Google: Gemini 3.1 Pro Preview (batch)

Gemini 3.1 Pro Preview is Google’s multimodal reasoning model for complex software, algorithm and knowledge tasks. It accepts text, images, audio, video and PDF and returns text or code, with tools and structured output. Low, medium and high reasoning levels guide effort; high is the documented default.

Ailoot Ultra · Batch POST /v1/batches google/gemini-3.1-pro-preview:batch

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 Google’s reasoning-oriented text model for complex tasks that need software engineering, algorithmic development or synthesis of multimodal evidence. It builds on Gemini 3 Pro, emphasizing precise tool use, more efficient reasoning and reliable multi-step execution. Text, images, audio, video and PDF can contribute to a written answer, a code artifact or a structured result.

For a demanding coding assignment, provide the goal, relevant repository material, observed behaviour and the constraints to retain. Entire code repositories can supply context for the assignment. For a code-contract review, ask the model to compare two modules’ handling of a shared data contract, identify the mismatch and propose a focused correction with supporting code references. The useful output distinguishes the analysis, proposed revision and evidence from any validation performed by the connected application.

The launch material demonstrates code-based visual explanations and interactive projects, including animated SVG output from a text brief. These are code artifacts rather than raster-image generation. A clear brief can define what should be explained, how the visual should behave and the data it should represent. Review the resulting code and rendering against those requirements. For a data-synthesis task, supply the actual sources and ask for one view that preserves the relationships and qualifications found in them.

The documented thinking levels are low, medium and high, with high as the default for 3.1 Pro Preview. Thinking cannot be fully disabled in this Pro profile. Choose lower effort for a bounded answer and more reasoning for difficult multi-step work, while leaving sufficient output allowance for the final artifact. A small output cap can interrupt reasoning before a usable final answer appears. The thinking guide separates a summary of reasoning from the full internal process and explains why the conversation’s thought-signature context matters.

The exact Preview table lists function calling, code execution, search grounding, Maps grounding, URL context and structured outputs. It scopes File Search to AI Studio. Provide function purposes and parameter definitions and continue from the real returned result. The separate Custom Tools Preview profile is optimized to prioritize custom tools and bash; it is a distinct named producer endpoint, so that specialization should not be silently treated as a property of every 3.1 Pro request. Select a structured result schema when a task needs defined records instead of unrestricted prose.

For multimodal analysis, identify the relevant page, visual, recording interval or video event. For document analysis, compare a diagram with its written specification and return each discrepancy with its supporting passage. A spoken source can contribute a transcript or an interpretation in text. Preserve source references for the conclusions that need checking, and separate what is visible or stated from what is inferred. This profile’s output remains text, including code; native image or speech generation is not part of its exact capability table.

The native 3.1 Pro card specifies up to 1M context and 64K text output, and the exact Preview API table gives 1,048,576 input tokens and 65,536 output tokens. Include instructions, conversation history and media in the input budget. Caching and batch processing are documented, with Flex and Priority options also listed for the producer profile. For several independent items, define the required output and retain each source identifier rather than combining unrelated evidence into one untraceable answer.

The named Preview edition remains distinct from Pro’s Custom Tools profile and subsequent Flash or Pro releases. Its strengths concern complex reasoning, coding and evidence synthesis; the final artifact still needs checking against the source and the actual tool results. Retain unresolved ambiguity when the supplied information does not support a definite conclusion. A useful deliverable is the supported answer, code or record the task requested, with enough evidence to assess its practical limits.

Ailoot Ultra · Batch POST /v1/batches google/gemini-3.1-pro-preview:batch

google/gemini-3.1-pro-preview:batch

Google /google/gemini-3.1-pro-preview:batch

Overview

Accepted inputs: Text
Results: Text

Available in Ailoot · inputs & methods

Ailoot Ultra · Batch POST /v1/batches

Input types: text
Output types: text
Output format: inline results[].response.body JSON

Field	Type	Requirement	Values & limits	Default
Field`endpoint`	Type`string`	RequirementRequired	Values & limits /v1/chat/completions	Default—
Field`model`	Type`string`	RequirementRequired	Values & limits google/gemini-3.1-pro-preview:batch	Default—
Field`requests`	Type`batch_item[]`	RequirementRequired	Values & limits Minimum items: 1	Default—
Field`requests[].custom_id`	Type`string`	RequirementRequired	Values & limits	Default—
Field`requests[].body.messages`	Type`message[]`	RequirementRequired	Values & limits	Default—
Field`completion_window`	Type`string`	RequirementOptional	Values & limits 24h	Default—
Field`provider`	Type`object\|null`	RequirementOptional	Values & limits	Default—
Field`requests[].body.model`	Type`string`	RequirementOptional	Values & limits google/gemini-3.1-pro-preview:batch, google/gemini-3.1-pro-preview	Default—
Field`provider.only`	Type`string[]`	RequirementOptional	Values & limits google-vertex/global	Default—
Field`provider.allow_fallbacks`	Type`boolean\|null`	RequirementOptional	Values & limits false, null	Default—

