StartupVideo world modelFounded 2023
Real-time generative AI company whose Oasis 3 interactive world model generates action-conditioned simulation environments for training robots, starting with autonomous vehicles.
Decart is a real-time generative AI company founded in 2023 that builds the Lucy live video models, the Oasis interactive world models and the DOS optimization stack. Its Oasis 3 model, released in June 2026, generates action-responsive simulation environments for robot learning, starting with autonomous vehicles. In May 2026 it raised $300M led by Radical Ventures, bringing total funding above $450M.
Sources: decart.ai (19 Oct 2026) · decart.ai (10 Jun 2026) · decart.ai (18 May 2026)
Private company · $453M disclosed in USD across 4 of 4 listed rounds
| Date | Round | Amount | Lead | Source |
|---|---|---|---|---|
| Q4 2024 | Seed | $21M | Sequoia | decart.ai, 19 Oct 2026 |
| Q1 2025 | Series AValuation: over $500M (per TechCrunch headline cited on company page) | $32M | Benchmark | decart.ai, 19 Oct 2026 |
| Q3 2025 | Funding roundValuation: $3.1B (per Fortune headline cited on company page) | $100M | n/a | decart.ai, 19 Oct 2026 |
| 18 May 2026 | Funding round | $300M | Radical Ventures | decart.ai, 18 May 2026 |
#78 of 104 model companies · score 9.9 / 100 · updated 19 Oct 2026
Each bar shows an input from 0 to 100% before weighting. 0 of 1 listed models have open weights.
The score (0–100) measures public, checkable evidence about a company's robot models. It does not measure private capability. A company that publishes no benchmark results and no weights can still have strong models. Every input is computed from the data on each company page, and every number there links to its source.
| Input | Weight | How it is measured |
|---|---|---|
| Published benchmarks | 35% | For each scored benchmark, the company's best result divided by the best result any company has published, averaged over the benchmarks it reports. No published results scores 0. Results come only from the model's own paper, model card or the official benchmark leaderboard. Scored: LIBERO (4-suite average success, %); SimplerEnv Google Robot, visual matching (%); SimplerEnv Google Robot, variant aggregation (%); SimplerEnv WidowX / Bridge (%); CALVIN ABC→D (average length, 0–5); RoboArena (pairwise rating). |
| Open weights | 20% | Share of the company's listed models whose weights the public can download. |
| Adoption | 20% | GitHub stars on the company's most-starred official model repository, on a log scale relative to the most-starred repository in the list. Hugging Face downloads are shown on company pages but not scored, because gated repositories report zero. |
| Released models | 15% | Number of publicly announced, named models, capped at 5. |
| Disclosed funding | 10% | The sum of the funding rounds listed on the company's profile that were disclosed in USD, on a log scale from $10M to the largest sum in the list. Rounds reported only in other currencies are listed but not converted or counted. This input applies only to private startups and robot makers. For big-tech divisions, research labs and listed companies it is n/a, and its weight is shared across the other inputs in proportion. |
Sources. arXiv papers and official model cards for benchmarks and parameters; official leaderboards (RoboArena); the GitHub and Hugging Face APIs for stars and downloads; company announcements and established press for funding. Missing values are shown as n/a, never estimated.
Updated 19 Oct 2026. Benchmark protocols differ between papers, for example in fine-tuning and number of trials, so small gaps between close scores are not meaningful.
Closed weightsWorld model
API-accessible interactive world model that generates multi-view, action-responsive simulation environments for robot learning, launched for autonomous vehicles with humanoids and other robots planned.
No benchmark results on a shared public benchmark have been published for this model.
