Nano Banana Pro is Google’s Gemini 3 Pro Image model for image generation and editing driven by reasoning. The manufacturer places complex graphic design, high-fidelity product mockups and factual data visualisation at the centre of its intended use. It accepts text and images and produces image and text. This named Pro image edition is based on Gemini 3 Pro, while retaining its own image-task input and capability profile.
The model supports 1K, 2K and 4K images. Documented shapes include 1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9 and 21:9. A square 4K image is 4096 × 4096; a 16:9 4K image is 5504 × 3072. Select resolution and aspect ratio for the artifact you need, whether a portrait poster, product scene or wide explanatory graphic. The 0.5K option and additional extreme ratios of Gemini 3.1 Flash Image are not part of this Pro table.
Text rendering supports clear lettering in posters, diagrams and other designed assets. For a complex infographic, provide the underlying facts, exact wording, a reading sequence and the relation between labels and visual elements. Separate the information to communicate from the desired artistic style. Google recommends preparing the text first and then generating the image containing it. This is particularly useful when a composition combines a headline, a diagram and several short captions.
Multi-image input supports up to 14 references. Google’s feature table describes up to six object references for fidelity, up to five character references for consistency and up to three style references. These roles help prepare a composite visual: a product photograph defines appearance, a separate picture establishes setting and a style image informs treatment. Tell the model which details are authoritative and which references provide inspiration, rather than asking it to combine every visible feature indiscriminately.
Google Search grounding can supply real-world information for a factual visualisation or a scene informed by current conditions. The Pro image workflow documents web grounding; the Image Search extension is documented specifically for Nano Banana 2.1 and Gemini 3.1 Flash Image. For a data graphic, supply the values you intend to show or request grounded information in the producer workflow, then make the relationship, labels and layout explicit. Search-informed generation still produces an image to review, not a substitute for checking the depicted facts.
Reasoning supports the composition of a complex prompt before final rendering. The image guide describes interim images used to test composition and logic, distinct from the final artifact. Do not transfer Nano Banana 2’s minimal/high control table or Nano Banana 2.1’s medium-default table to this Pro edition. For a demanding design, give a complete brief with the visual hierarchy, required object relationships and the elements that must survive later edits.
The model card describes substantive remaining limits: very small text and long passages may render poorly, character appearance can change between a reference and a result, and masked or doodle edits may only partly follow the instruction or leave marks. It can also confuse left and right or copy a source element into the new composition. For a product revision, explicitly state the preserved geometry and labels and inspect the edited area; for a diagram, compare the direction of arrows and relative object positions.
The current API table gives an input limit of 65,536 tokens and an output limit of 32,768 tokens. Image generation, thinking, search grounding and batch processing are supported; audio generation, function calling, code execution, caching, structured outputs, file search, URL context and Live API are unsupported. The image task accepts neither audio nor video input in this profile. Generated images include SynthID, and a requested number of output images is not always followed exactly.
A practical product brief can combine an image of the product, a lighting reference and the exact headline: “Preserve the product’s proportions and lettering, show it on a pale stone surface under soft side light, and leave the upper third clear for the supplied heading.” For a factual diagram, identify each entity and connection and provide the approved labels. Refine the result in a conversation by naming one change and the features to preserve, keeping the selected image and its explanatory text together as the design artifact.
", "aboutText": "Nano Banana Pro is Google’s Gemini 3 Pro Image model for image generation and editing driven by reasoning. The manufacturer places complex graphic design, high-fidelity product mockups and factual data visualisation at the centre of its intended use. It accepts text and images and produces image and text. This named Pro image edition is based on Gemini 3 Pro, while retaining its own image-task input and capability profile.\n\nThe model supports 1K, 2K and 4K images. Documented shapes include 1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9 and 21:9. A square 4K image is 4096 × 4096; a 16:9 4K image is 5504 × 3072. Select resolution and aspect ratio for the artifact you need, whether a portrait poster, product scene or wide explanatory graphic. The 0.5K option and additional extreme ratios of Gemini 3.1 Flash Image are not part of this Pro table.\n\nText rendering supports clear lettering in posters, diagrams and other designed assets. For a complex infographic, provide the underlying facts, exact wording, a reading sequence and the relation between labels and visual elements. Separate the information to communicate from the desired artistic style. Google recommends preparing the text first and then generating the image containing it. This is particularly useful when a composition combines a headline, a diagram and several short captions.\n\nMulti-image input supports up to 14 references. Google’s feature table describes up to six object references for fidelity, up to five character references for consistency and up to three style references. These roles help prepare a composite visual: a product photograph defines appearance, a separate picture establishes setting and a style image informs treatment. Tell the model which details are authoritative and which references provide inspiration, rather than asking it to combine every visible feature indiscriminately.\n\nGoogle Search grounding can supply real-world information for a factual visualisation or a scene informed by current conditions. The Pro image workflow documents web grounding; the Image Search extension is documented specifically for Nano Banana 2.1 and Gemini 3.1 Flash Image. For a data graphic, supply the values you intend to show or request grounded information in the producer workflow, then make the relationship, labels and layout explicit. Search-informed generation still produces an image to review, not a substitute for checking the depicted facts.\n\nReasoning supports the composition of a complex prompt before final rendering. The image guide describes interim images used to test composition and logic, distinct from the final artifact. Do not transfer Nano Banana 2’s minimal/high control table or Nano Banana 2.1’s medium-default table to this Pro edition. For a demanding design, give a complete brief with the visual hierarchy, required object relationships and the elements that must survive later edits.\n\nThe model card describes substantive remaining limits: very small text and long passages may render poorly, character appearance can change between a reference and a result, and masked or doodle edits may only partly follow the instruction or leave marks. It can also confuse left and right or copy a source element into the new composition. For a product revision, explicitly state the preserved geometry and labels and inspect the edited area; for a diagram, compare the direction of arrows and relative object positions.\n\nThe current API table gives an input limit of 65,536 tokens and an output limit of 32,768 tokens. Image generation, thinking, search grounding and batch processing are supported; audio generation, function calling, code execution, caching, structured outputs, file search, URL context and Live API are unsupported. The image task accepts neither audio nor video input in this profile. Generated images include SynthID, and a requested number of output images is not always followed exactly.\n\nA practical product brief can combine an image of the product, a lighting reference and the exact headline: “Preserve the product’s proportions and lettering, show it on a pale stone surface under soft side light, and leave the upper third clear for the supplied heading.” For a factual diagram, identify each entity and connection and provide the approved labels. Refine the result in a conversation by naming one change and the features to preserve, keeping the selected image and its explanatory text together as the design artifact.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Visualize a supplied packaging cycle\n\nCreate one landscape explanatory graphic showing this packaging return cycle: customer returns an empty jar, store inspects it, accepted jars are washed, clean jars are refilled. Use four large illustrated stages connected clockwise. Add only these exact labels: 'RETURN', 'INSPECT', 'WASH', 'REFILL'. Make the arrow direction and stage order unmistakable.One landscape image with four labeled stages and arrows matching the supplied cycle.Inspect that arrows connect RETURN to INSPECT to WASH to REFILL in that order, with exact labels.gemini-3-pro-imageNano Banana Pro (Gemini 3 Pro Image) is Google’s reasoning-driven image model for complex design, product mockups and factual visualisations. It accepts text and image references and generates images with text, with 1K–4K output and detailed creative control.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Nano Banana Pro is Google’s Gemini 3 Pro Image model for image generation and editing driven by reasoning. The manufacturer places complex graphic design, high-fidelity product mockups and factual data visualisation at the centre of its intended use. It accepts text and images and produces image and text. This named Pro image edition is based on Gemini 3 Pro, while retaining its own image-task input and capability profile.
