Inference Logoinference shell

Runway Gen-4 Image

Runway Gen-4 Image — high-quality text-to-image generation with optional reference images and @tag syntax. 5 credits per 720p, 8 credits per 1080p.

text to imageimage to image
run in browser →run via APIrun with your agent
maker
runway
category
image
input → output
text → image, image → image
runs via
api, javascript and python sdk, belt cli, mcp
updated
2026-08-04
price
$0.05/image (720p), $0.08/image (1080p)

api reference

about

runway gen-4 image — high-quality text-to-image generation with optional reference images and @tag syntax. 5 credits per 720p, 8 credits per 1080p.

get an api key

create one in settings → api keys. the examples read it from INFERENCE_API_KEY.

bash
1export INFERENCE_API_KEY="inf_your_key"

1. calling the api

install the client

nothing to install. any http client works; the examples use curl, and jq to read responses. every response wraps its result in a data field.

run and get result

send "wait": true to hold the connection until the task finishes. without it the call returns right away with the task still running.

bash
12# "wait": true holds the connection until the task finishes.3# the response is {"data": {"id", "status", "status_text", "output", ...}}4curl -X POST https://api.inference.sh/apps/run \5  -H "Authorization: Bearer $INFERENCE_API_KEY" \6  -H "Content-Type: application/json" \7  -d '{8    "app": "runway/gen-4-image",9    "input": {10      "prompt": "prompt"11    },12    "wait": true13  }'

stream live updates

get real-time progress updates as the task runs. ideal for showing progress bars, partial results, or long-running tasks. send "stream": true and the response is a server-sent event stream that ends when the task does.

bash
12# "stream": true answers with server-sent events until the task finishes3curl -N -X POST https://api.inference.sh/apps/run \4  -H "Authorization: Bearer $INFERENCE_API_KEY" \5  -H "Content-Type: application/json" \6  -d '{7    "app": "runway/gen-4-image",8    "input": {9      "prompt": "prompt"10    },11    "stream": true12  }'

run in the background

start the task and get its id back right away, then check on it later. for long tasks, or when you'd rather be called back, see webhooks.

bash
1# without "wait" the call returns the task right away (status 1, "received")2TASK_ID=$(curl -s -X POST https://api.inference.sh/apps/run \3  -H "Authorization: Bearer $INFERENCE_API_KEY" \4  -H "Content-Type: application/json" \5  -d '{6    "app": "runway/gen-4-image",7    "input": {8      "prompt": "prompt"9    }10  }' | jq -r '.data.id')1112# later: read its state. done when status is 10 (completed), 11 (failed) or 12 (cancelled)13curl -s https://api.inference.sh/tasks/$TASK_ID \14  -H "Authorization: Bearer $INFERENCE_API_KEY" | jq '.data | {status, status_text, output}'

2. authentication

the api uses api keys for authentication. see the authentication docs for detailed setup instructions.

3. files

file inputs are automatically handled by the sdk. you can pass local paths, urls, or base64 data.

automatic upload

pass urls directly or use data uris for inline content.

bash
1# pass URLs or data URIs as input values2curl -X POST https://api.inference.sh/apps/run \3  -H "Authorization: Bearer $INFERENCE_API_KEY" \4  -H "Content-Type: application/json" \5  -d '{6    "app": "runway/gen-4-image",7    "input": {8      "image": "https://example.com/image.png",9      "audio": "data:audio/mp3;base64,..."10    },11    "wait": true12  }'

manual upload

you can also upload files manually and use the returned url.

bash
1# 1. create the file: the response has an upload_url and the file's uri2FILE=$(curl -s -X POST https://api.inference.sh/files \3  -H "Authorization: Bearer $INFERENCE_API_KEY" \4  -H "Content-Type: application/json" \5  -d '{"files": [{"filename": "image.png", "content_type": "image/png"}]}')67# 2. PUT the bytes to upload_url8curl -X PUT "$(echo "$FILE" | jq -r '.data[0].upload_url')" \9  -H "Content-Type: image/png" \10  --data-binary @image.png1112# 3. pass the uri as an input value13echo "$FILE" | jq -r '.data[0].uri'

