LongCat-Image is a 6B parameter bilingual (Chinese-English) text-to-image model from Meituan, excelling at multilingual text rendering, photorealism, and deployment efficiency. Ready-to-use REST inference API with best performance and no cold starts.
待機中

$0.151回あたり·~66 / $10

A creative movie poster design. In the center is a futuristic robot cat. The title text "LongCat AI" is written in large, bold, metallic letters at the top. High contrast, 8k resolution.

A rainy cyberpunk street at night, a bright neon sign hanging on the wall that says "Open 24 Hours" and "便利店". Cinematic lighting, realistic reflections on the wet ground.

Professional food photography of a delicious beef burger with melted cheese, fresh lettuce, and tomatoes. Steam rising, water droplets on the vegetables, shallow depth of field, studio lighting

A hyper-realistic close-up portrait of an old fisherman with a white beard, wearing a yellow raincoat. detailed skin texture, weather-beaten face, dramatic lighting, sharp focus on eyes.

A panoramic view of a ruined modern metropolis reclaimed by nature. Skyscrapers are collapsing and covered in massive green vines and waterfalls. A rusted aircraft carrier sits in the middle of a flooded street. Sunset lighting, melancholic atmosphere, highly detailed textures, movie concept art.
LongCat-Image is an open-source, bilingual (Chinese-English) foundation model for image generation developed by Meituan. With only 6B parameters, it addresses key challenges in multilingual text rendering, photorealism, deployment efficiency, and developer accessibility.
With only 6B parameters, LongCat-Image outperforms larger open-source models across multiple benchmarks, demonstrating efficient model design.
Superior accuracy in rendering Chinese characters with industry-leading Chinese dictionary coverage. More stable than existing SOTA open-source models.
Innovative data strategy and training framework delivers high-quality, photorealistic image generation.
Natively supports both Chinese and English prompts with excellent text rendering in both languages.
The efficient 6B parameter architecture keeps GPU usage moderate, ideal for batch jobs and cost-sensitive pipelines.
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/longcat-image/text-to-image with your input as JSON. The endpoint returns a prediction id; poll the prediction endpoint until status flips to completed, then read the output URL from data.outputs[0]. Examples for Longcat Image Text To Image below.
# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/longcat-image/text-to-image" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $WAVESPEED_API_KEY" \
-d '{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"size": "1024*1024",
"output_format": "jpeg",
"seed": -1,
"enable_sync_mode": false,
"enable_base64_output": false
}'
# Response includes a prediction id. Poll for the result:
curl -X GET "https://api.wavespeed.ai/api/v3/predictions/{request_id}/result" \
-H "Authorization: Bearer $WAVESPEED_API_KEY"
# When status is "completed", read the output from data.outputs[0].// npm install wavespeed
const WaveSpeed = require('wavespeed');
const client = new WaveSpeed(); // reads WAVESPEED_API_KEY from env
const result = await client.run("wavespeed-ai/longcat-image/text-to-image", {
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"size": "1024*1024",
"output_format": "jpeg",
"seed": -1,
"enable_sync_mode": false,
"enable_base64_output": false
});
console.log(result.outputs[0]); // → URL of the generated output# pip install wavespeed
import wavespeed
output = wavespeed.run(
"wavespeed-ai/longcat-image/text-to-image",
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"size": "1024*1024",
"output_format": "jpeg",
"seed": -1,
"enable_sync_mode": false,
"enable_base64_output": false
}
)
print(output["outputs"][0]) # → URL of the generated outputLongcat Image Text To Image is a WaveSpeedAI model for image generation, exposed as a REST API on WaveSpeedAI. LongCat-Image is a 6B parameter bilingual (Chinese-English) text-to-image model from Meituan, excelling at multilingual text rendering, photorealism, and deployment efficiency. Ready-to-use REST inference API with best performance and no cold starts. You can call it programmatically or try it from the playground above.
POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID; poll the prediction endpoint until status flips to "completed", then read the output URL from the result. The playground generates a ready-to-paste code sample in Python, JavaScript, or cURL for whatever inputs you've set. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/wavespeed-ai/longcat-image-text-to-image.
Longcat Image Text To Image starts at $0.15 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.
Key inputs: `prompt`, `size`, `seed`, `enable_base64_output`, `enable_sync_mode`, `output_format`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/wavespeed-ai/longcat-image-text-to-image.
Average end-to-end generation time on WaveSpeedAI is around 217 seconds per request — measured across recent runs. Queue time scales with global demand; live status is visible in the prediction record.
Commercial usage rights depend on the model's license, set by its provider (WaveSpeedAI). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.