Method parameters and lifecycle

Parameter	Contract
Parameter`content_type`	Contractapplication/json
Parameter`required`	Contractendpoint, model, requests
Parameter`material_types`	Contracttext
Parameter`representation`	Contractinline requests[]; each item has custom_id and body
Parameter`request_endpoint_profile`	Contract/v1/chat/completions
Parameter`file_material`	ContractCaller may read actual UTF-8 JSONL locally into requests[]. No upload purpose, input_file_id or file reference is used. Missing/empty customer material stops preparation before POST.
Parameter`serialization_order`	Contractendpoint, model, provider (if supplied), completion_window (if supplied), requests last
Parameter`optional_controls`	Contractprovider.only, provider.allow_fallbacks, completion_window
Parameter`schema_scope`	ContractText Chat Completions bodies in the documented inline Batch envelope.

Lifecycle: asynchronous

GET /v1/batches/{operation_id} · actual original create response.id; same user/key/group

Pricing

Tariff references do not add request options. Supported parameters are listed for the selected method.

Fast mode: Not specified When fast mode is selected
Flex mode: Not specified When flex mode is selected
Maximum reasoning effort: Not specified When maximum reasoning effort uses the default adjustment
Explicit cache pricing: Not enabled When explicit cache is requested

Ailoot rates

Prices by configuration

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 $0.002 / 1K tokens Output · Output tokens $0.012 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens
ConfigurationRequest-length tier 1	Rate schedule—	Context tokens, more than0	Context tokens, up to and including200,000	Charge Input · Input tokens $0.002 / 1K tokens Output · Output tokens $0.012 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens
ConfigurationRequest-length tier 2	Rate schedule—	Context tokens, more than200,000	Context tokens, up to and includingNo upper bound	Charge Input · Input tokens $0.004 / 1K tokens Output · Output tokens $0.018 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 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 →

Example requests

Ailoot Ultra · Batch

curl · retained fixturePython HTTP · actual customer JSONLPython · save every completed result

set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
python3 - <<'PY_REQUEST'
import json
from pathlib import Path
body=json.loads('{"endpoint":"/v1/chat/completions","model":"google/gemini-3.1-pro-preview:batch","requests":[{"custom_id":"req-0001","body":{"messages":[{"role":"user","content":"Hello!"}],"max_tokens":16}}]}')
Path("batch-request.json").write_text(json.dumps(body,ensure_ascii=False),encoding="utf-8")
PY_REQUEST
curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/batches \
  -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \
  --data-binary @batch-request.json -D batch-response-headers.txt -o batch-create-response.json

import json, os
from pathlib import Path
from urllib.request import Request, urlopen

def prepare_requests(customer_path):
    path = Path(customer_path)
    if not path.is_file(): raise ValueError("Customer JSONL file is missing")
    items = [json.loads(line) for line in path.read_text(encoding="utf-8").splitlines() if line.strip()]
    if not items: raise ValueError("Customer JSONL contains no requests")
    ids = set()
    for item in items:
        if set(item) != {"custom_id", "body"}: raise ValueError("Each item needs custom_id and body")
        cid = item["custom_id"]
        if not isinstance(cid, str) or not cid.strip() or cid in ids: raise ValueError("custom_id must be nonempty and unique")
        ids.add(cid)
        if not isinstance(item["body"], dict) or not item["body"].get("messages"): raise ValueError("Supply actual text messages")
        supplied_model = item["body"].get("model")
        if supplied_model is not None and supplied_model not in ['google/gemini-3.1-pro-preview:batch', 'google/gemini-3.1-pro-preview']: raise ValueError("body.model must be an exact documented caller or wire ID")
    return {"endpoint":"/v1/chat/completions", "model":"google/gemini-3.1-pro-preview:batch", "requests":items}

if __name__ == "__main__":
    material = os.environ.get("AILOOT_BATCH_REQUESTS_JSONL", "")
    if not material: raise SystemExit("Set AILOOT_BATCH_REQUESTS_JSONL to your actual customer file")
    body = prepare_requests(material)
    key = os.environ.get("AILOOT_API_KEY", "")
    if not key: raise SystemExit("Set AILOOT_API_KEY")
    request = Request("https://api.ailoot.ai/v1/batches", data=json.dumps(body, ensure_ascii=False).encode("utf-8"), headers={"Authorization":"Bearer " + key, "Content-Type":"application/json"}, method="POST")
    with urlopen(request) as response:
        raw = response.read()
        Path("batch-create-response.json").write_bytes(raw)
        print(raw.decode("utf-8"))

