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02
Examples
Real outputs from the public examples of this model on Replicate, with the prompt and settings that produced them. They were not generated live on this page.
Prompt
Close-up of a chameleon's eye, with its scaly skin changing color. Ultra high resolution 4k.
Lengde: 0:05
Settings
num_frames
121
fps
24
Prompt
A pristine snowglobe featuring a winter scene sits peacefully. The globe violently explodes, sending glass, water, and glittering fake snow in all directions. The scene is captured with high-speed photography.
Lengde: 0:05
Settings
num_frames
121
fps
24
Prompt
The video opens with a close-up of a woman in a white and purple outfit, holding a glowing purple butterfly. She has dark hair and walks gracefully through a traditional Japanese-style village at night
Lengde: 0:05
Settings
num_frames
121
fps
24
03
Om Mochi 1
Kort sagtPer 23. september 2026
Mochi 1 er en modell fra Community i kategorien Videogenerering. På Railwail koster Mochi 1 ≈ 0,5041 USD per kjøring. Støttede bildeformat: 16:9, 9:16 og 1:1.
Bakgrunn
Om Genmo
Grunnlagt 2023 · San Francisco, USA
Genmo was founded in 2023 by Paras Jain (CEO) and Ajay Jain (CTO), both PhDs from UC Berkeley's BAIR lab, with a focus on open-source generative video. The company released the early Replay product and the smaller GEN-1 video model before launching Mochi 1 in October 2024 as a 10B-parameter open-weight text-to-video model under the Apache 2.0 licence -- at the time the largest open-source video model with a fully permissive licence. Genmo positions Mochi 1 as a research foundation for the community to fine-tune, extend and study, in deliberate contrast to closed competitors. The company has raised over $30M from investors including NEA and the Y Combinator network.
Asymmetric Diffusion Transformer (AsymmDiT) at 10B parameters with custom 3D VAE
Mochi 1 is a 10B-parameter Asymmetric Diffusion Transformer (AsymmDiT) operating on a high-compression 3D causal Variational Autoencoder. The 'asymmetric' design uses dramatically more parameters for the video stream than for the text stream while sharing self-attention, on the hypothesis that visual modeling is the bottleneck for video generation. Position information uses 3D RoPE; the model is trained with Rectified Flow Matching at full resolution and high motion intensity. Text conditioning uses a T5-XXL encoder. Mochi 1 generates 5-second clips at 480p / 30 fps natively (with a 720p HD variant in preview at launch). Training is done on a curated, filtered video corpus with dense captions produced by an in-house captioner. The team explicitly report ablations on resolution scheduling, motion intensity filtering and caption quality.
Parametere
10 billion
Evner
10B open-weight text-to-video model under Apache 2.0 (most permissive in class)
Asymmetric DiT design biased toward visual capacity
curl https://railwail.com/api/v1/videos/generations \
-H "Authorization: Bearer $RAILWAIL_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "mochi-1-genmo",
"prompt": "A slow drone shot over a misty pine forest at sunrise"
}'
# Response: {"job_id": "...", "status": "queued", ...}
# Poll until status is completed, failed or cancelled:
curl https://railwail.com/api/v1/jobs/JOB_ID \
-H "Authorization: Bearer $RAILWAIL_API_KEY"
import os
import time
import requests
API = "https://railwail.com/api/v1"
headers = {"Authorization": f"Bearer {os.environ['RAILWAIL_API_KEY']}"}
job = requests.post(
f"{API}/videos/generations",
headers=headers,
json={
"model": "mochi-1-genmo",
"prompt": "A slow drone shot over a misty pine forest at sunrise",
},
).json()
while True:
status = requests.get(f"{API}/jobs/{job['job_id']}", headers=headers).json()
if status["status"] in ("completed", "failed", "cancelled"):
break
time.sleep(5)
print(status["status"], status.get("output_url"))
const API = "https://railwail.com/api/v1";
const headers = {
Authorization: `Bearer ${process.env.RAILWAIL_API_KEY}`,
"Content-Type": "application/json",
};
const job = await fetch(`${API}/videos/generations`, {
method: "POST",
headers,
body: JSON.stringify({
model: "mochi-1-genmo",
prompt: "A slow drone shot over a misty pine forest at sunrise"
}),
}).then((r) => r.json());
let status;
do {
await new Promise((r) => setTimeout(r, 5000));
status = await fetch(`${API}/jobs/${job.job_id}`, { headers }).then((r) => r.json());
} while (!["completed", "failed", "cancelled"].includes(status.status));
console.log(status.status, status.output_url);
Mochi 1 er en modell fra Community i kategorien Videogenerering. På Railwail kan du kalle den med en API-nøkkel gjennom Railwail API.
Hvor mye koster Mochi 1 på Railwail?
På Railwail koster Mochi 1 ≈ 0,5041 USD per kjøring. Du betaler for det hver forespørsel faktisk bruker. Bruk betales fra forhåndskjøpte kreditter; 1 kreditt tilsvarer 0,01 USD.
Hvilke innstillinger støtter Mochi 1?
I henhold til inndataskjemaet kjenner Mochi 1 disse parametrene: prompt (opptil 2 000 tegn), fps (8 til 30), seed, aspect_ratio (16:9, 9:16 eller 1:1), duration_sec (1 til 6) og motion_strength (0 til 1).
Hvor rask er Mochi 1?
Det finnes ennå ikke nok målte kjøringer av Mochi 1 på Railwail til å angi en kjøretid. Det avhenger av inndataene, innstillingene og belastningen hos leverandøren.
Er Mochi 1 bedre enn Google Veo 3.1?
Det avhenger av oppgaven. Mochi 1 (Community) og Google Veo 3.1 (Google DeepMind) er begge modeller i kategorien Videogenerering. Sammenligningssiden viser prisene og spesifikasjonene deres side ved side.
Opprett en Railwail API-nøkkel og send forespørselen din med modell-ID-en mochi-1-genmo. Kodeeksempler for curl, Python og JavaScript finnes i API-delen av denne siden.
Tencent's HunyuanVideo, a 13B open-weights text-to-video diffusion transformer. Produces high-motion, photorealistic clips with smooth temporal consistency and was one of the first open models to rival closed systems on motion quality.