The model supports 1K, 2K and 4K images. Documented shapes include 1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9 and 21:9. A square 4K image is 4096 × 4096; a 16:9 4K image is 5504 × 3072. Select resolution and aspect ratio for the artifact you need, whether a portrait poster, product scene or wide explanatory graphic. The 0.5K option and additional extreme ratios of Gemini 3.1 Flash Image are not part of this Pro table.
Text rendering supports clear lettering in posters, diagrams and other designed assets. For a complex infographic, provide the underlying facts, exact wording, a reading sequence and the relation between labels and visual elements. Separate the information to communicate from the desired artistic style. Google recommends preparing the text first and then generating the image containing it. This is particularly useful when a composition combines a headline, a diagram and several short captions.
Multi-image input supports up to 14 references. Google’s feature table describes up to six object references for fidelity, up to five character references for consistency and up to three style references. These roles help prepare a composite visual: a product photograph defines appearance, a separate picture establishes setting and a style image informs treatment. Tell the model which details are authoritative and which references provide inspiration, rather than asking it to combine every visible feature indiscriminately.
Google Search grounding can supply real-world information for a factual visualisation or a scene informed by current conditions. The Pro image workflow documents web grounding; the Image Search extension is documented specifically for Nano Banana 2.1 and Gemini 3.1 Flash Image. For a data graphic, supply the values you intend to show or request grounded information in the producer workflow, then make the relationship, labels and layout explicit. Search-informed generation still produces an image to review, not a substitute for checking the depicted facts.
Reasoning supports the composition of a complex prompt before final rendering. The image guide describes interim images used to test composition and logic, distinct from the final artifact. Do not transfer Nano Banana 2’s minimal/high control table or Nano Banana 2.1’s medium-default table to this Pro edition. For a demanding design, give a complete brief with the visual hierarchy, required object relationships and the elements that must survive later edits.
The model card describes substantive remaining limits: very small text and long passages may render poorly, character appearance can change between a reference and a result, and masked or doodle edits may only partly follow the instruction or leave marks. It can also confuse left and right or copy a source element into the new composition. For a product revision, explicitly state the preserved geometry and labels and inspect the edited area; for a diagram, compare the direction of arrows and relative object positions.
The current API table gives an input limit of 65,536 tokens and an output limit of 32,768 tokens. Image generation, thinking, search grounding and batch processing are supported; audio generation, function calling, code execution, caching, structured outputs, file search, URL context and Live API are unsupported. The image task accepts neither audio nor video input in this profile. Generated images include SynthID, and a requested number of output images is not always followed exactly.
A practical product brief can combine an image of the product, a lighting reference and the exact headline: “Preserve the product’s proportions and lettering, show it on a pale stone surface under soft side light, and leave the upper third clear for the supplied heading.” For a factual diagram, identify each entity and connection and provide the approved labels. Refine the result in a conversation by naming one change and the features to preserve, keeping the selected image and its explanatory text together as the design artifact.
POST /v1/images/generationstextimage| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits gemini-3-pro-image | Default— |
Fieldprompt | Typestring | RequirementRequired | Values & limits Minimum length: 1 | Default— |
Fieldn | Typeinteger | RequirementOptional | Values & limits Minimum: 1 | Default1 |
Fieldresponse_format | Typestring | RequirementOptional | Values & limits url, b64_json | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Fieldsize | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldquality | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldbackground | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldoutput_format | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldoutput_compression | Typeinteger | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmoderation | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldinput_fidelity | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldstyle | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldpartial_images | Typeinteger | RequirementOptional | Values & limits Model-specific | Default— |
Text to image · Required: prompt
| Parameter | Contract |
|---|---|
Parametermaterial_types | Contracttext |
Parameterrequired | Contractmodel, prompt |
Parameteroptional_controls | Contractnull |
Parametercontent_type | Contractapplication/json |
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}
Method shapes and IDs are exact; supplier capability is separate from admission.
POST /v1/images/generationsAiloot 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 →
Model-level reference schedules do not change method selection, quotes or charges. Active charges use the selected Ailoot method rates.
| Configuration | Rate schedule | Charge |
|---|---|---|
| ConfigurationUltra | Rate scheduleBase rate | Charge Input · Input tokens $0.004000 / 1K tokens Output · Output tokens $0.024000 / 1K tokens Input · image $0.004000 / 1K tokens Output · image output $0.24000 / 1K tokens Input · audio $0.004000 / 1K tokens Input · input audio cache $0.0004000 / 1K tokens Output · web search $0.028 per web search Output · internal reasoning $0.024000 / 1K tokens Input · Cache read $0.0004000 / 1K tokens Input · Cache write $0.000750000 / 1K tokens |
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\nexport AILOOT_API_KEY\nexport AILOOT_API_KEY\n: "${AILOOT_PROMPT_FILE:?Set your actual UTF-8 image brief}"\nexport AILOOT_PROMPT_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "gemini-3-pro-image", "prompt": ""}')\nf=Path(os.environ["AILOOT_PROMPT_FILE"])\nif not f.is_file():raise SystemExit("Choose your actual UTF-8 brief")\np['prompt']=f.read_text(encoding="utf-8")\nif not p['prompt'].strip():raise SystemExit("Brief must contain text")\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\nfrom openai import OpenAI\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")\n\nf=Path(os.environ["AILOOT_PROMPT_FILE"])\nif not f.is_file():raise SystemExit("Set your actual UTF-8 image brief file")\nrequest={'model': 'gemini-3-pro-image', 'prompt': ''}\nrequest["prompt"]=f.read_text(encoding="utf-8")\nclient=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)\nresult=client.images.generate(**request)\nPath("sdk-result.txt").write_text(str(result),encoding="utf-8")\nsave_images([{"b64_json":getattr(item,"b64_json",None),"url":getattr(item,"url",None)} for item in result.data])\nimport OpenAI from "openai";\nimport { readFileSync, writeFileSync } from "node:fs";\nconst key=process.env.AILOOT_API_KEY;\nif(!key) throw new Error("Set your actual Ailoot API key");\nconst client=new OpenAI({baseURL:"https://api.ailoot.ai/v1",apiKey:key,maxRetries:0});\nconst request={"model": "gemini-3-pro-image", "prompt": ""};\nif(!process.env.AILOOT_PROMPT_FILE)throw new Error("Set your actual UTF-8 image brief file");\nrequest.prompt=readFileSync(process.env.AILOOT_PROMPT_FILE,"utf8");\nconst result=await client.images.generate(request);\nwriteFileSync("sdk-result.json",JSON.stringify(result,null,2));\nif(!result.data?.length) throw new Error("No completed images");\nfor(let index=0;index<result.data.length;index++) {\n const item=result.data[index];let encoded=item.b64_json,uri=item.url||"",raw;\n if(!encoded && uri.startsWith("data:image/")) {\n const comma=uri.indexOf(",");if(comma<0 || !uri.slice(0,comma).includes(";base64")) throw new Error("Unsupported image data URI");\n encoded=uri.slice(comma+1);\n }\n if(encoded) {\n if(encoded.length%4!==0 || !/^[A-Za-z0-9+/]*={0,2}$/.test(encoded)) throw new Error("Invalid Base64");\n raw=Buffer.from(encoded,"base64");\n } else if(uri) {\n const u=new URL(uri,"https://api.ailoot.ai");\n if(u.protocol!=="https:" || u.username || u.password) throw new Error("Invalid returned URL");\n const owned=u.origin==="https://api.ailoot.ai" && u.pathname.startsWith("/v1/media/assets/") && u.pathname.endsWith("/content");\n const response=await fetch(u,{headers:owned?{Authorization:"Bearer "+key}:{},redirect:"manual"});\n if(!response.ok) throw new Error("Image content download failed: "+response.status);\n raw=Buffer.from(await response.arrayBuffer());\n } else throw new Error("Image has neither bytes nor URL");\n if(!raw.length) throw new Error("Empty image result");\n const ext=raw.subarray(0,4).equals(Buffer.from([137,80,78,71]))?"png":raw[0]===255&&raw[1]===216?"jpg":raw.toString("ascii",0,4)==="RIFF"&&raw.toString("ascii",8,12)==="WEBP"?"webp":raw.toString("utf8").trimStart().startsWith("<svg")||raw.toString("utf8").trimStart().startsWith("<?xml")?"svg":"bin";\n writeFileSync(`image-${index}.${ext}`,raw);\n}\nconsole.log("Every returned image saved; sdk-result.json retains complete metadata");\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}Save every completed image; retrieve each returned URL or decode its Base64 contents.
