models

Gemini 3.1 Pro

Markdown · JSON

Gemini 3.1 Pro

A multimodal Google model for complex coding tasks, analysis of large bodies of material and precise multi-step tool use.

About the model

This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.

Gemini 3.1 Pro Preview is intended for complex material and tasks requiring several sequential decisions. Google highlights software engineering, tool use and consistency of factual answers. The model helps analyze large datasets, entire software repositories and tasks combining text with visual or audio material.

Material the model understands

Google's specifications list text, images, video, audio and PDF. In Ailoot API's current catalogue, text methods declare text and images as input and text as output. Video, audio and PDF are not included in this input set. Long context holds up to 1,048,576 input tokens, and a text response up to 65,536 output tokens. Understanding images and audio does not mean generating them: this version's output remains text.

Reasoning and working step by step

Thinking allows additional effort to be devoted to internal reasoning. In Google's described base-model mode, the default level is high, with low, medium and high available. These values describe Google's base model; the mode and availability of control through Ailoot API depend on the selected method and model variant. Main uses include complex coding, algorithm development, strategic planning and iterative improvement of a solution. This is especially useful when several connected requirements need to be considered and intermediate results revisited.

The model's capabilities include function calling, code execution and structured responses. Context caching, search grounding, Google Maps and use of URL contents are supported; batch processing is provided. This set allows a workflow to be built in which the model analyzes data, selects the next tool and forms a final result. Actual availability of each tool depends on the method used to work with the model.

Version and characteristics

The card concerns the gemini-3.1-pro-preview preview version. It is based on Gemini 3 Pro. The manufacturer separately identifies gemini-3.1-pro-preview-customtools, intended for workflows involving a command shell and custom tools. Its tool-selection priorities and behavioral characteristics are not properties of the ordinary Preview version.

The general limitations of the base line remain: responses may contain fabricated information, and some requests may run slowly or time out. Multi-step reasoning, large context and data access help with complex tasks, but do not make every generated conclusion a verified fact.

Practical task

Schedule dependent engineering work

Technical example in English

Schedule dependent engineering work

Plan these tasks with two engineers, one task per engineer at a time: A takes 2 days; B takes 3 days after A; C takes 2 days after A; D takes 1 day after both B and C. No task may be split. Give a day-by-day allocation, earliest finish and dependency check.

Expected result

A two-engineer schedule, total elapsed days and a concise check that every prerequisite is satisfied.

Check the result

Neither B nor C starts before A ends, and D waits for both; recompute the claimed finish from the allocation.
Ailoot · Anthropic Messages POST /v1/messages gemini-3.1-pro

gemini-3.1-pro

Google /gemini-3.1-pro

Overview

A multimodal Google model for complex coding tasks, analysis of large bodies of material and precise multi-step tool use.