4. webhooks

get notified when a task completes by providing a webhook url. when the task reaches a terminal state (completed, failed, or cancelled), a POST request is sent to your url with the task result.

bash
1curl -X POST https://api.inference.sh/apps/run \2  -H "Authorization: Bearer $INFERENCE_API_KEY" \3  -H "Content-Type: application/json" \4  -d '{5    "app": "runway/gen-4-image",6    "input": {7        "prompt": "prompt"8    },9    "webhook": "https://your-server.com/webhook"10  }'

webhook payload

your endpoint receives a JSON POST with the task result:

json
1{2  "event": "task.completed",3  "timestamp": "2024-01-15T10:30:05Z",4  "data": {5    "id": "task_abc123",6    "short_id": "abc123",7    "status": 10,8    "status_text": "completed",9    "output": { ... },10    "created_at": "2024-01-15T10:30:00Z",11    "updated_at": "2024-01-15T10:30:05Z"12  }13}
eventstringtask.completed, task.failed or task.cancelled
timestampstringiso timestamp of the delivery
data.idstringtask id
data.short_idstringshort task id
data.statusnumberterminal status (10=completed, 11=failed, 12=cancelled, 13=rejected)
data.status_textstringthe status as a word: completed, failed or cancelled
data.outputobjecttask output (when completed)
data.errorstringerror message (when failed, omitted otherwise)
data.session_idstringsession id (when the task ran in a session, omitted otherwise)
data.created_atstringiso timestamp
data.updated_atstringiso timestamp

5. schema

input

promptstring*

text prompt. use @tagname to reference tagged images.

ratiostring

output image aspect ratio. 720p = 5 credits, 1080p = 8 credits.

default: "1280:720"
options:"1280:720""720:1280""1104:832""832:1104""960:960""1584:672""1920:1080""1080:1920"
reference_imagesarray

reference images with optional tags for style/subject guidance.

seedinteger

seed for reproducible results.

output

imagestring(file)*

generated image.

other image apps

Gemini Nano Banana 2.1

google/gemini-nano-banana-2-1

$1.50/M input, $0.0336–$0.1134/image (1K–4K), +$0.014/query Google Search

Grok Imagine Image 2.0

xai/grok-imagine-image-2-0

$0.04/img low 1k, $0.06/img low 2k, $0.06/img med 1k, $0.08/img med 2k; $0.01/source img (edits)

GPT Image 2.5 Sunburst

openai/gpt-image-2-5-sunburst

$0.006-$0.21 per 1024x1024 image: low $0.006, medium $0.013, high $0.053, xhigh $0.094, max $0.21. Larger sizes cost more; edits add ~$0.008 per reference image.

GPT Image 2.5 Flare

openai/gpt-image-2-5-flare

$0.006-$0.21/image at 1024x1024: low $0.0059, medium $0.0132, high $0.0527, xhigh $0.0937, max $0.2107. Larger sizes cost more; edits add ~$0.0083 per reference image.

Image Resize & Crop

eval/resize

from $0.00000277/sec

more from runway

Runway Act-Two

runway/act-two

$0.05/sec

Runway Gen-4 Image Turbo

runway/gen-4-image-turbo

$0.02/image

Runway Aleph 2.0

runway/aleph-2

$0.28/sec

Runway Gen-4 Turbo

runway/gen-4-turbo

$0.05/sec

new models

  1. Tripo Auto Rig

    tripo/tripo-rig

    3d$0.01/credit (rig check free, auto rig 25 credits, retarget 10 credits/animation)

  2. Hunyuan 3D Auto Rigging

    tencent/hunyuan-3d-auto-rigging

    3d$0.02/credit, 10 credits/call ($0.20/call)

  3. TokenRig

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    3dGPU time, billed per millisecond

  4. Hunyuan 3D 3.1

    tencent/hunyuan-3d-3-1

    3d$0.02/credit; Geometry 15cr, Normal 25cr, +10cr views/PBR/face-count

  5. Rodin Gen-2.5

    hyper3d/rodin-gen-2-5

    3d$0.58/credit; Extreme-High tier +0.5 credits, texture extreme-high +2 credits

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