Qwen-Image-3.0 is Qwen’s third-generation image model, combining text-to-image creation and reference-based editing in one model. The standard edition balances image quality and response speed, while Qwen-Image-3.0 Pro is the flagship quality-oriented sibling. A request produces an image: a poster, illustrated explanation, product scene, storyboard or revised photograph rather than an editable document or a running interface.
Its rich-content capabilities suit a canvas with several related sections or a nested visual layout. Prepare the headings, exact wording, hierarchy and relationship between panels before adding stylistic detail. For a product comparison, a useful brief specifies the main product view, the attributes to label and the reading order of the supporting panels. For a storyboard, describe each panel’s subject, action and caption separately. The recommended prompt maximum is 4,500 tokens; spend that space on the content and layout the image actually needs.
The model supports detailed textures, typography down to small text demonstrated at 10 pixels, and native rendering across 12 languages with multiple fonts and artistic styles. Those capabilities can combine a realistic object with an information-rich graphic rather than forcing a choice between a photograph and a text-heavy layout. Supply the exact words and numerals to render, their intended language and where they belong. Treat a generated interface as a visual mockup: buttons, charts and labels in the image do not implement the behavior of an application.
Editing can change text, add or remove objects, move elements, adjust a subject’s action, transfer a style or enhance a damaged image’s details. Supply one to three images and a single clear editing instruction. Image numbering follows the supplied order, so identify which image contributes the subject, which provides a garment or object and which supplies the setting. For a product scene, ask to place the object from image 1 into the environment from image 2 while preserving the features that distinguish the product. Keep changes and preservation requirements explicit.
The exact image-input workflow accepts JPG, JPEG, PNG, BMP, TIFF, WebP and GIF; only the first frame of an animated GIF is processed. Each image can be up to 10 MB. The current API accepts dimensions from 1 to 8,000 pixels with no more than a 10:1 long-to-short-side ratio, and recommends 384–2,048 pixels for good results. Choose an input that clearly shows the details being transferred or edited. A tightly cropped reference can obscure the surrounding structure needed to place an object naturally.
The output is PNG, with one to six images per request and one as the default. Output pixel area ranges from 512 × 512 to 2,048 × 2,048, with aspect ratios between 1:8 and 8:1; these are area limits, not a rule that every side must be at most 2,048 pixels. If no size is supplied, the model recommends a resolution from the prompt. Select a landscape, square or portrait composition for the intended use, then inspect the returned image at that actual size so tiny labels and fine detail can be assessed where they will appear.
Prompt rewriting is enabled by default in the producer workflow. Direct enhancement works for both generation and editing; agent enhancement is a more elaborate rewriting mode for text-to-image only and is rejected for image-to-image editing. Thinking is enabled by default, requires prompt rewriting and increases generation time. Negative prompts can describe unwanted elements, while a fixed seed helps compare variations. State exclusions and exact preservation requirements clearly rather than expecting a seed to make a changing brief return an identical picture.
For a useful final artifact, inspect the rendered words, units, panel order and details that identify the subject, then revise the specific part that missed the brief. A multi-image response gives alternatives to compare; keep each returned image associated with the prompt and references that produced it. The producer’s image locations expire after 24 hours, so retain the actual image asset when the result becomes part of a project. The standard 3.0 edition remains distinct from Pro and from Qwen 2.1 models with their different reference counts and transparency features.