Accepted inputs
Text · Images
Results
Text

Available in Ailoot · inputs & methods

Ailoot · Anthropic Messages POST /v1/messages
Input types
text, image
Output types
text
Context
1,048,576 tokens
Maximum output
65,536 tokens
Output format
json or SSE
FieldTypeRequirementValues & limitsDefault
FieldmodelTypestringRequirementRequiredValues & limits
gemini-3.1-pro
Default—
FieldmessagesTypemessage[]RequirementRequiredValues & limits
Minimum items: 1
Default—
Fieldmessages[].roleTypestringRequirementRequiredValues & limits
user, assistant
Default—
Fieldmessages[].contentTypestring|content_block[]RequirementRequiredValues & limitsDefault—
FieldsystemTypestring|content_block[]RequirementOptionalValues & limitsDefault—
Fieldmax_tokensTypeintegerRequirementRequiredValues & limits
Minimum: 1
Limit applies to: exact selected model/card ceiling
Default—
FieldstreamTypebooleanRequirementOptionalValues & limitsDefaultfalse
FieldtemperatureTypenumberRequirementOptionalValues & limits
Model-specific
Default—
Fieldtop_pTypenumberRequirementOptionalValues & limits
Model-specific
Default—
Fieldstop_sequencesTypestring[]RequirementOptionalValues & limits
Model-specific
Default—
FieldtoolsTypetool[]RequirementOptionalValues & limits
Model-specific
Default—
Fieldtools[].nameTypestringRequirementOptionalValues & limits
Applies when: tool supplied
Default—
Fieldtools[].descriptionTypestringRequirementOptionalValues & limitsDefault—
Fieldtools[].input_schemaTypeJSON_schemaRequirementOptionalValues & limitsDefault—
Fieldtool_choiceTypeobjectRequirementOptionalValues & limits
Model-specific
Default—
Fieldthinking.typeTypestringRequirementOptionalValues & limits
Model-specific
Default—
Fieldthinking.budget_tokensTypeintegerRequirementOptionalValues & limits
Model-specific
Default—
Fieldoutput_config.effortTypestringRequirementOptionalValues & limits
Model-specific
Default—
FieldmetadataTypeobjectRequirementOptionalValues & limitsDefault—
Fieldcache_controlTypeobjectRequirementOptionalValues & limits
Model-specific
Default—
Input material combinations

Text · UTF8 text

messages[].content or type=text block

Text + image

{type:image,source:{type:base64,media_type,data}} or source type=url

Method parameters and lifecycle
ParameterContract
Parametermaterial_typesContracttext, image
ParameterrequiredContractmodel, messages, max_tokens
Parameteroptional_controlsContractnull
Parametercontent_typeContractapplication/json
Parametertext_file_materialContractactual UTF-8 content inserted as text; file IDs/URLs need separate exact support

Lifecycle: exact retained published method

Method shapes and IDs are exact; supplier capability is separate from admission.

Pricing

Ailoot method rates

Ailoot Anthropic MessagesPOST /v1/messages
Input
$0.002 / 1K
Output
$0.012 / 1K
Cache read
$0.0002 / 1K

Prices by request length · Anthropic Messages

Request size, tokensInputOutputCache readCache write5 min cache1 hour cache
Request size, tokens(0; 200,000]Input$0.002Output$0.012Cache read$0.0002Cache writeBase: —5 min cacheBase: —1 hour cacheBase: —
Request size, tokens(200,000; ∞)Input$0.004Output$0.018Cache read$0.0004Cache writeBase: —5 min cacheBase: —1 hour cacheBase: —

Rates in USD per 1K tokens. If no rate is set, the base rate for this method is inherited; «—» means no base rate is set.

