Gemini 4 Argon (batch) is the published batch-labelled variant of Google’s multimodal reasoning model. It retains the Argon model family’s focus on software engineering, mathematical and scientific analysis, and multi-step reasoning over large evidence sets. The record accepts text, images, audio, video and files such as PDFs and returns text.
A useful batch workload contains many clearly defined items with comparable result requirements. A document collection can use one extraction or comparison rubric across all items. A code-review workload can define the same review questions for a series of changes. A research collection can ask for the same evidence fields from each source package.
Give every work item an identity that the surrounding application retains with its input and result. Keep the instructions stable where the work is comparable, while placing item-specific evidence and questions in the individual task. This makes completed outputs easier to compare without confusing one document or repository change with another.
The published context window is 1,048,576 tokens. Keep related source material together and identify document boundaries, filenames or sections that matter to the task. Ask for an output that serves the collection’s purpose: a reviewed change, an evidence table, a structured set of findings or a concise report.
The batch variant publishes mandatory reasoning with low, medium and high effort levels. Match the effort to the questions that must be resolved and keep the answer format explicit. When the response is consumed by another process, the published structured-output capability can support fields that remain comparable across the collection. Tool-calling is also listed as a model feature; the application defines and executes the tools supplied to the task.
The standard and batch entries have distinct published identifiers within the Argon family. Preserve the exact batch suffix when selecting this variant and retain that choice with the completed artifact. Use the chosen variant’s own capacity and output requirements when organising the collection.
For a large answer, plan the expected content and the completion allowance of the selected batch profile before choosing a work-item size. The batch source page discusses long output separately from the standard entry. Keep the batch variant and its selected completion allowance attached to the collection so every item follows the same output plan.
Use a representative sample of the collection to assess whether the chosen instructions produce the required evidence and level of detail. Keep document references, extracted findings and any open questions together in each result. If an item needs a revision, retain its original evidence and selection so the corrected result remains comparable with the rest of the collection.
google/gemini-4-argon:batchGemini 4 Argon (batch) is the separately listed batch variant of Google’s long-context reasoning model. It accepts text, images, audio, video and documents and produces text, with tool-calling and structured-output features. Keep its identifier and output allowances distinct from standard Argon when organising batch work.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Gemini 4 Argon (batch) is the published batch-labelled variant of Google’s multimodal reasoning model. It retains the Argon model family’s focus on software engineering, mathematical and scientific analysis, and multi-step reasoning over large evidence sets. The record accepts text, images, audio, video and files such as PDFs and returns text.
A useful batch workload contains many clearly defined items with comparable result requirements. A document collection can use one extraction or comparison rubric across all items. A code-review workload can define the same review questions for a series of changes. A research collection can ask for the same evidence fields from each source package.
Give every work item an identity that the surrounding application retains with its input and result. Keep the instructions stable where the work is comparable, while placing item-specific evidence and questions in the individual task. This makes completed outputs easier to compare without confusing one document or repository change with another.
The published context window is 1,048,576 tokens. Keep related source material together and identify document boundaries, filenames or sections that matter to the task. Ask for an output that serves the collection’s purpose: a reviewed change, an evidence table, a structured set of findings or a concise report.
The batch variant publishes mandatory reasoning with low, medium and high effort levels. Match the effort to the questions that must be resolved and keep the answer format explicit. When the response is consumed by another process, the published structured-output capability can support fields that remain comparable across the collection. Tool-calling is also listed as a model feature; the application defines and executes the tools supplied to the task.
The standard and batch entries have distinct published identifiers within the Argon family. Preserve the exact batch suffix when selecting this variant and retain that choice with the completed artifact. Use the chosen variant’s own capacity and output requirements when organising the collection.
For a large answer, plan the expected content and the completion allowance of the selected batch profile before choosing a work-item size. The batch source page discusses long output separately from the standard entry. Keep the batch variant and its selected completion allowance attached to the collection so every item follows the same output plan.
Use a representative sample of the collection to assess whether the chosen instructions produce the required evidence and level of detail. Keep document references, extracted findings and any open questions together in each result. If an item needs a revision, retain its original evidence and selection so the corrected result remains comparable with the rest of the collection.
