Microsoft MAI Image 2.5 Pro Image Edit transforms source images with natural-language instructions, applying precise edits while preserving the details that should remain unchanged for creative design, product visuals, marketing assets, and production workflows. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Bezczynny

$0.29za uruchomienie·~34 / $10

Change that a tiny medieval kingdom exists between the books. Add miniature castles, tiny knights riding mice, tiny bridges connecting book spines, and a curious bookstore owner kneeling down to watch them. Keep the bookstore cozy while making it fantastical.

Change the flower arrangement class goes hilariously wrong: flowers growing wildly out of control, vines wrapping around desks, one robot panicking, another proudly holding an absurd giant bouquet, petals flying everywhere, comedic chaos while preserving the classroom scene.
Microsoft MAI Image 2.5 Pro Edit transforms a source image with natural-language instructions. It is built for precise visual changes, layout-aware refinement, and preservation-focused editing where identity, composition, or important source details should remain unchanged.
Instruction-based image editing
Edit an existing image by describing the desired change in natural language.
Preservation-focused results
Modify selected visual details while keeping important source elements unchanged.
Layout-aware refinement
Apply edits to background, objects, lighting, style, or composition while maintaining a coherent final image.
Flexible aspect ratios
Use auto for model-selected framing or choose a specific aspect ratio when needed.
Standard output formats
Export edited images as jpeg, png, or webp.
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | Text instruction describing how to edit the source image. |
| image | Yes | Source image to edit. Public image URLs and uploaded images are supported. |
| aspect_ratio | No | Output aspect ratio. Use auto to let the model choose a suitable ratio. |
| output_format | No | Output format: jpeg, png, or webp. |
auto for model-selected framing.jpeg, png, or webp when needed.Pricing includes a base image editing cost plus a prompt length surcharge.
| Item | Price |
|---|---|
| Base image edit | $0.29 |
| Prompt Length | Price |
|---|---|
| 10 characters | $0.290075 |
| 100 characters | $0.29075 |
| 500 characters | $0.29375 |
| 1,000 characters | $0.2975 |
| 2,000 characters | $0.305 |
auto when the source composition should guide the output ratio.png for high-quality general output, jpeg for smaller files, and webp for web-friendly images.Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/microsoft/mai-image-2.5-pro/edit with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Mai Image 2.5 Pro Edit below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"aspect_ratio": "1:1",
"output_format": "png"
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/microsoft/mai-image-2.5-pro/edit" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $WAVESPEED_API_KEY" \
-d "$REQUEST_BODY")
TASK=$(printf '%s' "$SUBMIT_RESPONSE" | jq 'if has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "$TASK" | jq -r '.id')
if [ -z "$PREDICTION_ID" ] || [ "$PREDICTION_ID" = "null" ]; then
printf 'Submission response did not contain a prediction id
' >&2
exit 1
fi
RESULT_URL=$(printf '%s' "$TASK" | jq -r '.urls.get // empty')
if [ -z "$RESULT_URL" ]; then
RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
fi
# 2. Poll until the prediction finishes.
while true; do
RESPONSE=$(curl --silent --show-error --fail-with-body "$RESULT_URL" \
-H "Authorization: Bearer $WAVESPEED_API_KEY")
RESULT=$(printf '%s' "$RESPONSE" | jq 'if has("data") then .data else . end')
STATUS=$(printf '%s' "$RESULT" | jq -r '.status')
case "$STATUS" in
completed) printf '%s\n' "$RESULT" | jq '.outputs'; break ;;
failed|cancelled|timeout) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
created|processing) sleep 2 ;;
*) printf 'Unexpected status: %s
' "$STATUS" >&2; exit 1 ;;
esac
doneconst submitUrl = "https://api.wavespeed.ai/api/v3/microsoft/mai-image-2.5-pro/edit";
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');
async function requestJson(url, options = {}) {
const response = await fetch(url, options);
if (!response.ok) throw new Error(await response.text());
return response.json();
}
// 1. Submit the prediction.
const body = await requestJson(submitUrl, {
method: "POST",
headers: {
"Authorization": `Bearer ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"aspect_ratio": "1:1",
"output_format": "png"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${task.id}/result`;
// 2. Poll until the prediction finishes.
while (true) {
const resultBody = await requestJson(resultUrl, {
headers: { "Authorization": `Bearer ${apiKey}` },
});
const result = resultBody.data ?? resultBody;
if (result.status === "completed") {
console.log(result.outputs);
break;
}
if (["failed", "cancelled", "timeout"].includes(result.status)) throw new Error(JSON.stringify(result));
if (!["created", "processing"].includes(result.status)) throw new Error("Unexpected status: " + result.status);
await new Promise(resolve => setTimeout(resolve, 2000));
}import json
import os
import time
from urllib.request import Request, urlopen
api_key = os.environ["WAVESPEED_API_KEY"]
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
payload = {
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"aspect_ratio": "1:1",
"output_format": "png"
}
def request_json(url, data=None):
request = Request(url, data=data, headers=headers, method="POST" if data else "GET")
with urlopen(request) as response:
return json.load(response)
# 1. Submit the prediction.
body = request_json("https://api.wavespeed.ai/api/v3/microsoft/mai-image-2.5-pro/edit", json.dumps(payload).encode())
task = body.get("data", body)
if not task.get("id"):
raise RuntimeError("Submission response did not contain a prediction id")
result_url = task.get("urls", {}).get("get") or f"https://api.wavespeed.ai/api/v3/predictions/{task['id']}/result"
# 2. Poll until the prediction finishes.
while True:
result_body = request_json(result_url)
result = result_body.get("data", result_body)
status = result.get("status")
if status == "completed":
print(result.get("outputs", []))
break
if status in {"failed", "cancelled", "timeout"}:
raise RuntimeError(result)
if status not in {"created", "processing"}:
raise RuntimeError(f"Unexpected status: {status}")
time.sleep(2)Mai Image 2.5 Pro Edit is a Microsoft model for image editing, exposed as a REST API on WaveSpeedAI. Microsoft MAI Image 2.5 Pro Image Edit transforms source images with natural-language instructions, applying precise edits while preserving the details that should remain unchanged for creative design, product visuals, marketing assets, and production workflows. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. 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 result endpoint starting around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. The playground generates production-oriented Python, JavaScript, and cURL examples with timeouts, transient-error handling, and safe GET retries. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/microsoft/microsoft-mai-image-2.5-pro-edit.
Mai Image 2.5 Pro Edit starts at $0.29 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`, `image`, `aspect_ratio`, `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/microsoft/microsoft-mai-image-2.5-pro-edit.
Sign up for a free WaveSpeedAI account to claim starter credits, copy your API key from /accesskey, then call the endpoint shown in the API tab of the playground. The playground also auto-generates a code sample in Python, JavaScript, or cURL for the parameters you've set.
Commercial usage rights depend on the model's license, set by its provider (Microsoft). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.