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 →

Example requests

Ailoot · Anthropic Messages

Text exampleImage inputPython SDKNode.js SDKPython stream
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 source-material file}"
export AILOOT_TEXT_FILE
python3 - <<'PY_REQUEST'
import json,os
from pathlib import Path
p=json.loads('{"model": "gemini-3.1-pro", "max_tokens": 1024, "messages": []}')
f=Path(os.environ["AILOOT_TEXT_FILE"])
if not f.is_file():raise SystemExit("Choose your actual UTF-8 source material")
text=f.read_text(encoding="utf-8")
if not text.strip():raise SystemExit("Source material must contain text")
p["messages"]=[{"role":"user","content":"Summarize this material and quote supporting source passages.\n\n"+text}]
Path("request.json").write_text(json.dumps(p,ensure_ascii=False))
PY_REQUEST
curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \
  -H "x-api-key: $AILOOT_API_KEY" -H 'anthropic-version: 2023-06-01' -H 'Content-Type: application/json' \
  --data-binary @request.json -D response-headers.txt -o result.json
python3 - <<'PY_RESULT'
import json
from pathlib import Path
print(json.dumps(json.loads(Path("result.json").read_text()),ensure_ascii=False,indent=2))
PY_RESULT
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
: "${AILOOT_IMAGE_FILE:?Set your actual PNG JPEG or WebP image}"
export AILOOT_IMAGE_FILE
python3 - <<'PY_REQUEST'
import json,os
from pathlib import Path
p=json.loads('{"model": "gemini-3.1-pro", "max_tokens": 1024, "messages": [{"role": "user", "content": [{"type": "text", "text": "Read labels and numbers in this image; flag unreadable text."}]}]}')
import base64
f=Path(os.environ["AILOOT_IMAGE_FILE"])
if not f.is_file():raise SystemExit("Choose an actual PNG JPEG or WebP image")
raw=f.read_bytes()
mime="image/png" if raw.startswith(b"\x89PNG\r\n\x1a\n") else "image/jpeg" if raw.startswith(b"\xff\xd8") else "image/webp" if raw[:4]==b"RIFF" and raw[8:12]==b"WEBP" else None
if not mime:raise SystemExit("Use PNG JPEG or WebP")
b64=base64.b64encode(raw).decode()
p["messages"][0]["content"].append({"type":"image","source":{"type":"base64","media_type":mime,"data":b64}})
Path("request.json").write_text(json.dumps(p,ensure_ascii=False))
PY_REQUEST
curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \
  -H "x-api-key: $AILOOT_API_KEY" -H 'anthropic-version: 2023-06-01' -H 'Content-Type: application/json' \
  --data-binary @request.json -D response-headers.txt -o result.json
python3 - <<'PY_RESULT'
import json
from pathlib import Path
print(json.dumps(json.loads(Path("result.json").read_text()),ensure_ascii=False,indent=2))
PY_RESULT
import anthropic
import os

if not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")
client = anthropic.Anthropic(base_url="https://api.ailoot.ai", api_key=os.environ["AILOOT_API_KEY"], max_retries=0)
msg = client.messages.create(
    model="gemini-3.1-pro",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Explain the difference between an observation and a hypothesis in three sentences."}],
)
import base64, json
from pathlib import Path

def save_result(data, stem="result"):
    # Keep every field, including usage, IDs, reasoning and tool requests.
    Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
    print(json.dumps(data, ensure_ascii=False, indent=2))
    def save_blob(encoded, suffix, label):
        raw = base64.b64decode(encoded, altchars=b"-_", validate=True)
        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
        file = Path(stem + "-" + label + "." + ext)
        file.write_bytes(raw)
        print("Saved", file)
    for index, block in enumerate(data.get("content") or []):
        print("Content", index, json.dumps(block, ensure_ascii=False))
    for choice in data.get("choices") or []:
        print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))
    for index, item in enumerate(data.get("output") or []):
        print("Output", index, json.dumps(item, ensure_ascii=False))
        if item.get("type") == "image_generation_call" and item.get("result"):
            save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))
    for ci, candidate in enumerate(data.get("candidates") or []):
        for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):
            print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))
            blob = part.get("inlineData") or part.get("inline_data")
            if blob and blob.get("data"):
                mime = blob.get("mimeType") or blob.get("mime_type")
                suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")
                save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))

save_result(msg.to_dict(mode="json"))
import Anthropic from "@anthropic-ai/sdk";

if (!process.env.AILOOT_API_KEY?.trim()) throw new Error("Set AILOOT_API_KEY before making a request");
const client = new Anthropic({ baseURL: "https://api.ailoot.ai", apiKey: process.env.AILOOT_API_KEY, maxRetries: 0 });
const msg = await client.messages.create({
  model: "gemini-3.1-pro",
  max_tokens: 1024,
  messages: [{ role: "user", content: "Explain the difference between an observation and a hypothesis in three sentences." }],
});
import { writeFileSync } from "node:fs";