POST /v1/batchestexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldendpoint | Typestring | RequirementRequired | Values & limits /v1/chat/completions | Default— |
Fieldmodel | Typestring | RequirementRequired | Values & limits google/gemini-4-argon:batch | Default— |
Fieldrequests | Typebatch_item[] | RequirementRequired | Values & limits Minimum items: 1 | Default— |
Fieldrequests[].custom_id | Typestring | RequirementRequired | Values & limits | Default— |
Fieldrequests[].body.messages | Typemessage[] | RequirementRequired | Values & limits | Default— |
Fieldcompletion_window | Typestring | RequirementOptional | Values & limits 24h | Default— |
Fieldprovider | Typeobject|null | RequirementOptional | Values & limits | Default— |
Fieldrequests[].body.model | Typestring | RequirementOptional | Values & limits google/gemini-4-argon:batch, google/gemini-4-argon | Default— |
Fieldprovider.only | Typestring[] | RequirementOptional | Values & limits google-vertex/global | Default— |
Fieldprovider.allow_fallbacks | Typeboolean|null | RequirementOptional | Values & limits false, null | Default— |
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractendpoint, model, requests |
Parametermaterial_types | Contracttext |
Parameterrepresentation | Contractinline requests[]; each item has custom_id and body |
Parameterrequest_endpoint_profile | Contract/v1/chat/completions |
Parameterfile_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. |
Parameterserialization_order | Contractendpoint, model, provider (if supplied), completion_window (if supplied), requests last |
Parameteroptional_controls | Contractprovider.only, provider.allow_fallbacks, completion_window |
Parameterschema_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
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 $0.002 / 1K tokens Output · Output tokens $0.01 / 1K tokens Input · Cache read $0.0001 / 1K tokens Input · Cache write $0.000375 / 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 $0.002 / 1K tokens Output · Output tokens $0.01 / 1K tokens Input · Cache read $0.0001 / 1K tokens Input · Cache write $0.000375 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json\nfrom pathlib import Path\nbody=json.loads('{"endpoint":"/v1/chat/completions","model":"google/gemini-4-argon:batch","requests":[{"custom_id":"req-0001","body":{"messages":[{"role":"user","content":"Hello!"}],"max_tokens":16}}]}')\nPath("batch-request.json").write_text(json.dumps(body,ensure_ascii=False),encoding="utf-8")\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/batches \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @batch-request.json -D batch-response-headers.txt -o batch-create-response.json\nimport json, os\nfrom pathlib import Path\nfrom urllib.request import Request, urlopen\n\ndef prepare_requests(customer_path):\n path = Path(customer_path)\n if not path.is_file(): raise ValueError("Customer JSONL file is missing")\n items = [json.loads(line) for line in path.read_text(encoding="utf-8").splitlines() if line.strip()]\n if not items: raise ValueError("Customer JSONL contains no requests")\n ids = set()\n for item in items:\n if set(item) != {"custom_id", "body"}: raise ValueError("Each item needs custom_id and body")\n cid = item["custom_id"]\n if not isinstance(cid, str) or not cid.strip() or cid in ids: raise ValueError("custom_id must be nonempty and unique")\n ids.add(cid)\n if not isinstance(item["body"], dict) or not item["body"].get("messages"): raise ValueError("Supply actual text messages")\n supplied_model = item["body"].get("model")\n if supplied_model is not None and supplied_model not in ['google/gemini-4-argon:batch', 'google/gemini-4-argon']: raise ValueError("body.model must be an exact documented caller or wire ID")\n return {"endpoint":"/v1/chat/completions", "model":"google/gemini-4-argon:batch", "requests":items}\n\nif __name__ == "__main__":\n material = os.environ.get("AILOOT_BATCH_REQUESTS_JSONL", "")\n if not material: raise SystemExit("Set AILOOT_BATCH_REQUESTS_JSONL to your actual customer file")\n body = prepare_requests(material)\n key = os.environ.get("AILOOT_API_KEY", "")\n if not key: raise SystemExit("Set AILOOT_API_KEY")\n 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")\n with urlopen(request) as response:\n raw = response.read()\n Path("batch-create-response.json").write_bytes(raw)\n print(raw.decode("utf-8"))\nimport json\nfrom pathlib import Path\nraw = Path("batch-status-response.json").read_bytes()\nresponse = json.loads(raw)\nif response.get("status") != "completed":\n raise SystemExit("Keep the original operation; read its status without resubmitting")\nresults = response.get("results")\nif not isinstance(results, list) or not results:\n raise SystemExit("Preserve the original response; completed results are missing")\nPath("batch-results.jsonl").write_text("".join(json.dumps(item, ensure_ascii=False) + "\\n" for item in results), encoding="utf-8")\n# Preserve batch-status-response.json with aggregate usage and every result/error.\ninline results[].response.body JSON
GET /v1/batches/{operation_id}set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\nBATCH_OPERATION_ID="$(python3 - <<'PY_ID'\nimport json\nfrom pathlib import Path\nfrom urllib.parse import quote\nresponse = json.loads(Path("batch-create-response.json").read_text(encoding="utf-8"))\noperation = response.get("id")\nif not isinstance(operation, str) or not operation: raise SystemExit("No actual original operation ID; preserve the response and do not resubmit")\nprint(quote(operation, safe=""))\nPY_ID\n)"\ncurl --fail-with-body --silent --show-error "https://api.ailoot.ai/v1/batches/$BATCH_OPERATION_ID" \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -o batch-status-response.json\nKeep 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.
Preserve the original operation ID. 503 result_pending_check has X-Should-Retry:false; never resubmit an unknown original batch.
| Field | Type |
|---|---|
Fieldresults[].custom_id | Typestring |
Fieldresults[].response.body.choices[].message.content | Typestring|null |
Fieldresults[].error | Typeobject|null |
Fieldusage | Typeobject|null |
Fieldid | Typestring |
Fieldmodel | Typestring |
Fieldendpoint | Typestring |
Fieldstatus | Typestring |
Fieldrequest_counts | Typeobject |
Fieldresults[].id | Typestring |
Fieldresults[].response.status_code | Typeinteger |
Fieldresults[].response.request_id | Typestring |
| 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. |
Ailoot Ultra google/gemini-4-argon:batch
Use the method paths shown above.