Gemini 3.8 Flash is Google’s text-output model for sustained software engineering, multi-step agents and complex knowledge workflows. It builds on Gemini 3.7 Flash, with improvements in software engineering and agentic knowledge work. It accepts text, images, audio, video and PDF, so a task can combine a written objective with the actual material needed to resolve it. The useful result is a code change, an explained analysis, a written answer or a structured record.
For a coding assignment, supply the requirement, relevant files, observed behaviour and the constraints to preserve. A screenshot can establish the intended interface, while logs or a reproducing example show the failure. For a debugging task, ask the model to trace a mismatch between a form’s visible state and its stored value, identify the responsible code path and propose a focused correction. Keep the intended final artifact clear so a longer sequence of investigation and tool use still has a specific completion condition.
For longer-running work, use smaller reasoning steps, iterative tool calls and checks of intermediate results. Low, medium and high thinking levels control the amount of reasoning; medium is the documented default. Low suits a quick draft or bounded analysis, while high is intended for difficult mathematics, coding or multi-step tasks. Gemini 3.8 Flash does not support the minimal thinking level. A sustained debugging assignment and a straightforward field extraction therefore need different reasoning choices rather than the same setting for every request.
The documented tools include function calling, code execution, file search, search grounding, Google Maps grounding and URL context, with Computer Use listed as a preview capability. Structured outputs define a machine-readable result. Functions should have a clear purpose and parameter definition: the model can request a function, while the connected application performs the operation and returns its result. Preserve function identities and the actual returned material across turns. A useful final answer explains which evidence supports the proposed correction or extracted value.
The 3.8 migration guide uses thinking-level controls in place of legacy reasoning-budget configuration and advises removing deprecated sampling parameters and prefilled model turns. It also requires preserving thought signatures in the relevant stateless workflow and matching function responses to their calls by identity and name. These details matter when continuing a multi-step task: the next turn needs the actual prior context rather than a rewritten account that loses the tool result or signature. Keep the question and requested output explicit in the new user turn.
For multimodal knowledge work, identify the relevant document pages, chart, audio interval or video event and state how it should contribute to the answer. A document comparison can request a table of differences with source passages; a chart question can ask for the visible values and the conclusion they support. A video analysis can request a description of the events that justify a finding. The model returns text from these inputs. Generated images, speech and Live audio interaction belong to separate model profiles.
Its Gemini API limits are 1,048,576 input tokens and 65,536 output tokens, corresponding to the native model’s up to 1M context and 64K text output. Instructions, conversation history and supplied media consume input context, and reasoning or a long final artifact must fit the documented output allowance. Google also lists caching, batch processing, Flex and Priority processing for this producer profile. For independent items, retain a stable item identifier and an explicit output format so each result remains connected to its source.
Possible limitations include hallucinations, occasional slowness or timeouts and greater token use at higher effort. Its knowledge cutoff is March 2026, with some domains still limited to January 2025. Supply current evidence or use the documented grounding tools when the task depends on later information. Review source citations, calculations and proposed code against the actual inputs and execution results. The final useful artifact should distinguish a supported finding from an unresolved question without assuming that a fluent answer establishes the requested outcome.
", "aboutText": "Gemini 3.8 Flash is Google’s text-output model for sustained software engineering, multi-step agents and complex knowledge workflows. It builds on Gemini 3.7 Flash, with improvements in software engineering and agentic knowledge work. It accepts text, images, audio, video and PDF, so a task can combine a written objective with the actual material needed to resolve it. The useful result is a code change, an explained analysis, a written answer or a structured record.\n\nFor a coding assignment, supply the requirement, relevant files, observed behaviour and the constraints to preserve. A screenshot can establish the intended interface, while logs or a reproducing example show the failure. For a debugging task, ask the model to trace a mismatch between a form’s visible state and its stored value, identify the responsible code path and propose a focused correction. Keep the intended final artifact clear so a longer sequence of investigation and tool use still has a specific completion condition.\n\nFor longer-running work, use smaller reasoning steps, iterative tool calls and checks of intermediate results. Low, medium and high thinking levels control the amount of reasoning; medium is the documented default. Low suits a quick draft or bounded analysis, while high is intended for difficult mathematics, coding or multi-step tasks. Gemini 3.8 Flash does not support the minimal thinking level. A sustained debugging assignment and a straightforward field extraction therefore need different reasoning choices rather than the same setting for every request.\n\nThe documented tools include function calling, code execution, file search, search grounding, Google Maps grounding and URL context, with Computer Use listed as a preview capability. Structured outputs define a machine-readable result. Functions should have a clear purpose and parameter definition: the model can request a function, while the connected application performs the operation and returns its result. Preserve function identities and the actual returned material across turns. A useful final answer explains which evidence supports the proposed correction or extracted value.\n\nThe 3.8 migration guide uses thinking-level controls in place of legacy reasoning-budget configuration and advises removing deprecated sampling parameters and prefilled model turns. It also requires preserving thought signatures in the relevant stateless workflow and matching function responses to their calls by identity and name. These details matter when continuing a multi-step task: the next turn needs the actual prior context rather than a rewritten account that loses the tool result or signature. Keep the question and requested output explicit in the new user turn.\n\nFor multimodal knowledge work, identify the relevant document pages, chart, audio interval or video event and state how it should contribute to the answer. A document comparison can request a table of differences with source passages; a chart question can ask for the visible values and the conclusion they support. A video analysis can request a description of the events that justify a finding. The model returns text from these inputs. Generated images, speech and Live audio interaction belong to separate model profiles.