function saveResult(data, stem = "result") {
  writeFileSync(stem + ".json", JSON.stringify(data, null, 2));
  console.log(JSON.stringify(data, null, 2));
  const saveBlob = (encoded, suffix, label) => {
    const raw = Buffer.from(encoded, "base64");
    const ext = raw.subarray(0,8).equals(Buffer.from([137,80,78,71,13,10,26,10])) ? "png" : raw[0]===255 && raw[1]===216 ? "jpg" : raw.subarray(0,4).toString()==="RIFF" && raw.subarray(8,12).toString()==="WEBP" ? "webp" : suffix;
    const file = `${stem}-${label}.${ext}`;
    writeFileSync(file, raw); console.log("Saved", file);
  };
  for (const [i, block] of (data.content || []).entries()) console.log("Content", i, JSON.stringify(block));
  for (const choice of data.choices || []) console.log("Choice", choice.index, JSON.stringify(choice.message || choice.delta || {}));
  for (const [i, item] of (data.output || []).entries()) {
    console.log("Output", i, JSON.stringify(item));
    if (item.type === "image_generation_call" && item.result) saveBlob(item.result, item.output_format || "bin", `output-${i}`);
  }
  for (const [ci, candidate] of (data.candidates || []).entries()) {
    for (const [pi, part] of (candidate.content?.parts || []).entries()) {
      console.log("Candidate/part", ci, pi, JSON.stringify(part));
      const blob = part.inlineData || part.inline_data;
      if (blob?.data) {
        const mime = blob.mimeType || blob.mime_type;
        const suffix = ({"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"})[mime] || "bin";
        saveBlob(blob.data, suffix, `candidate-${ci}-part-${pi}`);
      }
    }
  }
}

saveResult(msg);
import anthropic,os
from pathlib import Path

if not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")
client=anthropic.Anthropic(base_url="https://api.ailoot.ai",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)
import base64, json
from pathlib import Path

def save_result(data, stem="result"):
    # Keep every field, including usage, IDs, reasoning and tool requests.
    Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
    print(json.dumps(data, ensure_ascii=False, indent=2))
    def save_blob(encoded, suffix, label):
        raw = base64.b64decode(encoded, altchars=b"-_", validate=True)
        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
        file = Path(stem + "-" + label + "." + ext)
        file.write_bytes(raw)
        print("Saved", file)
    for index, block in enumerate(data.get("content") or []):
        print("Content", index, json.dumps(block, ensure_ascii=False))
    for choice in data.get("choices") or []:
        print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))
    for index, item in enumerate(data.get("output") or []):
        print("Output", index, json.dumps(item, ensure_ascii=False))
        if item.get("type") == "image_generation_call" and item.get("result"):
            save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))
    for ci, candidate in enumerate(data.get("candidates") or []):
        for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):
            print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))
            blob = part.get("inlineData") or part.get("inline_data")
            if blob and blob.get("data"):
                mime = blob.get("mimeType") or blob.get("mime_type")
                suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")
                save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))

with client.messages.stream(model='gemini-3.1-pro',max_tokens=1024,messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}]) as stream:
    with Path("stream-events.jsonl").open("w",encoding="utf-8") as log:
        for event in stream:
            payload=event.to_dict(mode="json")
            log.write(json.dumps(payload,ensure_ascii=False)+"\n"); log.flush()
            print(json.dumps(payload,ensure_ascii=False))
    final=stream.get_final_message()
    save_result(final.to_dict(mode="json"),"stream-result")

Receive and save results

Ailoot · Anthropic Messages

json or SSE

Keep all completed results

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.

If the original response needs checking

Keep request/response IDs and headers. No generic text job retrieval URL is supplied.

Response fields
FieldType
FieldidTypestring
FieldmodelTypestring
Fieldcontent[]Typetext|thinking|tool_use|other content block
Fieldstop_reasonTypestring|null
FieldusageTypeobject
Errors and next steps
ResponseConditionNext step
Response400 · invalid_request_errorConditionInvalid required fields, material representation or parameter valueNext stepCorrect the indicated fields before a new creation request.

Aliases

Model aliases

Ailoot gemini-3.1-pro

Method path aliases

Use the method paths shown above.

Related caller IDs

gemini-3.1-pro

Ailoot · Gemini generateContent POST /v1beta/models/{model}:generateContent gemini-3.1-pro

gemini-3.1-pro

Google /gemini-3.1-pro

Overview

A multimodal Google model for complex coding tasks, analysis of large bodies of material and precise multi-step tool use.