Save every data item. Decode each b64_json or fetch every returned signed URL. Select a filename extension from the actual MIME/bytes.
If an image generation or edit result requires a check, keep the original operation_id/X-Client-Request-ID and returned result information. Recover existing owned results through the media library using actual task_id or asset_id values. Do not resubmit the generation/edit request or construct a polling URL from operation_id.
| Field | Type |
|---|---|
Fieldcreated | Typeinteger |
Fielddata[].url | Typestring |
Fielddata[].b64_json | Typebase64 string |
Fieldusage | Typeobject |
| Response | Condition | Next step |
|---|---|---|
Response400 · invalid_request_error | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. |
Responseserver_error | ConditionThe returned response says the operation or result requires a check | Next stepKeep the original returned operation/task ID, request headers and response; continue supported reads of that original operation. Do not resubmit the creation request. |
Ailoot gemini-3-pro-image, gemini-3-pro-image-preview, nano-banana-pro
Ailoot Ultra google/gemini-3-pro-image
/v1/images
google/gemini-3-pro-image, gemini-3-pro-image-preview, nano-banana-pro, gemini-3-pro-image
gemini-3-pro-image-previewNano Banana Pro (Gemini 3 Pro Image) is Google’s reasoning-driven image model for complex design, product mockups and factual visualisations. It accepts text and image references and generates images with text, with 1K–4K output and detailed creative control.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Nano Banana Pro is Google’s Gemini 3 Pro Image model for image generation and editing driven by reasoning. The manufacturer places complex graphic design, high-fidelity product mockups and factual data visualisation at the centre of its intended use. It accepts text and images and produces image and text. This named Pro image edition is based on Gemini 3 Pro, while retaining its own image-task input and capability profile.
The model supports 1K, 2K and 4K images. Documented shapes include 1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9 and 21:9. A square 4K image is 4096 × 4096; a 16:9 4K image is 5504 × 3072. Select resolution and aspect ratio for the artifact you need, whether a portrait poster, product scene or wide explanatory graphic. The 0.5K option and additional extreme ratios of Gemini 3.1 Flash Image are not part of this Pro table.
Text rendering supports clear lettering in posters, diagrams and other designed assets. For a complex infographic, provide the underlying facts, exact wording, a reading sequence and the relation between labels and visual elements. Separate the information to communicate from the desired artistic style. Google recommends preparing the text first and then generating the image containing it. This is particularly useful when a composition combines a headline, a diagram and several short captions.
Multi-image input supports up to 14 references. Google’s feature table describes up to six object references for fidelity, up to five character references for consistency and up to three style references. These roles help prepare a composite visual: a product photograph defines appearance, a separate picture establishes setting and a style image informs treatment. Tell the model which details are authoritative and which references provide inspiration, rather than asking it to combine every visible feature indiscriminately.
Google Search grounding can supply real-world information for a factual visualisation or a scene informed by current conditions. The Pro image workflow documents web grounding; the Image Search extension is documented specifically for Nano Banana 2.1 and Gemini 3.1 Flash Image. For a data graphic, supply the values you intend to show or request grounded information in the producer workflow, then make the relationship, labels and layout explicit. Search-informed generation still produces an image to review, not a substitute for checking the depicted facts.
Reasoning supports the composition of a complex prompt before final rendering. The image guide describes interim images used to test composition and logic, distinct from the final artifact. Do not transfer Nano Banana 2’s minimal/high control table or Nano Banana 2.1’s medium-default table to this Pro edition. For a demanding design, give a complete brief with the visual hierarchy, required object relationships and the elements that must survive later edits.
The model card describes substantive remaining limits: very small text and long passages may render poorly, character appearance can change between a reference and a result, and masked or doodle edits may only partly follow the instruction or leave marks. It can also confuse left and right or copy a source element into the new composition. For a product revision, explicitly state the preserved geometry and labels and inspect the edited area; for a diagram, compare the direction of arrows and relative object positions.
The current API table gives an input limit of 65,536 tokens and an output limit of 32,768 tokens. Image generation, thinking, search grounding and batch processing are supported; audio generation, function calling, code execution, caching, structured outputs, file search, URL context and Live API are unsupported. The image task accepts neither audio nor video input in this profile. Generated images include SynthID, and a requested number of output images is not always followed exactly.
A practical product brief can combine an image of the product, a lighting reference and the exact headline: “Preserve the product’s proportions and lettering, show it on a pale stone surface under soft side light, and leave the upper third clear for the supplied heading.” For a factual diagram, identify each entity and connection and provide the approved labels. Refine the result in a conversation by naming one change and the features to preserve, keeping the selected image and its explanatory text together as the design artifact.
POST /v1/images/generationstextimage| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits gemini-3-pro-image-preview | Default— |
Fieldprompt | Typestring | RequirementRequired | Values & limits Minimum length: 1 | Default— |
Fieldn | Typeinteger | RequirementOptional | Values & limits Minimum: 1 | Default1 |
Fieldresponse_format | Typestring | RequirementOptional | Values & limits url, b64_json | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Fieldsize | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldquality | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldbackground | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldoutput_format | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldoutput_compression | Typeinteger | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmoderation | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldinput_fidelity | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldstyle | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldpartial_images | Typeinteger | RequirementOptional | Values & limits Model-specific | Default— |
Text to image · Required: prompt
| Parameter | Contract |
|---|---|
Parametermaterial_types | Contracttext |
Parameterrequired | Contractmodel, prompt |
Parameteroptional_controls | Contractnull |
Parametercontent_type | Contractapplication/json |
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}
Method shapes and IDs are exact; supplier capability is separate from admission.
POST /v1/images/generationsAiloot 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 →
Model-level reference schedules do not change method selection, quotes or charges. Active charges use the selected Ailoot method rates.