\n\nIts Gemini API limits are 1,048,576 input tokens and 65,536 output tokens, corresponding to the native model’s up to 1M context and 64K text output. Instructions, conversation history and supplied media consume input context, and reasoning or a long final artifact must fit the documented output allowance. Google also lists caching, batch processing, Flex and Priority processing for this producer profile. For independent items, retain a stable item identifier and an explicit output format so each result remains connected to its source.\n\nPossible limitations include hallucinations, occasional slowness or timeouts and greater token use at higher effort. Its knowledge cutoff is March 2026, with some domains still limited to January 2025. Supply current evidence or use the documented grounding tools when the task depends on later information. Review source citations, calculations and proposed code against the actual inputs and execution results. The final useful artifact should distinguish a supported finding from an unresolved question without assuming that a fluent answer establishes the requested outcome.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "6a7013560dc0c57265266bab1819e661a1678511ab9754e262c859204223113c", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "en", "methods": [ { "html": "google/gemini-3.8-flash:batchGemini 3.8 Flash is Google’s multimodal workhorse for sustained software engineering, agents and complex knowledge tasks. It accepts text, images, audio, video and PDF, returns text, and offers low, medium or high reasoning with tools and structured output. Its documented input/output limits are 1,048,576/65,536 tokens.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Gemini 3.8 Flash is Google’s text-output model for sustained software engineering, multi-step agents and complex knowledge workflows. It builds on Gemini 3.7 Flash, with improvements in software engineering and agentic knowledge work. It accepts text, images, audio, video and PDF, so a task can combine a written objective with the actual material needed to resolve it. The useful result is a code change, an explained analysis, a written answer or a structured record.
For a coding assignment, supply the requirement, relevant files, observed behaviour and the constraints to preserve. A screenshot can establish the intended interface, while logs or a reproducing example show the failure. For a debugging task, ask the model to trace a mismatch between a form’s visible state and its stored value, identify the responsible code path and propose a focused correction. Keep the intended final artifact clear so a longer sequence of investigation and tool use still has a specific completion condition.
For longer-running work, use smaller reasoning steps, iterative tool calls and checks of intermediate results. Low, medium and high thinking levels control the amount of reasoning; medium is the documented default. Low suits a quick draft or bounded analysis, while high is intended for difficult mathematics, coding or multi-step tasks. Gemini 3.8 Flash does not support the minimal thinking level. A sustained debugging assignment and a straightforward field extraction therefore need different reasoning choices rather than the same setting for every request.
The documented tools include function calling, code execution, file search, search grounding, Google Maps grounding and URL context, with Computer Use listed as a preview capability. Structured outputs define a machine-readable result. Functions should have a clear purpose and parameter definition: the model can request a function, while the connected application performs the operation and returns its result. Preserve function identities and the actual returned material across turns. A useful final answer explains which evidence supports the proposed correction or extracted value.
The 3.8 migration guide uses thinking-level controls in place of legacy reasoning-budget configuration and advises removing deprecated sampling parameters and prefilled model turns. It also requires preserving thought signatures in the relevant stateless workflow and matching function responses to their calls by identity and name. These details matter when continuing a multi-step task: the next turn needs the actual prior context rather than a rewritten account that loses the tool result or signature. Keep the question and requested output explicit in the new user turn.
For multimodal knowledge work, identify the relevant document pages, chart, audio interval or video event and state how it should contribute to the answer. A document comparison can request a table of differences with source passages; a chart question can ask for the visible values and the conclusion they support. A video analysis can request a description of the events that justify a finding. The model returns text from these inputs. Generated images, speech and Live audio interaction belong to separate model profiles.
Its Gemini API limits are 1,048,576 input tokens and 65,536 output tokens, corresponding to the native model’s up to 1M context and 64K text output. Instructions, conversation history and supplied media consume input context, and reasoning or a long final artifact must fit the documented output allowance. Google also lists caching, batch processing, Flex and Priority processing for this producer profile. For independent items, retain a stable item identifier and an explicit output format so each result remains connected to its source.
Possible limitations include hallucinations, occasional slowness or timeouts and greater token use at higher effort. Its knowledge cutoff is March 2026, with some domains still limited to January 2025. Supply current evidence or use the documented grounding tools when the task depends on later information. Review source citations, calculations and proposed code against the actual inputs and execution results. The final useful artifact should distinguish a supported finding from an unresolved question without assuming that a fluent answer establishes the requested outcome.
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-3.8-flash: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-3.8-flash:batch, google/gemini-3.8-flash | 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 $0.00075 / 1K tokens Output · Output tokens $0.00375 / 1K tokens Input · Cache read $0.000075 / 1K tokens Input · Cache write $0.0000833333333333334 / 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 $0.00075 / 1K tokens Output · Output tokens $0.00375 / 1K tokens Input · Cache read $0.000075 / 1K tokens Input · Cache write $0.0000833333333333334 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${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-3.8-flash: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-3.8-flash:batch', 'google/gemini-3.8-flash']: raise ValueError("body.model must be an exact documented caller or wire ID")\n return {"endpoint":"/v1/chat/completions", "model":"google/gemini-3.8-flash: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-3.8-flash:batch
Use the method paths shown above.