Accepted inputs
Text · Images
Results
Text

Available in Ailoot · inputs & methods

Ailoot · Gemini generateContent POST /v1beta/models/{model}:generateContent
Input types
text, image
Output types
text
Context
1,048,576 tokens
Maximum output
65,536 tokens
Output format
json or supported streaming
FieldTypeRequirementValues & limitsDefault
Fieldpath.modelTypestringRequirementRequiredValues & limits
gemini-3.1-pro
Default—
FieldcontentsTypecontent[]RequirementRequiredValues & limits
Minimum items: 1
Default—
Fieldcontents[].roleTypestringRequirementOptionalValues & limits
user, model
Default—
Fieldcontents[].partsTypepart[]RequirementRequiredValues & limitsDefault—
FieldsystemInstructionTypecontentRequirementOptionalValues & limitsDefault—
FieldgenerationConfig.maxOutputTokensTypeintegerRequirementOptionalValues & limits
Minimum: 1
Model-specific
Default—
FieldgenerationConfig.temperatureTypenumberRequirementOptionalValues & limits
Model-specific
Default—
FieldgenerationConfig.topPTypenumberRequirementOptionalValues & limits
Model-specific
Default—
FieldgenerationConfig.topKTypeintegerRequirementOptionalValues & limits
Model-specific
Default—
FieldgenerationConfig.stopSequencesTypestring[]RequirementOptionalValues & limits
Model-specific
Default—
FieldgenerationConfig.thinkingConfig.includeThoughtsTypebooleanRequirementOptionalValues & limits
Model-specific
Default—
FieldgenerationConfig.thinkingConfig.thinkingBudgetTypeintegerRequirementOptionalValues & limits
Model-specific
Default—
Fieldtools[].functionDeclarationsTypefunction_declaration[]RequirementOptionalValues & limits
Model-specific
Default—
FieldtoolConfig.functionCallingConfig.modeTypestringRequirementOptionalValues & limits
Model-specific
Default—
FieldsafetySettingsTypeobject[]RequirementOptionalValues & limits
Model-specific
Default—
FieldsessionIdTypestringRequirementOptionalValues & limitsDefault—
Input material combinations

Text · UTF8 text

contents[].parts[].text

Text + image

parts[].inlineData={mimeType,data}

Method parameters and lifecycle
ParameterContract
Parametermaterial_typesContracttext, image
ParameterrequiredContractcontents
Parameteroptional_controlsContractnull
Parametercontent_typeContractapplication/json
Parametertext_file_materialContractactual UTF-8 content inserted as text; file IDs/URLs need separate exact support

Lifecycle: exact retained published method

Method shapes and IDs are exact; supplier capability is separate from admission.

Pricing

Ailoot method rates

Ailoot Gemini generateContentPOST /v1beta/models/{model}:generateContent
Input
$0.002 / 1K
Output
$0.012 / 1K
Cache read
$0.0002 / 1K

Prices by request length · Gemini generateContent

Request size, tokensInputOutputCache readCache write5 min cache1 hour cache
Request size, tokens(0; 200,000]Input$0.002Output$0.012Cache read$0.0002Cache writeBase: —5 min cacheBase: —1 hour cacheBase: —
Request size, tokens(200,000; ∞)Input$0.004Output$0.018Cache read$0.0004Cache writeBase: —5 min cacheBase: —1 hour cacheBase: —

Rates in USD per 1K tokens. If no rate is set, the base rate for this method is inherited; «—» means no base rate is set.

