US$ 1,5121 (151,21 créditos) são reservados no início; o tempo real de GPU é cobrado.
Para contas sem compra anterior: execuções acima de 2 créditos precisam de recarga.
Novo por aqui?
10 créditos grátis (US$ 0,10) ao se inscrever com Google
Utilizável 24 horas após inscrição, até 5 execuções por dia e no máximo 2 créditos por execução. Outros métodos de login começam sem créditos.
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.
Duração: 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.
Duração: 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
Duração: 0:05
Settings
num_frames
121
fps
24
03
Sobre Mochi 1
ResumoA partir de 23 de setembro de 2026
Mochi 1 é um modelo de Community na categoria Geração de vídeos. No Railwail, Mochi 1 custa ≈ US$ 0,5041 por execução. Proporções de aspecto suportadas: 16:9, 9:16 e 1:1.
Fundo
Sobre Genmo
Fundado em 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.
Parâmetros
10 billion
Capacidades
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 é um modelo de Community na categoria Geração de vídeos. No Railwail você pode chamá-lo com uma chave de API através da API Railwail.
Quanto custa Mochi 1 no Railwail?
No Railwail, Mochi 1 custa ≈ US$ 0,5041 por execução. Você é cobrado pelo que cada solicitação realmente usa. O uso é pago com créditos pré-pagos; 1 crédito equivale a US$ 0,01.
Quais configurações Mochi 1 suporta?
De acordo com seu esquema de entrada, Mochi 1 conhece estes parâmetros: prompt (até 2.000 caracteres), fps (8 a 30), seed, aspect_ratio (16:9, 9:16 ou 1:1), duration_sec (1 a 6) e motion_strength (0 a 1).
Qual é a velocidade de Mochi 1?
Ainda não há execuções medidas suficientes de Mochi 1 no Railwail para indicar um tempo de execução. Depende da entrada, das configurações e da carga no provedor.
Mochi 1 é melhor que Google Veo 3.1?
Depende da tarefa. Mochi 1 (Community) e Google Veo 3.1 (Google DeepMind) são ambos modelos na categoria Geração de vídeos. A página de comparação mostra seus preços e especificações lado a lado.
Crie uma chave de API Railwail e envie sua solicitação com o ID do modelo mochi-1-genmo. Exemplos de código para curl, Python e JavaScript estão na seção API desta página.
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.