", "aboutText": "Qwen-Image-3.0 is Qwen’s third-generation image model, combining text-to-image creation and reference-based editing in one model. The standard edition balances image quality and response speed, while Qwen-Image-3.0 Pro is the flagship quality-oriented sibling. A request produces an image: a poster, illustrated explanation, product scene, storyboard or revised photograph rather than an editable document or a running interface.\n\nIts rich-content capabilities suit a canvas with several related sections or a nested visual layout. Prepare the headings, exact wording, hierarchy and relationship between panels before adding stylistic detail. For a product comparison, a useful brief specifies the main product view, the attributes to label and the reading order of the supporting panels. For a storyboard, describe each panel’s subject, action and caption separately. The recommended prompt maximum is 4,500 tokens; spend that space on the content and layout the image actually needs.\n\nThe model supports detailed textures, typography down to small text demonstrated at 10 pixels, and native rendering across 12 languages with multiple fonts and artistic styles. Those capabilities can combine a realistic object with an information-rich graphic rather than forcing a choice between a photograph and a text-heavy layout. Supply the exact words and numerals to render, their intended language and where they belong. Treat a generated interface as a visual mockup: buttons, charts and labels in the image do not implement the behavior of an application.\n\nEditing can change text, add or remove objects, move elements, adjust a subject’s action, transfer a style or enhance a damaged image’s details. Supply one to three images and a single clear editing instruction. Image numbering follows the supplied order, so identify which image contributes the subject, which provides a garment or object and which supplies the setting. For a product scene, ask to place the object from image 1 into the environment from image 2 while preserving the features that distinguish the product. Keep changes and preservation requirements explicit.\n\nThe exact image-input workflow accepts JPG, JPEG, PNG, BMP, TIFF, WebP and GIF; only the first frame of an animated GIF is processed. Each image can be up to 10 MB. The current API accepts dimensions from 1 to 8,000 pixels with no more than a 10:1 long-to-short-side ratio, and recommends 384–2,048 pixels for good results. Choose an input that clearly shows the details being transferred or edited. A tightly cropped reference can obscure the surrounding structure needed to place an object naturally.\n\nThe output is PNG, with one to six images per request and one as the default. Output pixel area ranges from 512 × 512 to 2,048 × 2,048, with aspect ratios between 1:8 and 8:1; these are area limits, not a rule that every side must be at most 2,048 pixels. If no size is supplied, the model recommends a resolution from the prompt. Select a landscape, square or portrait composition for the intended use, then inspect the returned image at that actual size so tiny labels and fine detail can be assessed where they will appear.\n\nPrompt rewriting is enabled by default in the producer workflow. Direct enhancement works for both generation and editing; agent enhancement is a more elaborate rewriting mode for text-to-image only and is rejected for image-to-image editing. Thinking is enabled by default, requires prompt rewriting and increases generation time. Negative prompts can describe unwanted elements, while a fixed seed helps compare variations. State exclusions and exact preservation requirements clearly rather than expecting a seed to make a changing brief return an identical picture.\n\nFor a useful final artifact, inspect the rendered words, units, panel order and details that identify the subject, then revise the specific part that missed the brief. A multi-image response gives alternatives to compare; keep each returned image associated with the prompt and references that produced it. The producer’s image locations expire after 24 hours, so retain the actual image asset when the result becomes part of a project. The standard 3.0 edition remains distinct from Pro and from Qwen 2.1 models with their different reference counts and transparency features.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "abd2c1eef60a204c60dc5819beffdc49862ed0885c65710163797a9e7405810f", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "en", "methods": [ { "html": "qwen/qwen-image-3Qwen-Image-3.0 combines image generation and editing for information-rich layouts, detailed scenes and multilingual typography. The standard edition balances quality and speed, accepting a text brief or up to three images with an editing instruction.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Qwen-Image-3.0 is Qwen’s third-generation image model, combining text-to-image creation and reference-based editing in one model. The standard edition balances image quality and response speed, while Qwen-Image-3.0 Pro is the flagship quality-oriented sibling. A request produces an image: a poster, illustrated explanation, product scene, storyboard or revised photograph rather than an editable document or a running interface.
Its rich-content capabilities suit a canvas with several related sections or a nested visual layout. Prepare the headings, exact wording, hierarchy and relationship between panels before adding stylistic detail. For a product comparison, a useful brief specifies the main product view, the attributes to label and the reading order of the supporting panels. For a storyboard, describe each panel’s subject, action and caption separately. The recommended prompt maximum is 4,500 tokens; spend that space on the content and layout the image actually needs.