import json
from pathlib import Path
raw = Path("batch-status-response.json").read_bytes()
response = json.loads(raw)
if response.get("status") != "completed":
    raise SystemExit("Keep the original operation; read its status without resubmitting")
results = response.get("results")
if not isinstance(results, list) or not results:
    raise SystemExit("Preserve the original response; completed results are missing")
Path("batch-results.jsonl").write_text("".join(json.dumps(item, ensure_ascii=False) + "\n" for item in results), encoding="utf-8")
# Preserve batch-status-response.json with aggregate usage and every result/error.

Receive and save results

Ailoot Ultra · Batch

inline results[].response.body JSON

GET /v1/batches/{operation_id}

Check job status

curl · original operation status

set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
BATCH_OPERATION_ID="$(python3 - <<'PY_ID'
import json
from pathlib import Path
from urllib.parse import quote
response = json.loads(Path("batch-create-response.json").read_text(encoding="utf-8"))
operation = response.get("id")
if not isinstance(operation, str) or not operation: raise SystemExit("No actual original operation ID; preserve the response and do not resubmit")
print(quote(operation, safe=""))
PY_ID
)"
curl --fail-with-body --silent --show-error "https://api.ailoot.ai/v1/batches/$BATCH_OPERATION_ID" \
  -H "Authorization: Bearer $AILOOT_API_KEY" -o batch-status-response.json

Keep all completed results

Keep every complete returned response, file and content part.

Preserve the full original status JSON, every result/custom_id and complete response or error body. Save completed results[] as JSONL without filtering.

If the original response needs checking

Preserve the original operation ID. 503 result_pending_check has X-Should-Retry:false; never resubmit an unknown original batch.

Response fields

Field	Type
Field`results[].custom_id`	Typestring
Field`results[].response.body.choices[].message.content`	Typestring\|null
Field`results[].error`	Typeobject\|null
Field`usage`	Typeobject\|null
Field`id`	Typestring
Field`model`	Typestring
Field`endpoint`	Typestring
Field`status`	Typestring
Field`request_counts`	Typeobject
Field`results[].id`	Typestring
Field`results[].response.status_code`	Typeinteger
Field`results[].response.request_id`	Typestring

Errors and next steps

Response	Condition	Next step
Response400 · `invalid_request_error`	ConditionInvalid documented inline envelope, duplicate keys, item identity, or configured model capabilities conflict.	Next stepCorrect the indicated fields before any new submission.
Response401 · `authentication_error`	ConditionInvalid API key.	Next stepUse the authorized Ailoot key.
Response403 · `permission_error`	ConditionBatch group unavailable.	Next stepCheck key/group entitlement.
Response422 · `invalid_request_error`	ConditionBatch uses balance billing; subscription creation is rejected.	Next stepUse the authorized balance-billed group.
Response422 · `price_unavailable`	ConditionBatch input cannot be quoted.	Next stepPreserve response; use documented family input.
Response402 · `billing_error`	ConditionBatch reserve unavailable.	Next stepResolve admission before a new request.
Response503 · `price_unavailable`	ConditionBatch method price/native rates unavailable.	Next stepPreserve response; do not substitute prices.
Response503 · `api_error`	ConditionExact supplier route unavailable.	Next stepPreserve response for route repair.
Response503 · `result_pending_check`	ConditionOriginal result requires checking; X-Should-Retry:false.	Next stepKeep original operation_id/id and read its status; never repeat original POST.
Response404 · `not_found`	ConditionBatch status operation absent or owned by another identity.	Next stepUse exact original id and same user/key/group.

Aliases

Model aliases

Ailoot Ultra google/gemini-3.1-pro-preview:batch

Method path aliases

Use the method paths shown above.

Related caller IDs

google/gemini-3.1-pro-preview:batch

FAQ · Google: Gemini 3.1 Pro Preview (batch)

What inputs and results are available?: POST /v1/batches · google/gemini-3.1-pro-preview:batch: text → text
Which fields does the request use?: POST /v1/batches · google/gemini-3.1-pro-preview:batch: endpoint, model, requests, completion_window, provider
What limits apply?: POST /v1/batches · google/gemini-3.1-pro-preview:batch: requests.min_items: 1

Google: Gemini 3.1 Pro Preview (batch)