| Configuration | Rate schedule | Charge |
|---|---|---|
| ConfigurationUltra | Rate scheduleBase rate | Charge Input · Input tokens $0.004000 / 1K tokens Output · Output tokens $0.024000 / 1K tokens Input · image $0.004000 / 1K tokens Output · image output $0.24000 / 1K tokens Input · audio $0.004000 / 1K tokens Input · input audio cache $0.0004000 / 1K tokens Output · web search $0.028 per web search Output · internal reasoning $0.024000 / 1K tokens Input · Cache read $0.0004000 / 1K tokens Input · Cache write $0.000750000 / 1K tokens |
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\nexport AILOOT_API_KEY\nexport AILOOT_API_KEY\n: "${AILOOT_PROMPT_FILE:?Set your actual UTF-8 image brief}"\nexport AILOOT_PROMPT_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "gemini-3-pro-image-preview", "prompt": ""}')\nf=Path(os.environ["AILOOT_PROMPT_FILE"])\nif not f.is_file():raise SystemExit("Choose your actual UTF-8 brief")\np['prompt']=f.read_text(encoding="utf-8")\nif not p['prompt'].strip():raise SystemExit("Brief must contain text")\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\nfrom openai import OpenAI\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")\n\nf=Path(os.environ["AILOOT_PROMPT_FILE"])\nif not f.is_file():raise SystemExit("Set your actual UTF-8 image brief file")\nrequest={'model': 'gemini-3-pro-image-preview', 'prompt': ''}\nrequest["prompt"]=f.read_text(encoding="utf-8")\nclient=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)\nresult=client.images.generate(**request)\nPath("sdk-result.txt").write_text(str(result),encoding="utf-8")\nsave_images([{"b64_json":getattr(item,"b64_json",None),"url":getattr(item,"url",None)} for item in result.data])\nimport OpenAI from "openai";\nimport { readFileSync, writeFileSync } from "node:fs";\nconst key=process.env.AILOOT_API_KEY;\nif(!key) throw new Error("Set your actual Ailoot API key");\nconst client=new OpenAI({baseURL:"https://api.ailoot.ai/v1",apiKey:key,maxRetries:0});\nconst request={"model": "gemini-3-pro-image-preview", "prompt": ""};\nif(!process.env.AILOOT_PROMPT_FILE)throw new Error("Set your actual UTF-8 image brief file");\nrequest.prompt=readFileSync(process.env.AILOOT_PROMPT_FILE,"utf8");\nconst result=await client.images.generate(request);\nwriteFileSync("sdk-result.json",JSON.stringify(result,null,2));\nif(!result.data?.length) throw new Error("No completed images");\nfor(let index=0;index<result.data.length;index++) {\n const item=result.data[index];let encoded=item.b64_json,uri=item.url||"",raw;\n if(!encoded && uri.startsWith("data:image/")) {\n const comma=uri.indexOf(",");if(comma<0 || !uri.slice(0,comma).includes(";base64")) throw new Error("Unsupported image data URI");\n encoded=uri.slice(comma+1);\n }\n if(encoded) {\n if(encoded.length%4!==0 || !/^[A-Za-z0-9+/]*={0,2}$/.test(encoded)) throw new Error("Invalid Base64");\n raw=Buffer.from(encoded,"base64");\n } else if(uri) {\n const u=new URL(uri,"https://api.ailoot.ai");\n if(u.protocol!=="https:" || u.username || u.password) throw new Error("Invalid returned URL");\n const owned=u.origin==="https://api.ailoot.ai" && u.pathname.startsWith("/v1/media/assets/") && u.pathname.endsWith("/content");\n const response=await fetch(u,{headers:owned?{Authorization:"Bearer "+key}:{},redirect:"manual"});\n if(!response.ok) throw new Error("Image content download failed: "+response.status);\n raw=Buffer.from(await response.arrayBuffer());\n } else throw new Error("Image has neither bytes nor URL");\n if(!raw.length) throw new Error("Empty image result");\n const ext=raw.subarray(0,4).equals(Buffer.from([137,80,78,71]))?"png":raw[0]===255&&raw[1]===216?"jpg":raw.toString("ascii",0,4)==="RIFF"&&raw.toString("ascii",8,12)==="WEBP"?"webp":raw.toString("utf8").trimStart().startsWith("<svg")||raw.toString("utf8").trimStart().startsWith("<?xml")?"svg":"bin";\n writeFileSync(`image-${index}.${ext}`,raw);\n}\nconsole.log("Every returned image saved; sdk-result.json retains complete metadata");\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}Save every completed image; retrieve each returned URL or decode its Base64 contents.
Save every data item. Decode each b64_json or fetch every returned signed URL. Select a filename extension from the actual MIME/bytes.
If an image generation or edit result requires a check, keep the original operation_id/X-Client-Request-ID and returned result information. Recover existing owned results through the media library using actual task_id or asset_id values. Do not resubmit the generation/edit request or construct a polling URL from operation_id.
| Field | Type |
|---|---|
Fieldcreated | Typeinteger |
Fielddata[].url | Typestring |
Fielddata[].b64_json | Typebase64 string |
Fieldusage | Typeobject |
| Response | Condition | Next step |
|---|---|---|
Response400 · invalid_request_error | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. |
Responseserver_error | ConditionThe returned response says the operation or result requires a check | Next stepKeep the original returned operation/task ID, request headers and response; continue supported reads of that original operation. Do not resubmit the creation request. |
Ailoot gemini-3-pro-image, gemini-3-pro-image-preview, nano-banana-pro
Ailoot Ultra google/gemini-3-pro-image
/v1/images
google/gemini-3-pro-image, gemini-3-pro-image-preview, nano-banana-pro, gemini-3-pro-image
nano-banana-proNano Banana Pro (Gemini 3 Pro Image) is Google’s reasoning-driven image model for complex design, product mockups and factual visualisations. It accepts text and image references and generates images with text, with 1K–4K output and detailed creative control.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Nano Banana Pro is Google’s Gemini 3 Pro Image model for image generation and editing driven by reasoning. The manufacturer places complex graphic design, high-fidelity product mockups and factual data visualisation at the centre of its intended use. It accepts text and images and produces image and text. This named Pro image edition is based on Gemini 3 Pro, while retaining its own image-task input and capability profile.
The model supports 1K, 2K and 4K images. Documented shapes include 1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9 and 21:9. A square 4K image is 4096 × 4096; a 16:9 4K image is 5504 × 3072. Select resolution and aspect ratio for the artifact you need, whether a portrait poster, product scene or wide explanatory graphic. The 0.5K option and additional extreme ratios of Gemini 3.1 Flash Image are not part of this Pro table.
Text rendering supports clear lettering in posters, diagrams and other designed assets. For a complex infographic, provide the underlying facts, exact wording, a reading sequence and the relation between labels and visual elements. Separate the information to communicate from the desired artistic style. Google recommends preparing the text first and then generating the image containing it. This is particularly useful when a composition combines a headline, a diagram and several short captions.
Multi-image input supports up to 14 references. Google’s feature table describes up to six object references for fidelity, up to five character references for consistency and up to three style references. These roles help prepare a composite visual: a product photograph defines appearance, a separate picture establishes setting and a style image informs treatment. Tell the model which details are authoritative and which references provide inspiration, rather than asking it to combine every visible feature indiscriminately.
Google Search grounding can supply real-world information for a factual visualisation or a scene informed by current conditions. The Pro image workflow documents web grounding; the Image Search extension is documented specifically for Nano Banana 2.1 and Gemini 3.1 Flash Image. For a data graphic, supply the values you intend to show or request grounded information in the producer workflow, then make the relationship, labels and layout explicit. Search-informed generation still produces an image to review, not a substitute for checking the depicted facts.
Reasoning supports the composition of a complex prompt before final rendering. The image guide describes interim images used to test composition and logic, distinct from the final artifact. Do not transfer Nano Banana 2’s minimal/high control table or Nano Banana 2.1’s medium-default table to this Pro edition. For a demanding design, give a complete brief with the visual hierarchy, required object relationships and the elements that must survive later edits.
The model card describes substantive remaining limits: very small text and long passages may render poorly, character appearance can change between a reference and a result, and masked or doodle edits may only partly follow the instruction or leave marks. It can also confuse left and right or copy a source element into the new composition. For a product revision, explicitly state the preserved geometry and labels and inspect the edited area; for a diagram, compare the direction of arrows and relative object positions.
The current API table gives an input limit of 65,536 tokens and an output limit of 32,768 tokens. Image generation, thinking, search grounding and batch processing are supported; audio generation, function calling, code execution, caching, structured outputs, file search, URL context and Live API are unsupported. The image task accepts neither audio nor video input in this profile. Generated images include SynthID, and a requested number of output images is not always followed exactly.
A practical product brief can combine an image of the product, a lighting reference and the exact headline: “Preserve the product’s proportions and lettering, show it on a pale stone surface under soft side light, and leave the upper third clear for the supplied heading.” For a factual diagram, identify each entity and connection and provide the approved labels. Refine the result in a conversation by naming one change and the features to preserve, keeping the selected image and its explanatory text together as the design artifact.