The model supports detailed textures, typography down to small text demonstrated at 10 pixels, and native rendering across 12 languages with multiple fonts and artistic styles. Those capabilities can combine a realistic object with an information-rich graphic rather than forcing a choice between a photograph and a text-heavy layout. Supply the exact words and numerals to render, their intended language and where they belong. Treat a generated interface as a visual mockup: buttons, charts and labels in the image do not implement the behavior of an application.
Editing can change text, add or remove objects, move elements, adjust a subject’s action, transfer a style or enhance a damaged image’s details. Supply one to three images and a single clear editing instruction. Image numbering follows the supplied order, so identify which image contributes the subject, which provides a garment or object and which supplies the setting. For a product scene, ask to place the object from image 1 into the environment from image 2 while preserving the features that distinguish the product. Keep changes and preservation requirements explicit.
The exact image-input workflow accepts JPG, JPEG, PNG, BMP, TIFF, WebP and GIF; only the first frame of an animated GIF is processed. Each image can be up to 10 MB. The current API accepts dimensions from 1 to 8,000 pixels with no more than a 10:1 long-to-short-side ratio, and recommends 384–2,048 pixels for good results. Choose an input that clearly shows the details being transferred or edited. A tightly cropped reference can obscure the surrounding structure needed to place an object naturally.
The output is PNG, with one to six images per request and one as the default. Output pixel area ranges from 512 × 512 to 2,048 × 2,048, with aspect ratios between 1:8 and 8:1; these are area limits, not a rule that every side must be at most 2,048 pixels. If no size is supplied, the model recommends a resolution from the prompt. Select a landscape, square or portrait composition for the intended use, then inspect the returned image at that actual size so tiny labels and fine detail can be assessed where they will appear.
Prompt rewriting is enabled by default in the producer workflow. Direct enhancement works for both generation and editing; agent enhancement is a more elaborate rewriting mode for text-to-image only and is rejected for image-to-image editing. Thinking is enabled by default, requires prompt rewriting and increases generation time. Negative prompts can describe unwanted elements, while a fixed seed helps compare variations. State exclusions and exact preservation requirements clearly rather than expecting a seed to make a changing brief return an identical picture.
For a useful final artifact, inspect the rendered words, units, panel order and details that identify the subject, then revise the specific part that missed the brief. A multi-image response gives alternatives to compare; keep each returned image associated with the prompt and references that produced it. The producer’s image locations expire after 24 hours, so retain the actual image asset when the result becomes part of a project. The standard 3.0 edition remains distinct from Pro and from Qwen 2.1 models with their different reference counts and transparency features.
POST /v1/images/generationstext, imageimage| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits qwen/qwen-image-3 | Default— |
Fieldprompt | Typestring | RequirementRequired | Values & limits Minimum length: 1 | Default— |
Fieldresolution | Typestring | RequirementOptional | Values & limits 1K, 2K | Default— |
Fieldaspect_ratio | Typestring | RequirementOptional | Values & limits 1:1, 1:2, 1:4, 2:1, 2:3, 3:2, 3:4, 4:1, 4:3, 4:5, 5:4, 9:16, 16:9 | Default— |
Fieldn | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Maximum: 6 | Default— |
Fieldinput_references | Typearray | RequirementOptional | Values & limits Minimum items: 0 Maximum items: 4 | Default— |
Fieldprovider | Typeobject | RequirementOptional | Values & limits | Default— |
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, prompt |
Parametermaterial_types | Contracttext, image |
Lifecycle: synchronous_result_then_owned_library
GET /v1/media/tasks/{task_id} · actual returned task_id or media library record
GET /v1/media/assets/{asset_id}/content
GET /v1/media/assets
GET /v1/media/assets/{asset_id}
The displayed rates estimate the cost before generation. The final charge is calculated from the cost recorded for the original generation. Measured output duration and forecast quantities are not the final billable quantity.
Tariff references do not add request options. Supported parameters are listed for the selected method.
