot | ScienceQA? | SciPredict | SimpleBench? | simpleqa v? | seal swe a | seal swe a | swe bench | swe bench | swe bench | seal swe b | seal swe b | swe bench ? | swe bench ? | terminal b? | the agent ? | TriviaQA? | TutorBench | USAMO | VideoMME? | VISTA | seal visua | VPCT? | WeirdML? | Winogrande? | $/1M in | Context | Released |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 95.2 | - | - | - | - | - | - | - | 0.0 | 37.9 | 29.4 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 96.0 | 86.7 | 28.6 | 91.7 | 80.9 | 85.5 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | $0.40 | 164K | Dec 255mo ago | |
| 2 | 88.3 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 40.7 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 18.3 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 43.8 | 93.7 | - | 63.4 | 82.4 | 89.8 | - | - | - | - | - | - | - | - | - | - | 56.8 | - | - | 57.0 | - | - | 40.7 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 28.7 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | N/A | - | Jan 251y ago | |
| 3 | 84.4 | - | - | - | - | 89.6 | - | - | - | - | - | - | - | - | - | 71.7 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 82.8 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 71.2 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 67.8 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 80.0 | - | - | - | - | - | - | - | 72.6 | N/A | - | Jan 242y ago | |
| 4 | 58.7 | - | 74.2 | - | - | - | 57.0 | 4.0 | 52.9 | 36.7 | 41.7 | - | - | - | - | - | - | - | - | - | - | 1326.9 | 1424.4 | 14.0 | - | - | - | - | - | - | - | 22.1 | 2.1 | - | - | 77.9 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 46.7 | 75.7 | 45.0 | 23.1 | 64.2 | 64.0 | 51.8 | 44.3 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 93.0 | 84.6 | 23.2 | 89.7 | 75.4 | 85.8 | - | 87.8 | - | - | - | - | - | - | - | - | - | 27.5 | - | - | - | - | - | - | - | - | - | 39.6 | - | - | - | - | - | - | - | - | - | - | $0.25 | 131K | Dec 255mo ago | |
| 5 | 58.7 | - | 74.2 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 1285.5 | 1422.8 | - | - | - | - | - | - | 83.3 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 36.7 | 73.2 | 44.3 | 19.3 | 65.6 | 64.4 | 49.9 | 45.5 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 42.9 | - | - | - | - | - | - | - | 39.5 | - | $0.27 | 164K | Sep 257mo ago | |
| 6 | 58.3 | - | 48.4 | - | - | 93.7 | - | - | - | - | - | - | - | - | - | 83.3 | - | - | - | - | - | - | 1358.2 | - | - | - | - | - | - | 50.0 | - | 1.7 | - | - | - | 42.0 | - | - | - | 85.2 | 53.8 | 83.2 | 72.3 | 40.3 | 83.1 | - | - | - | - | - | - | - | - | - | - | - | 77.0 | - | - | - | - | - | - | - | - | - | - | 64.8 | - | - | 82.9 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 15.8 | 69.4 | - | - | - | - | - | - | - | 2.7 | - | - | - | - | - | - | - | - | - | - | - | - | 82.9 | - | - | - | - | - | - | 36.1 | 70.4 | $0.32 | 164K | Dec 241y ago | |
| 7 | 56.4 | - | 71.4 | - | - | - | 21.2 | 1.1 | 20.8 | 24.0 | 27.1 | - | - | - | - | - | - | - | - | - | - | - | 1421.7 | - | - | - | - | 35.1 | - | - | - | - | - | - | - | 68.4 | - | - | - | - | 66.6 | 78.4 | 79.3 | 42.4 | 82.8 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 96.6 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 89.0 | 80.6 | 14.4 | 80.0 | 61.0 | 83.5 | - | 66.4 | - | - | - | - | - | - | - | - | 29.0 | 27.4 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 41.6 | - | $0.50 | 164K | May 2511mo ago | |
| 8 | 53.4 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 1417.9 | - | - | - | - | - | - | 52.8 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 85.2 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 28.0 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 38.4 | - | $0.15 | 33K | Aug 258mo ago | |
| 9 | 48.0 | - | 56.9 | - | - | - | 15.8 | 1.3 | - | - | - | - | - | - | 34.9 | - | - | - | - | - | - | - | 1397.5 | - | - | - | - | 35.1 | - | 69.4 | - | - | - | - | - | 62.3 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 83.0 | - | - | - | - | - | - | - | - | - | - | 93.0 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 53.3 | - | - | - | - | - | - | - | - | 17.1 | 27.4 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 36.5 | - | $0.70 | 64K | Jan 251y ago | |
| 10 | 47.6 | - | - | - | - | - | - | - | - | 11.4 | 15.9 | - | - | - | - | - | 35.8 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 2.0 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 43.4 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 30.7 | - | - | 41.6 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 13.3 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | $0.70 | 131K | Jan 251y ago | |
| 11 | 42.2 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 17.1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 4.6 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 41.9 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 17.1 | - | - | 41.0 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 16.1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | $0.29 | 33K | Jan 251y ago | |
| 12 | 38.4 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 5.3 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 0.7 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 37.8 | 62.4 | - | 37.8 | 69.4 | 80.2 | - | - | - | - | - | - | - | - | - | - | 41.4 | - | - | 22.0 | - | - | 12.1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 0.5 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | N/A | - | Jan 251y ago | |
| 13 | 38.3 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 7.9 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 3.9 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 40.4 | 59.8 | - | 42.3 | 54.6 | 74.2 | - | - | - | - | - | - | - | - | - | - | 39.3 | - | - | 19.6 | - | - | 14.7 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 3.5 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | N/A | - | Jan 251y ago | |
| 14 | 26.5 | - | - | - | - | 22.9 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 35.4 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 19.2 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 24.0 | N/A | - | Jan 242y ago | |
| 15 | 16.1 | - | - | - | - | 15.2 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 21.3 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 15.2 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 15.2 | N/A | - | Jan 242y ago | |
| 16 | 13.9 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 4.7 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 0.8 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 34.6 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 16.9 | - | - | 2.1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 3.0 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | N/A | - | Jan 251y ago |
About DeepSeek
Quick answers · sourced from our data
How many models does DeepSeek have?
BenchGecko tracks 26 models from DeepSeek, of which 22 (85%) are open source. Every entry is updated daily from live provider feeds.
What is the best model from DeepSeek?
DeepSeek V3.2 Speciale is currently the highest scoring DeepSeek model we track, with an average benchmark score of 78.2. Scores are computed across every public benchmark we have data for.
What is the cheapest DeepSeek model?
The cheapest DeepSeek model on BenchGecko starts at $0.14 per 1M input tokens. Pricing is pulled from OpenRouter and cross-checked against official provider rate cards.
How does DeepSeek compare on benchmarks?
DeepSeek models average 39.9 across the benchmarks we track · see the All Providers page for the full ranking by model count, open source ratio, and average score.
Where is DeepSeek based?
DeepSeek is headquartered in China. BenchGecko groups providers by region to make it easy to compare US, EU, China, and Rest of World markets.
Is DeepSeek open source?
22 of 26 DeepSeek models are open source (85%). The rest are proprietary · closed weights served via API.