POST /v1/images/generationstextimage| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits nano-banana-pro | Default— |
Fieldprompt | Typestring | RequirementRequired | Values & limits Minimum length: 1 | Default— |
Fieldn | Typeinteger | RequirementOptional | Values & limits Minimum: 1 | Default1 |
Fieldresponse_format | Typestring | RequirementOptional | Values & limits url, b64_json | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Fieldsize | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldquality | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldbackground | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldoutput_format | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldoutput_compression | Typeinteger | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmoderation | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldinput_fidelity | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldstyle | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldpartial_images | Typeinteger | RequirementOptional | Values & limits Model-specific | Default— |
Text to image · Required: prompt
| Parameter | Contract |
|---|---|
Parametermaterial_types | Contracttext |
Parameterrequired | Contractmodel, prompt |
Parameteroptional_controls | Contractnull |
Parametercontent_type | Contractapplication/json |
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}
Method shapes and IDs are exact; supplier capability is separate from admission.
POST /v1/images/generationsAiloot 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 →
Model-level reference schedules do not change method selection, quotes or charges. Active charges use the selected Ailoot method rates.
| Configuration | Rate schedule | Charge |
|---|---|---|
| ConfigurationUltra | Rate scheduleBase rate | Charge Input · Input tokens $0.004000 / 1K tokens Output · Output tokens $0.024000 / 1K tokens Input · image $0.004000 / 1K tokens Output · image output $0.24000 / 1K tokens Input · audio $0.004000 / 1K tokens Input · input audio cache $0.0004000 / 1K tokens Output · web search $0.028 per web search Output · internal reasoning $0.024000 / 1K tokens Input · Cache read $0.0004000 / 1K tokens Input · Cache write $0.000750000 / 1K tokens |
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\nexport AILOOT_API_KEY\nexport AILOOT_API_KEY\n: "${AILOOT_PROMPT_FILE:?Set your actual UTF-8 image brief}"\nexport AILOOT_PROMPT_FILE\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "nano-banana-pro", "prompt": ""}')\nf=Path(os.environ["AILOOT_PROMPT_FILE"])\nif not f.is_file():raise SystemExit("Choose your actual UTF-8 brief")\np['prompt']=f.read_text(encoding="utf-8")\nif not p['prompt'].strip():raise SystemExit("Brief must contain text")\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\nfrom openai import OpenAI\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")\n\nf=Path(os.environ["AILOOT_PROMPT_FILE"])\nif not f.is_file():raise SystemExit("Set your actual UTF-8 image brief file")\nrequest={'model': 'nano-banana-pro', 'prompt': ''}\nrequest["prompt"]=f.read_text(encoding="utf-8")\nclient=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)\nresult=client.images.generate(**request)\nPath("sdk-result.txt").write_text(str(result),encoding="utf-8")\nsave_images([{"b64_json":getattr(item,"b64_json",None),"url":getattr(item,"url",None)} for item in result.data])\nimport OpenAI from "openai";\nimport { readFileSync, writeFileSync } from "node:fs";\nconst key=process.env.AILOOT_API_KEY;\nif(!key) throw new Error("Set your actual Ailoot API key");\nconst client=new OpenAI({baseURL:"https://api.ailoot.ai/v1",apiKey:key,maxRetries:0});\nconst request={"model": "nano-banana-pro", "prompt": ""};\nif(!process.env.AILOOT_PROMPT_FILE)throw new Error("Set your actual UTF-8 image brief file");\nrequest.prompt=readFileSync(process.env.AILOOT_PROMPT_FILE,"utf8");\nconst result=await client.images.generate(request);\nwriteFileSync("sdk-result.json",JSON.stringify(result,null,2));\nif(!result.data?.length) throw new Error("No completed images");\nfor(let index=0;index<result.data.length;index++) {\n const item=result.data[index];let encoded=item.b64_json,uri=item.url||"",raw;\n if(!encoded && uri.startsWith("data:image/")) {\n const comma=uri.indexOf(",");if(comma<0 || !uri.slice(0,comma).includes(";base64")) throw new Error("Unsupported image data URI");\n encoded=uri.slice(comma+1);\n }\n if(encoded) {\n if(encoded.length%4!==0 || !/^[A-Za-z0-9+/]*={0,2}$/.test(encoded)) throw new Error("Invalid Base64");\n raw=Buffer.from(encoded,"base64");\n } else if(uri) {\n const u=new URL(uri,"https://api.ailoot.ai");\n if(u.protocol!=="https:" || u.username || u.password) throw new Error("Invalid returned URL");\n const owned=u.origin==="https://api.ailoot.ai" && u.pathname.startsWith("/v1/media/assets/") && u.pathname.endsWith("/content");\n const response=await fetch(u,{headers:owned?{Authorization:"Bearer "+key}:{},redirect:"manual"});\n if(!response.ok) throw new Error("Image content download failed: "+response.status);\n raw=Buffer.from(await response.arrayBuffer());\n } else throw new Error("Image has neither bytes nor URL");\n if(!raw.length) throw new Error("Empty image result");\n const ext=raw.subarray(0,4).equals(Buffer.from([137,80,78,71]))?"png":raw[0]===255&&raw[1]===216?"jpg":raw.toString("ascii",0,4)==="RIFF"&&raw.toString("ascii",8,12)==="WEBP"?"webp":raw.toString("utf8").trimStart().startsWith("<svg")||raw.toString("utf8").trimStart().startsWith("<?xml")?"svg":"bin";\n writeFileSync(`image-${index}.${ext}`,raw);\n}\nconsole.log("Every returned image saved; sdk-result.json retains complete metadata");\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}Save every completed image; retrieve each returned URL or decode its Base64 contents.
Save every data item. Decode each b64_json or fetch every returned signed URL. Select a filename extension from the actual MIME/bytes.
If an image generation or edit result requires a check, keep the original operation_id/X-Client-Request-ID and returned result information. Recover existing owned results through the media library using actual task_id or asset_id values. Do not resubmit the generation/edit request or construct a polling URL from operation_id.
| Field | Type |
|---|---|
Fieldcreated | Typeinteger |
Fielddata[].url | Typestring |
Fielddata[].b64_json | Typebase64 string |
Fieldusage | Typeobject |
| Response | Condition | Next step |
|---|---|---|
Response400 · invalid_request_error | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. |
Responseserver_error | ConditionThe returned response says the operation or result requires a check | Next stepKeep the original returned operation/task ID, request headers and response; continue supported reads of that original operation. Do not resubmit the creation request. |
Ailoot gemini-3-pro-image, gemini-3-pro-image-preview, nano-banana-pro
Ailoot Ultra google/gemini-3-pro-image
/v1/images
google/gemini-3-pro-image, gemini-3-pro-image-preview, nano-banana-pro, gemini-3-pro-image
google/gemini-3-pro-imageNano Banana Pro (Gemini 3 Pro Image) is Google’s reasoning-driven image model for complex design, product mockups and factual visualisations. It accepts text and image references and generates images with text, with 1K–4K output and detailed creative control.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Nano Banana Pro is Google’s Gemini 3 Pro Image model for image generation and editing driven by reasoning. The manufacturer places complex graphic design, high-fidelity product mockups and factual data visualisation at the centre of its intended use. It accepts text and images and produces image and text. This named Pro image edition is based on Gemini 3 Pro, while retaining its own image-task input and capability profile.
The model supports 1K, 2K and 4K images. Documented shapes include 1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9 and 21:9. A square 4K image is 4096 × 4096; a 16:9 4K image is 5504 × 3072. Select resolution and aspect ratio for the artifact you need, whether a portrait poster, product scene or wide explanatory graphic. The 0.5K option and additional extreme ratios of Gemini 3.1 Flash Image are not part of this Pro table.
Text rendering supports clear lettering in posters, diagrams and other designed assets. For a complex infographic, provide the underlying facts, exact wording, a reading sequence and the relation between labels and visual elements. Separate the information to communicate from the desired artistic style. Google recommends preparing the text first and then generating the image containing it. This is particularly useful when a composition combines a headline, a diagram and several short captions.