Match each charge to the conditions shown. Use one rate for each set of alternative configurations.
| Configuration | Rate schedule | Forecast quantity | Billing unit | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Forecast quantity— | Billing unit— | Charge Input · Input tokens · Estimate for comparison only — / 1K tokens Output · Output tokens · Estimate for comparison only — / 1K tokens Input · Cache read · Estimate for comparison only — / 1K tokens Input · Cache write · Estimate for comparison only — / 1K tokens Input · Cache write, 1 hour · Estimate for comparison only — / 1K tokens |
| Configurationinput image | Rate schedule— | Forecast quantity— | Billing unitper input image | Charge Input · input image · Estimate for comparison only $0.006 per input image |
| ConfigurationEstimated generation cost | Rate schedule— | Forecast quantityone generation estimate | Billing unit— | Charge Output · Estimated generation cost · Estimate for comparison only $0.06 estimate before generation This estimate is not an additional charge or a fixed rate for the completed generation. |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\nexport AILOOT_API_KEY\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np={'model': 'qwen/qwen-image-3', 'prompt': 'a red panda astronaut floating in space, studio lighting'}\n\nPath("request.json").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/images/generations \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport base64, json, os, subprocess\nfrom pathlib import Path\nfrom urllib.parse import urlsplit, urljoin\n\ndef save_images(items):\n if not items: raise SystemExit("No completed images; retain the full response")\n for index, item in enumerate(items):\n encoded=item.get("b64_json")\n uri=item.get("url", "")\n if not encoded and uri.startswith("data:image/"):\n header, separator, encoded=uri.partition(",")\n if not separator or ";base64" not in header: raise SystemExit("Unsupported image data URI")\n if encoded:\n raw=base64.b64decode(encoded,validate=True)\n elif uri:\n uri=urljoin("https://api.ailoot.ai",uri)\n parsed=urlsplit(uri)\n if parsed.scheme!="https" or not parsed.netloc or parsed.username or parsed.password:\n raise SystemExit("Invalid returned image URL")\n owned=parsed.netloc=="api.ailoot.ai" and parsed.path.startswith("/v1/media/assets/") and parsed.path.endswith("/content")\n args=["curl","--fail-with-body","--silent","--show-error",uri]\n if owned: args += ["-H","Authorization: Bearer "+os.environ["AILOOT_API_KEY"]]\n temporary=Path(f"image-{index}.bin")\n subprocess.run(args+["-o",str(temporary)],check=True)\n raw=temporary.read_bytes()\n temporary.unlink()\n else: raise SystemExit("Image has neither bytes nor URL")\n if not raw: raise SystemExit("Empty image result")\n ext="png" if raw.startswith(b"\\x89PNG") else "jpg" if raw.startswith(b"\\xff\\xd8") else "webp" if raw[:4]==b"RIFF" and raw[8:12]==b"WEBP" else "svg" if raw.lstrip().startswith((b"<svg",b"<?xml")) else "bin"\n Path(f"image-{index}.{ext}").write_bytes(raw)\n print("Every returned image saved; complete response metadata retained")\nsave_images(json.loads(Path("result.json").read_text())["data"])\nPY_RESULT\nDownload each returned image or save its Base64 contents
GET /v1/media/tasks/{task_id}GET /v1/media/assets/{asset_id}/contentGET /v1/media/assetsGET /v1/media/assets/{asset_id}set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/media/assets \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -o owned-assets.json\n# Preserve every returned asset and follow its exact signed content URL. Do not construct a signed token.\nSave every completed image; retrieve each returned URL or decode its Base64 contents.
Retain the complete response and headers, every actual output, original returned identifiers and all usage fields. Recover the original operation without submitting creation again.
Keep the returned X-Client-Request-ID for reconciliation. Read only actual returned task_id/asset_id values or the owned media library; never build a task URL from the operation ID and never resubmit creation.
| Field | Type |
|---|---|
Fieldcreated | Type |
Fielddata[].url | Type |
Fielddata[].b64_json | Type |
Fieldusage | Type |
Ailoot Ultra qwen/qwen-image-3
/v1/images