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 →

Example requests

Ailoot · Gemini generateContent

Text exampleImage inputPython SDKNode.js SDKPython stream
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 source-material file}"
export AILOOT_TEXT_FILE
python3 - <<'PY_REQUEST'
import json,os
from pathlib import Path
p=json.loads('{"contents": []}')
f=Path(os.environ["AILOOT_TEXT_FILE"])
if not f.is_file():raise SystemExit("Choose your actual UTF-8 source material")
text=f.read_text(encoding="utf-8")
if not text.strip():raise SystemExit("Source material must contain text")
p["contents"]=[{"role":"user","parts":[{"text":"Summarize this material and quote supporting source passages.\n\n"+text}]}]
Path("request.json").write_text(json.dumps(p,ensure_ascii=False))
PY_REQUEST
curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1beta/models/gemini-3.1-pro:generateContent \
  -H "x-goog-api-key: $AILOOT_API_KEY" -H 'Content-Type: application/json' \
  --data-binary @request.json -D response-headers.txt -o result.json
python3 - <<'PY_RESULT'
import base64, json
from pathlib import Path

def save_result(data, stem="result"):
    # Keep every field, including usage, IDs, reasoning and tool requests.
    Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
    print(json.dumps(data, ensure_ascii=False, indent=2))
    def save_blob(encoded, suffix, label):
        raw = base64.b64decode(encoded, altchars=b"-_", validate=True)
        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
        file = Path(stem + "-" + label + "." + ext)
        file.write_bytes(raw)
        print("Saved", file)
    for index, block in enumerate(data.get("content") or []):
        print("Content", index, json.dumps(block, ensure_ascii=False))
    for choice in data.get("choices") or []:
        print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))
    for index, item in enumerate(data.get("output") or []):
        print("Output", index, json.dumps(item, ensure_ascii=False))
        if item.get("type") == "image_generation_call" and item.get("result"):
            save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))
    for ci, candidate in enumerate(data.get("candidates") or []):
        for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):
            print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))
            blob = part.get("inlineData") or part.get("inline_data")
            if blob and blob.get("data"):
                mime = blob.get("mimeType") or blob.get("mime_type")
                suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")
                save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))

save_result(json.loads(Path("result.json").read_text(encoding="utf-8")))
PY_RESULT
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
: "${AILOOT_IMAGE_FILE:?Set your actual PNG JPEG or WebP image}"
export AILOOT_IMAGE_FILE
python3 - <<'PY_REQUEST'
import json,os
from pathlib import Path
p=json.loads('{"contents": [{"role": "user", "parts": [{"text": "Read labels and numbers in this image; flag unreadable text."}]}]}')
import base64
f=Path(os.environ["AILOOT_IMAGE_FILE"])
if not f.is_file():raise SystemExit("Choose an actual PNG JPEG or WebP image")
raw=f.read_bytes()
mime="image/png" if raw.startswith(b"\x89PNG\r\n\x1a\n") else "image/jpeg" if raw.startswith(b"\xff\xd8") else "image/webp" if raw[:4]==b"RIFF" and raw[8:12]==b"WEBP" else None
if not mime:raise SystemExit("Use PNG JPEG or WebP")
b64=base64.b64encode(raw).decode()
p["contents"][0]["parts"].append({"inlineData":{"mimeType":mime,"data":b64}})
Path("request.json").write_text(json.dumps(p,ensure_ascii=False))
PY_REQUEST
curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1beta/models/gemini-3.1-pro:generateContent \
  -H "x-goog-api-key: $AILOOT_API_KEY" -H 'Content-Type: application/json' \
  --data-binary @request.json -D response-headers.txt -o result.json
python3 - <<'PY_RESULT'
import base64, json
from pathlib import Path

def save_result(data, stem="result"):
    # Keep every field, including usage, IDs, reasoning and tool requests.
    Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
    print(json.dumps(data, ensure_ascii=False, indent=2))
    def save_blob(encoded, suffix, label):
        raw = base64.b64decode(encoded, altchars=b"-_", validate=True)
        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
        file = Path(stem + "-" + label + "." + ext)
        file.write_bytes(raw)
        print("Saved", file)
    for index, block in enumerate(data.get("content") or []):
        print("Content", index, json.dumps(block, ensure_ascii=False))
    for choice in data.get("choices") or []:
        print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))
    for index, item in enumerate(data.get("output") or []):
        print("Output", index, json.dumps(item, ensure_ascii=False))
        if item.get("type") == "image_generation_call" and item.get("result"):
            save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))
    for ci, candidate in enumerate(data.get("candidates") or []):
        for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):
            print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))
            blob = part.get("inlineData") or part.get("inline_data")
            if blob and blob.get("data"):
                mime = blob.get("mimeType") or blob.get("mime_type")
                suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")
                save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))

