whatmodel.sh / video / Cosmos-1.0-Autoregressive-4B

Cosmos-1.0-Autoregressive-4B is an open-weights video model from NVIDIA, released January 2025. Its published size is 4B parameters. Licence: nvidia-open-model.

Catalogue identity notes

Comparison-only, proposed so the Cosmos paper's 3D-consistency table counts. The paper's 'Cosmos-Predict1-4B' is the 4B autoregressive base model (Table 10); weights nvidia/Cosmos-1.0-Autoregressive-4B (sha 0b168630ad2400affefe460f2743c071de1fb257, created 2025-01-07).

34.1±4.5comparison only: no current run option verifiedfrom 2 benchmark results across 1 source* thinly tested, treat the rank as provisional

$ how it compares

The video models ranked either side of Cosmos-1.0-Autoregressive-4B, out of 139 with a score, then more from NVIDIA.

#modelscorewhereRAMreleased
117Kling 1.0 Kuaishou (Kling AI)35±7comparison only2024-06
118VideoCrafter2 Tencent AI Lab34±2local~9.4GB2024-01
119Cosmos-1.0-Autoregressive-4B NVIDIA34±5comparison only2025-01
120Pika 1.0 Pika32±3comparison only2023-11
121Open-Sora 1.1 HPC-AI Tech32±7local~14GB2024-04
more from NVIDIA
38Cosmos3-Super NVIDIA58±4local~160GB2026-05
60Cosmos3-Nano NVIDIA52±3local~42GB2026-05
72Cosmos-1.0-Diffusion-7B-Video2World NVIDIA49±5comparison only2025-01

see all video models · find the ones that fit your machine

$ what it's good at

text to video▇▅▄▆█▆▇▂▃▃▂▄ #75 of 94 * · 91% of its score · 2 tests
testresultvs the others in its rundifficultycountswhy
Cosmos 3D consistency: camera-pose estimation success rate on generated videos lab35.6% ✎█·█·······██ beats 1/3easy · avg 68%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 87%sharp · at its level
Cosmos 3D consistency: Sampson epipolar error on generated videos (lower is better) lab0.4330 ✎█···█···█··█ beats 1/3avg 0.385▇▇ 4.2%blunt · noisy

$ where to run it

No verified API or download recorded yet · add one

Official weights and documentation: huggingface.co/nvidia ↗

$ how the score was worked out · Cosmos World Foundation Model… 91%

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
Cosmos World Foundation Model Platform for Physical AI (arXiv:2501.03575v3) · lab-reported2▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 91%CC-BY-4.0
prior · pull toward the middle▇▇▇ 9%
weighted average= 34.1

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data updated · sources and licences · json