Multi-image input supports up to 14 references. Google’s feature table describes up to six object references for fidelity, up to five character references for consistency and up to three style references. These roles help prepare a composite visual: a product photograph defines appearance, a separate picture establishes setting and a style image informs treatment. Tell the model which details are authoritative and which references provide inspiration, rather than asking it to combine every visible feature indiscriminately.
Google Search grounding can supply real-world information for a factual visualisation or a scene informed by current conditions. The Pro image workflow documents web grounding; the Image Search extension is documented specifically for Nano Banana 2.1 and Gemini 3.1 Flash Image. For a data graphic, supply the values you intend to show or request grounded information in the producer workflow, then make the relationship, labels and layout explicit. Search-informed generation still produces an image to review, not a substitute for checking the depicted facts.
Reasoning supports the composition of a complex prompt before final rendering. The image guide describes interim images used to test composition and logic, distinct from the final artifact. Do not transfer Nano Banana 2’s minimal/high control table or Nano Banana 2.1’s medium-default table to this Pro edition. For a demanding design, give a complete brief with the visual hierarchy, required object relationships and the elements that must survive later edits.
The model card describes substantive remaining limits: very small text and long passages may render poorly, character appearance can change between a reference and a result, and masked or doodle edits may only partly follow the instruction or leave marks. It can also confuse left and right or copy a source element into the new composition. For a product revision, explicitly state the preserved geometry and labels and inspect the edited area; for a diagram, compare the direction of arrows and relative object positions.
The current API table gives an input limit of 65,536 tokens and an output limit of 32,768 tokens. Image generation, thinking, search grounding and batch processing are supported; audio generation, function calling, code execution, caching, structured outputs, file search, URL context and Live API are unsupported. The image task accepts neither audio nor video input in this profile. Generated images include SynthID, and a requested number of output images is not always followed exactly.
A practical product brief can combine an image of the product, a lighting reference and the exact headline: “Preserve the product’s proportions and lettering, show it on a pale stone surface under soft side light, and leave the upper third clear for the supplied heading.” For a factual diagram, identify each entity and connection and provide the approved labels. Refine the result in a conversation by naming one change and the features to preserve, keeping the selected image and its explanatory text together as the design artifact.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits google/gemini-3-pro-image | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits Minimum items: 1 | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits Examples: system, user, assistant, tool, function Conditions: These roles are explicitly represented by Source compatibility types; the exact model role set remains model-specific. Model-specific | Default— |
Fieldmessages[].content | Typestring|content_part[]|null | RequirementOptional | Values & limits | Default— |
Fieldmessages[].tool_calls | Typetool_call[] | RequirementOptional | Values & limits | Default— |
Fieldmessages[].tool_call_id | Typestring | RequirementOptional | Values & limits Applies when: role=tool; use actual returned ID | Default— |
Fieldmessages[].name | Typestring | RequirementOptional | Values & limits | Default— |
Fieldmessages[].reasoning_content | Typestring | RequirementOptional | Values & limits Applies when: Round-trip actual returned reasoning where this model uses it | Default— |
Fieldmessages[].reasoning_details | Typeobject|array | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmax_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
Fieldmax_completion_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Fieldstream_options.include_usage | Typeboolean | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtemperature | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtop_p | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldstop | Typestring|string[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools | Typefunction_tool[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools[].type | Typestring | RequirementOptional | Values & limits function Model-specific | Default— |
Fieldtools[].function.name | Typestring | RequirementRequired | Values & limits Applies when: function tool supplied | Default— |
Fieldtools[].function.description | Typestring | RequirementOptional | Values & limits | Default— |
Fieldtools[].function.parameters | TypeJSON_schema | RequirementOptional | Values & limits | Default— |
Fieldtool_choice | Typestring|object | RequirementOptional | Values & limits Model-specific | Default— |
Fieldparallel_tool_calls | Typeboolean | RequirementOptional | Values & limits Model-specific | Default— |
Fieldresponse_format | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldreasoning_effort | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldreasoning | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldservice_tier | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldinstructions | Typestring | RequirementOptional | Values & limits Compatibility field | Default— |
Fieldfunctions | Typefunction[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldfunction_call | Typestring|object | RequirementOptional | Values & limits Model-specific | Default— |
Fieldprompt_cache_options | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmodalities | Typestring[] | RequirementOptional | Values & limits text | Default— |
Fieldprovider | Typeobject | RequirementOptional | Values & limits Conditions: provider.options is rejected on this Ultra text route | Default— |
Fieldcache_control.ttl | Typestring | RequirementOptional | Values & limits Conditions: 1h is rejected in actual prompt-cache locations | Default— |
Text · UTF8 text
messages[].content
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, messages |
Parametermaterial_types | Contracttext |
Parametermessages | Contractmessage array with role/content shape |
Parameterfile_material | Contractactual UTF-8 document inserted as literal text; no file ID inferred |
Parameteroptional_controls | Contractstream, temperature, top_p, stop, max_tokens, max_completion_tokens, reasoning, response_format, tools, tool_choice |
Parametercontrol_scope | Contractmodel-specific; user function tools differ from hosted tools |
Parameteroutput_modalities | Contracttext |
Parameterhosted_operations | Contractfalse |
Parametermultimodal_input_binding | Contractnull |
Lifecycle: synchronous_or_streamed
Send exact model ID with messages.
Context length is not an output cap.
A text file may supply literal text; image/audio/file-object inputs need exact binding.
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 | Context tokens, more than | Context tokens, up to and including | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.004 / 1K tokens Output · Output tokens $0.024 / 1K tokens Input · Cache read $0.0004 / 1K tokens Input · Cache write $0.00075 / 1K tokens Input · Cache write, 1 hour — / 1K tokens Output · Image output tokens $0.24 / 1K image tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.004 / 1K tokens Output · Output tokens $0.024 / 1K tokens Input · Cache read $0.0004 / 1K tokens Input · Cache write $0.00075 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Model-level reference schedules do not change method selection, quotes or charges. Active charges use the selected Ailoot method rates.