save_result(json.loads(Path("result.json").read_text(encoding="utf-8")))
PY_RESULT
from google import genai
from google.genai import types
import os

if not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")
client = genai.Client(
    api_key=os.environ["AILOOT_API_KEY"],
    http_options=types.HttpOptions(base_url="https://api.ailoot.ai"),
)
resp = client.models.generate_content(model="gemini-3.1-pro", contents="Explain the difference between an observation and a hypothesis in three sentences.")
import base64, json
from pathlib import Path

def save_result(data, stem="result"):
    # Keep every field, including usage, IDs, reasoning and tool requests.
    Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
    print(json.dumps(data, ensure_ascii=False, indent=2))
    def save_blob(encoded, suffix, label):
        raw = base64.b64decode(encoded, altchars=b"-_", validate=True)
        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
        file = Path(stem + "-" + label + "." + ext)
        file.write_bytes(raw)
        print("Saved", file)
    for index, block in enumerate(data.get("content") or []):
        print("Content", index, json.dumps(block, ensure_ascii=False))
    for choice in data.get("choices") or []:
        print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))
    for index, item in enumerate(data.get("output") or []):
        print("Output", index, json.dumps(item, ensure_ascii=False))
        if item.get("type") == "image_generation_call" and item.get("result"):
            save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))
    for ci, candidate in enumerate(data.get("candidates") or []):
        for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):
            print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))
            blob = part.get("inlineData") or part.get("inline_data")
            if blob and blob.get("data"):
                mime = blob.get("mimeType") or blob.get("mime_type")
                suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")
                save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))

save_result(resp.to_json_dict())
import { GoogleGenAI } from "@google/genai";

if (!process.env.AILOOT_API_KEY?.trim()) throw new Error("Set AILOOT_API_KEY before making a request");
const ai = new GoogleGenAI({ apiKey: process.env.AILOOT_API_KEY, httpOptions: { baseUrl: "https://api.ailoot.ai" } });
const resp = await ai.models.generateContent({ model: "gemini-3.1-pro", contents: "Explain the difference between an observation and a hypothesis in three sentences." });
import { writeFileSync } from "node:fs";

function saveResult(data, stem = "result") {
  writeFileSync(stem + ".json", JSON.stringify(data, null, 2));
  console.log(JSON.stringify(data, null, 2));
  const saveBlob = (encoded, suffix, label) => {
    const raw = Buffer.from(encoded, "base64");
    const ext = raw.subarray(0,8).equals(Buffer.from([137,80,78,71,13,10,26,10])) ? "png" : raw[0]===255 && raw[1]===216 ? "jpg" : raw.subarray(0,4).toString()==="RIFF" && raw.subarray(8,12).toString()==="WEBP" ? "webp" : suffix;
    const file = `${stem}-${label}.${ext}`;
    writeFileSync(file, raw); console.log("Saved", file);
  };
  for (const [i, block] of (data.content || []).entries()) console.log("Content", i, JSON.stringify(block));
  for (const choice of data.choices || []) console.log("Choice", choice.index, JSON.stringify(choice.message || choice.delta || {}));
  for (const [i, item] of (data.output || []).entries()) {
    console.log("Output", i, JSON.stringify(item));
    if (item.type === "image_generation_call" && item.result) saveBlob(item.result, item.output_format || "bin", `output-${i}`);
  }
  for (const [ci, candidate] of (data.candidates || []).entries()) {
    for (const [pi, part] of (candidate.content?.parts || []).entries()) {
      console.log("Candidate/part", ci, pi, JSON.stringify(part));
      const blob = part.inlineData || part.inline_data;
      if (blob?.data) {
        const mime = blob.mimeType || blob.mime_type;
        const suffix = ({"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"})[mime] || "bin";
        saveBlob(blob.data, suffix, `candidate-${ci}-part-${pi}`);
      }
    }
  }
}

