Google: Gemini 3.5 Flash Lite (batch)

Gemini 3.5 Flash-Lite is Google’s multimodal model for focused subagent tasks, document parsing and repeated data extraction. Its default minimal reasoning suits bounded records; medium or high is documented for multi-step tool work. It returns text or structured results from text, images, audio, video and PDF.

Ailoot Ultra · Batch POST /v1/batches google/gemini-3.5-flash-lite: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 3.5 Flash-Lite is Google’s execution-focused multimodal text profile for repeated data work and focused subagent tasks. It builds on Gemini 3.1 Flash-Lite, improving document understanding, tool reliability and multi-turn instruction following. It accepts text, images, audio, video and PDF and returns text. A useful result is one extracted record, a classification, a short translation or a bounded tool-assisted analysis within a larger task.

For document parsing, define the fields that matter and the evidence each field must retain. Provide the relevant pages and specify how to represent absent or ambiguous information. For support-message extraction, extract a product code, mentioned issue and supporting sentence from a support message, keeping the message identifier in the answer. A document with multiple candidates for a field should not silently collapse them into one guessed value. Structured JSON can make the result usable for later processing when its schema matches the actual task.

The documented thinking levels are minimal, low, medium and high, with minimal as the default. The maker recommends it for high-volume extraction, routing and classification, while medium or high is appropriate for autonomous subagent work involving tool calls, code execution or several reasoning steps. The guide specifically advises increasing effort for those longer tasks to avoid premature tool termination. A one-field extraction and a document comparison with exceptions therefore benefit from different effort choices. Minimal is a supported setting of this edition rather than a general setting to transfer to every Flash model.

A focused subagent brief should contain one job, its source material, permitted tools and the required final form. For example, it can examine a table for a stated rule and return the affected rows with the relevant values. The maker also highlights tabular-data processing and frontend JavaScript work assisted by lightweight code execution. The surrounding application remains responsible for running a requested function and returning the real result. Keep a finished result distinguishable from an unexecuted suggestion or an unresolved input question.

The exact 3.5 Flash-Lite page lists function calling, code execution, file search, search grounding, Maps grounding and URL context, with Computer Use marked as preview. Structured output and thinking are supported. For a multi-turn persona or task, retain the actual instructions and tool observations so later replies follow the same assignment. The developer guide highlights stronger persona persistence than 3.1 Flash-Lite. Use a concise role and output contract rather than redefining the objective at every follow-up.

The guide also deprecates legacy sampling settings and prefilled final model turns for this edition. Control a bounded answer with explicit instructions or a schema rather than trying to start the answer inside a synthetic model-role turn. Preserve the Gemini 3 thought-signature context where the interface requires it, and match function results to their calls. These preparation rules support a continuation that still has the evidence needed to complete the same job.

The native card lists up to 1M context and 64K text output; the exact API page specifies 1,048,576 input tokens and 65,536 output tokens. Instructions, records, history and media all contribute to input preparation. Caching, batch, Flex and Priority options are listed for the producer profile. For repeated requests, use stable identifiers and a compact consistent answer format so each extraction can be compared with its source. Audio, image or video input produces a text interpretation or transcript in this profile, rather than generated speech or artwork.

Possible limitations include hallucinations and occasional delays or timeouts. Its knowledge cutoff is March 2026, with some domains limited to January 2025. Source-dependent extraction should return what the supplied material supports; current-fact questions need current evidence. Compared with 3.1 Flash-Lite, this profile adds the documented execution and computer-use capabilities, while 3.6 Flash emphasizes more complex coding and spatial work. Review extracted fields, tool results and retained instructions before incorporating the answer into a larger workflow.

Ailoot Ultra · Batch POST /v1/batches google/gemini-3.5-flash-lite:batch

google/gemini-3.5-flash-lite:batch

Google /google/gemini-3.5-flash-lite: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-3.5-flash-lite: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-3.5-flash-lite:batch, google/gemini-3.5-flash-lite	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 $0.0003 / 1K tokens Output · Output tokens $0.0025 / 1K tokens Input · Cache read $0.00003 / 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 includingNo upper bound	Charge Input · Input tokens $0.0003 / 1K tokens Output · Output tokens $0.0025 / 1K tokens Input · Cache read $0.00003 / 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-3.5-flash-lite: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-3.5-flash-lite:batch', 'google/gemini-3.5-flash-lite']: raise ValueError("body.model must be an exact documented caller or wire ID")
    return {"endpoint":"/v1/chat/completions", "model":"google/gemini-3.5-flash-lite: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-3.5-flash-lite:batch

Method path aliases

Use the method paths shown above.

Related caller IDs

google/gemini-3.5-flash-lite:batch

FAQ · Google: Gemini 3.5 Flash Lite (batch)

What inputs and results are available?: POST /v1/batches · google/gemini-3.5-flash-lite:batch: text → text
Which fields does the request use?: POST /v1/batches · google/gemini-3.5-flash-lite:batch: endpoint, model, requests, completion_window, provider
What limits apply?: POST /v1/batches · google/gemini-3.5-flash-lite:batch: requests.min_items: 1

Google: Gemini 3.5 Flash Lite (batch)