| Configuration | Billing conditions | Charge |
|---|---|---|
| ConfigurationGoogle: Nano Banana Pro (Gemini 3 Pro Image) · Default configuration | Billing conditionsDefault configuration | Charge Input · Input tokens $0.004 / 1K tokens Output · Output tokens $0.024 / 1K tokens Input · Input image $0.004 / 1K tokens Output · Output image $0.24 / 1K tokens Input · Audio input $0.004 / 1K tokens Input · Audio cache read $0.0004 / 1K tokens Output · Web search $0.028 per search Output · Reasoning tokens $0.024 / 1K tokens Input · Cache read $0.0004 / 1K tokens Input · Cache write · 5 minutes $0.00075 / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "google/gemini-3-pro-image", "messages": [{"role": "user", "content": "Explain the difference between an observation and a hypothesis in three sentences."}]}')\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/chat/completions \\\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 json\nfrom pathlib import Path\nr=json.loads(Path("result.json").read_text())\nfor x in r.get("choices",[]):\n if x.get("message",{}).get("content"): print(x["message"]["content"])\nPY_RESULT\nfrom openai import OpenAI\nimport os\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)\nresult=client.chat.completions.create(model='google/gemini-3-pro-image',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}])\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem="result"):\n # Keep every field, including usage, IDs, reasoning and tool requests.\n Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")\n print(json.dumps(data, ensure_ascii=False, indent=2))\n def save_blob(encoded, suffix, label):\n raw = base64.b64decode(encoded, altchars=b"-_", validate=True)\n ext = "png" if raw.startswith(b"\\x89PNG\\r\\n\\x1a\\n") else "jpg" if raw.startswith(b"\\xff\\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix\n file = Path(stem + "-" + label + "." + ext)\n file.write_bytes(raw)\n print("Saved", file)\n for index, block in enumerate(data.get("content") or []):\n print("Content", index, json.dumps(block, ensure_ascii=False))\n for choice in data.get("choices") or []:\n print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))\n for index, item in enumerate(data.get("output") or []):\n print("Output", index, json.dumps(item, ensure_ascii=False))\n if item.get("type") == "image_generation_call" and item.get("result"):\n save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))\n for ci, candidate in enumerate(data.get("candidates") or []):\n for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):\n print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))\n blob = part.get("inlineData") or part.get("inline_data")\n if blob and blob.get("data"):\n mime = blob.get("mimeType") or blob.get("mime_type")\n suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")\n save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))\n\nsave_result(result.to_dict(mode="json"))\nfrom openai import OpenAI\nimport os\nfrom pathlib import Path\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem="result"):\n # Keep every field, including usage, IDs, reasoning and tool requests.\n Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")\n print(json.dumps(data, ensure_ascii=False, indent=2))\n def save_blob(encoded, suffix, label):\n raw = base64.b64decode(encoded, altchars=b"-_", validate=True)\n ext = "png" if raw.startswith(b"\\x89PNG\\r\\n\\x1a\\n") else "jpg" if raw.startswith(b"\\xff\\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix\n file = Path(stem + "-" + label + "." + ext)\n file.write_bytes(raw)\n print("Saved", file)\n for index, block in enumerate(data.get("content") or []):\n print("Content", index, json.dumps(block, ensure_ascii=False))\n for choice in data.get("choices") or []:\n print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))\n for index, item in enumerate(data.get("output") or []):\n print("Output", index, json.dumps(item, ensure_ascii=False))\n if item.get("type") == "image_generation_call" and item.get("result"):\n save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))\n for ci, candidate in enumerate(data.get("candidates") or []):\n for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):\n print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))\n blob = part.get("inlineData") or part.get("inline_data")\n if blob and blob.get("data"):\n mime = blob.get("mimeType") or blob.get("mime_type")\n suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")\n save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))\n\nwith client.chat.completions.stream(model='google/gemini-3-pro-image',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}]) as stream:\n with Path("stream-events.jsonl").open("w",encoding="utf-8") as log:\n for event in stream:\n payload=event.to_dict(mode="json")\n log.write(json.dumps(payload,ensure_ascii=False)+"\\n"); log.flush()\n print(json.dumps(payload,ensure_ascii=False))\n final=stream.get_final_completion()\n save_result(final.to_dict(mode="json"),"stream-result")\njson or SSE
Keep every complete returned response, file and content part.
Keep every choice and its content/tool calls/reasoning. In SSE assemble deltas by choice/tool index, retain the final finish reason and usage.
Keep request/response IDs and headers. No generic text job retrieval URL is supplied.
| Field | Type |
|---|---|
Fieldchoices[].index | Typeinteger |
Fieldchoices[].message.content | Typestring|null |
Fieldchoices[].message.tool_calls | Typetool_call[] |
Fieldchoices[].message.reasoning_content | Typestring |
Fieldchoices[].message.reasoning_details | Typeobject|array |
Fieldchoices[].finish_reason | Typestring|null |
Fieldusage | Typeobject |
| Response | Condition | Next step |
|---|---|---|
Response400 · invalid_request_error | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. |
Ailoot gemini-3-pro-image, gemini-3-pro-image-preview, nano-banana-pro
Ailoot Ultra google/gemini-3-pro-image
/v1/images
google/gemini-3-pro-image, gemini-3-pro-image-preview, nano-banana-pro, gemini-3-pro-image
google/gemini-3-pro-imageNano Banana Pro (Gemini 3 Pro Image) is Google’s reasoning-driven image model for complex design, product mockups and factual visualisations. It accepts text and image references and generates images with text, with 1K–4K output and detailed creative control.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Nano Banana Pro is Google’s Gemini 3 Pro Image model for image generation and editing driven by reasoning. The manufacturer places complex graphic design, high-fidelity product mockups and factual data visualisation at the centre of its intended use. It accepts text and images and produces image and text. This named Pro image edition is based on Gemini 3 Pro, while retaining its own image-task input and capability profile.
The model supports 1K, 2K and 4K images. Documented shapes include 1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9 and 21:9. A square 4K image is 4096 × 4096; a 16:9 4K image is 5504 × 3072. Select resolution and aspect ratio for the artifact you need, whether a portrait poster, product scene or wide explanatory graphic. The 0.5K option and additional extreme ratios of Gemini 3.1 Flash Image are not part of this Pro table.
Text rendering supports clear lettering in posters, diagrams and other designed assets. For a complex infographic, provide the underlying facts, exact wording, a reading sequence and the relation between labels and visual elements. Separate the information to communicate from the desired artistic style. Google recommends preparing the text first and then generating the image containing it. This is particularly useful when a composition combines a headline, a diagram and several short captions.
Multi-image input supports up to 14 references. Google’s feature table describes up to six object references for fidelity, up to five character references for consistency and up to three style references. These roles help prepare a composite visual: a product photograph defines appearance, a separate picture establishes setting and a style image informs treatment. Tell the model which details are authoritative and which references provide inspiration, rather than asking it to combine every visible feature indiscriminately.
Google Search grounding can supply real-world information for a factual visualisation or a scene informed by current conditions. The Pro image workflow documents web grounding; the Image Search extension is documented specifically for Nano Banana 2.1 and Gemini 3.1 Flash Image. For a data graphic, supply the values you intend to show or request grounded information in the producer workflow, then make the relationship, labels and layout explicit. Search-informed generation still produces an image to review, not a substitute for checking the depicted facts.
Reasoning supports the composition of a complex prompt before final rendering. The image guide describes interim images used to test composition and logic, distinct from the final artifact. Do not transfer Nano Banana 2’s minimal/high control table or Nano Banana 2.1’s medium-default table to this Pro edition. For a demanding design, give a complete brief with the visual hierarchy, required object relationships and the elements that must survive later edits.
The model card describes substantive remaining limits: very small text and long passages may render poorly, character appearance can change between a reference and a result, and masked or doodle edits may only partly follow the instruction or leave marks. It can also confuse left and right or copy a source element into the new composition. For a product revision, explicitly state the preserved geometry and labels and inspect the edited area; for a diagram, compare the direction of arrows and relative object positions.
The current API table gives an input limit of 65,536 tokens and an output limit of 32,768 tokens. Image generation, thinking, search grounding and batch processing are supported; audio generation, function calling, code execution, caching, structured outputs, file search, URL context and Live API are unsupported. The image task accepts neither audio nor video input in this profile. Generated images include SynthID, and a requested number of output images is not always followed exactly.
A practical product brief can combine an image of the product, a lighting reference and the exact headline: “Preserve the product’s proportions and lettering, show it on a pale stone surface under soft side light, and leave the upper third clear for the supplied heading.” For a factual diagram, identify each entity and connection and provide the approved labels. Refine the result in a conversation by naming one change and the features to preserve, keeping the selected image and its explanatory text together as the design artifact.