saveResult(resp);
from google import genai
from google.genai import types
import os
from pathlib import Path

if not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")
client=genai.Client(api_key=os.environ["AILOOT_API_KEY"],http_options=types.HttpOptions(base_url="https://api.ailoot.ai"))
import base64, json
from pathlib import Path

def save_result(data, stem="result"):
    # Keep every field, including usage, IDs, reasoning and tool requests.
    Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
    print(json.dumps(data, ensure_ascii=False, indent=2))
    def save_blob(encoded, suffix, label):
        raw = base64.b64decode(encoded, altchars=b"-_", validate=True)
        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
        file = Path(stem + "-" + label + "." + ext)
        file.write_bytes(raw)
        print("Saved", file)
    for index, block in enumerate(data.get("content") or []):
        print("Content", index, json.dumps(block, ensure_ascii=False))
    for choice in data.get("choices") or []:
        print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))
    for index, item in enumerate(data.get("output") or []):
        print("Output", index, json.dumps(item, ensure_ascii=False))
        if item.get("type") == "image_generation_call" and item.get("result"):
            save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))
    for ci, candidate in enumerate(data.get("candidates") or []):
        for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):
            print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))
            blob = part.get("inlineData") or part.get("inline_data")
            if blob and blob.get("data"):
                mime = blob.get("mimeType") or blob.get("mime_type")
                suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")
                save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))

count=0
with Path("stream-events.jsonl").open("w",encoding="utf-8") as log:
    for chunk in client.models.generate_content_stream(model='gemini-3.1-pro',contents="Explain observations and hypotheses in three sentences."):
        payload=chunk.to_json_dict()
        log.write(json.dumps(payload,ensure_ascii=False)+"\n"); log.flush()
        save_result(payload,"stream-chunk-"+str(count))
        count+=1

Receive and save results

Ailoot · Gemini generateContent

json or supported streaming

Keep all completed results

Keep every complete returned response, file and content part.

Keep all candidates and parts, including function calls, thought signatures and usage; assemble stream chunks in order.

If the original response needs checking

Keep request/response IDs and headers. No generic text job retrieval URL is supplied.

Response fields
FieldType
FieldresponseIdTypestring
FieldmodelVersionTypestring
Fieldcandidates[].content.parts[]Typetext|inlineData|functionCall|other part
Fieldcandidates[].finishReasonTypestring
FieldusageMetadataTypeobject
Errors and next steps
ResponseConditionNext step
Response400 · invalid_request_errorConditionInvalid required fields, material representation or parameter valueNext stepCorrect the indicated fields before a new creation request.

Aliases

Model aliases

Ailoot gemini-3.1-pro

Method path aliases

Use the method paths shown above.

Related caller IDs

gemini-3.1-pro

FAQ · Gemini 3.1 Pro
What inputs and results are available?

POST /v1/messages · gemini-3.1-pro: text, image → text

POST /v1beta/models/{model}:generateContent · gemini-3.1-pro: text, image → text

Which fields does the request use?

POST /v1/messages · gemini-3.1-pro: model, messages, system, max_tokens, stream, metadata

POST /v1beta/models/{model}:generateContent · gemini-3.1-pro: contents, systemInstruction, sessionId

What limits apply?

POST /v1/messages · gemini-3.1-pro: ctx: 1048576; max_out: 65536; messages.min_items: 1; max_tokens.minimum: 1

POST /v1beta/models/{model}:generateContent · gemini-3.1-pro: ctx: 1048576; max_out: 65536; contents.min_items: 1

More details

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