POST /v1/images/generationstext, imageimage| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits google/gemini-3-pro-image | Default— |
Fieldprompt | Typestring | RequirementRequired | Values & limits Minimum length: 1 | Default— |
Fieldresolution | Typestring | RequirementOptional | Values & limits 1K, 2K, 4K | Default— |
Fieldaspect_ratio | Typestring | RequirementOptional | Values & limits 1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9 | Default— |
Fieldn | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Maximum: 1 | Default— |
Fieldinput_references | Typearray | RequirementOptional | Values & limits Minimum items: 0 Maximum items: 14 | 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 | Charge |
|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Forecast quantity— | 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 |
| ConfigurationEstimated generation cost | Rate schedule— | Forecast quantityone generation estimate | Charge Output · Estimated generation cost · Estimate for comparison only $0.98304 estimate before generation This estimate is not an additional charge or a fixed rate for the completed generation. |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\nexport AILOOT_API_KEY\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np={'model': 'google/gemini-3-pro-image', '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 gemini-3-pro-image, gemini-3-pro-image-preview, nano-banana-pro
Ailoot Ultra google/gemini-3-pro-image
/v1/images
google/gemini-3-pro-image, gemini-3-pro-image-preview, nano-banana-pro, gemini-3-pro-image
google/gemini-3-pro-imageNano Banana Pro (Gemini 3 Pro Image) is Google’s reasoning-driven image model for complex design, product mockups and factual visualisations. It accepts text and image references and generates images with text, with 1K–4K output and detailed creative control.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Nano Banana Pro is Google’s Gemini 3 Pro Image model for image generation and editing driven by reasoning. The manufacturer places complex graphic design, high-fidelity product mockups and factual data visualisation at the centre of its intended use. It accepts text and images and produces image and text. This named Pro image edition is based on Gemini 3 Pro, while retaining its own image-task input and capability profile.
The model supports 1K, 2K and 4K images. Documented shapes include 1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9 and 21:9. A square 4K image is 4096 × 4096; a 16:9 4K image is 5504 × 3072. Select resolution and aspect ratio for the artifact you need, whether a portrait poster, product scene or wide explanatory graphic. The 0.5K option and additional extreme ratios of Gemini 3.1 Flash Image are not part of this Pro table.
Text rendering supports clear lettering in posters, diagrams and other designed assets. For a complex infographic, provide the underlying facts, exact wording, a reading sequence and the relation between labels and visual elements. Separate the information to communicate from the desired artistic style. Google recommends preparing the text first and then generating the image containing it. This is particularly useful when a composition combines a headline, a diagram and several short captions.
Multi-image input supports up to 14 references. Google’s feature table describes up to six object references for fidelity, up to five character references for consistency and up to three style references. These roles help prepare a composite visual: a product photograph defines appearance, a separate picture establishes setting and a style image informs treatment. Tell the model which details are authoritative and which references provide inspiration, rather than asking it to combine every visible feature indiscriminately.
Google Search grounding can supply real-world information for a factual visualisation or a scene informed by current conditions. The Pro image workflow documents web grounding; the Image Search extension is documented specifically for Nano Banana 2.1 and Gemini 3.1 Flash Image. For a data graphic, supply the values you intend to show or request grounded information in the producer workflow, then make the relationship, labels and layout explicit. Search-informed generation still produces an image to review, not a substitute for checking the depicted facts.
Reasoning supports the composition of a complex prompt before final rendering. The image guide describes interim images used to test composition and logic, distinct from the final artifact. Do not transfer Nano Banana 2’s minimal/high control table or Nano Banana 2.1’s medium-default table to this Pro edition. For a demanding design, give a complete brief with the visual hierarchy, required object relationships and the elements that must survive later edits.
The model card describes substantive remaining limits: very small text and long passages may render poorly, character appearance can change between a reference and a result, and masked or doodle edits may only partly follow the instruction or leave marks. It can also confuse left and right or copy a source element into the new composition. For a product revision, explicitly state the preserved geometry and labels and inspect the edited area; for a diagram, compare the direction of arrows and relative object positions.
The current API table gives an input limit of 65,536 tokens and an output limit of 32,768 tokens. Image generation, thinking, search grounding and batch processing are supported; audio generation, function calling, code execution, caching, structured outputs, file search, URL context and Live API are unsupported. The image task accepts neither audio nor video input in this profile. Generated images include SynthID, and a requested number of output images is not always followed exactly.
A practical product brief can combine an image of the product, a lighting reference and the exact headline: “Preserve the product’s proportions and lettering, show it on a pale stone surface under soft side light, and leave the upper third clear for the supplied heading.” For a factual diagram, identify each entity and connection and provide the approved labels. Refine the result in a conversation by naming one change and the features to preserve, keeping the selected image and its explanatory text together as the design artifact.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits google/gemini-3-pro-image | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits user, assistant | Default— |
Fieldmessages[].content | Typestring | RequirementRequired | Values & limits | Default— |
Fieldmax_tokens | Typeinteger | RequirementRequired | Values & limits | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Text · UTF-8 text
messages[].content string
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, messages, max_tokens |
Parameteroptional_controls | Contractstream |
Parametermaterial_types | Contracttext |
Parametertext_file_material | ContractRead the actual UTF-8 document and put its text in the indicated input or message content string. |
Lifecycle: synchronous_or_streamed
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 | Context tokens, more than | Context tokens, up to and including | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.004 / 1K tokens Output · Output tokens $0.024 / 1K tokens Input · Cache read $0.0004 / 1K tokens Input · Cache write $0.00075 / 1K tokens Input · Cache write, 1 hour — / 1K tokens Output · Image output tokens $0.24 / 1K image tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.004 / 1K tokens Output · Output tokens $0.024 / 1K tokens Input · Cache read $0.0004 / 1K tokens Input · Cache write $0.00075 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "google/gemini-3-pro-image",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'google/gemini-3-pro-image', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nPath('request.json').write_text(json.dumps(payload, ensure_ascii=False), encoding='utf-8')\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \\\n -H "x-api-key: $AILOOT_API_KEY" -H 'anthropic-version: 2023-06-01' -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nPY_RESULT\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'google/gemini-3-pro-image', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nfrom urllib.request import Request, urlopen\nfrom urllib.error import HTTPError\n\napi_key = os.environ['AILOOT_API_KEY']\nif not api_key.strip():\n raise SystemExit('Set AILOOT_API_KEY before making a request')\nheaders = {'x-api-key': 'PLACEHOLDER_KEY', 'anthropic-version': '2023-06-01', 'Content-Type': 'application/json'}\nheaders['x-api-key'] = api_key\nrequest = Request('https://api.ailoot.ai/v1/messages', data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, method='POST')\ntry:\n with urlopen(request, timeout=120) as response:\n raw = response.read()\n Path('response-headers.txt').write_text(str(response.headers), encoding='utf-8')\nexcept HTTPError as error:\n Path('result.json').write_bytes(error.read())\n Path('response-headers.txt').write_text(str(error.headers), encoding='utf-8')\n raise\nPath('result.json').write_bytes(raw)\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nJSON or SSE
Keep every complete returned response, file and content part.
Keep all content blocks, thinking signatures and actual tool-use IDs. Assemble all SSE block deltas; a first text block is not the complete output.
Keep request/response IDs and headers. No generic text job retrieval URL is supplied.
| Field | Type |
|---|---|
Fieldid | Typestring |
Fieldmodel | Typestring |
Fieldcontent[] | Typetext|thinking|tool_use|other content block |
Fieldstop_reason | Typestring|null |
Fieldusage | Typeobject |
| Response | Condition | Next step |
|---|---|---|
Response400 · invalid_request_error | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. |
Ailoot gemini-3-pro-image, gemini-3-pro-image-preview, nano-banana-pro
Ailoot Ultra google/gemini-3-pro-image
/v1/images
google/gemini-3-pro-image, gemini-3-pro-image-preview, nano-banana-pro, gemini-3-pro-image
Technical example in English
Visualize a supplied packaging cycle\n\nCreate one landscape explanatory graphic showing this packaging return cycle: customer returns an empty jar, store inspects it, accepted jars are washed, clean jars are refilled. Use four large illustrated stages connected clockwise. Add only these exact labels: 'RETURN', 'INSPECT', 'WASH', 'REFILL'. Make the arrow direction and stage order unmistakable.One landscape image with four labeled stages and arrows matching the supplied cycle.Inspect that arrows connect RETURN to INSPECT to WASH to REFILL in that order, with exact labels.