Google: Gemini 3.1 Flash Lite (batch)

Gemini 3.1 Flash-Lite is Google’s stable lightweight multimodal text model for frequent, well-defined tasks. It is based on Gemini 3 Pro. Translation and classification are central workloads; other useful tasks include transcription, document summaries, entity extraction and model-choice classification. Text, images, audio, video and PDF can supply the source material, and the result is text. A useful brief asks for one operation and one final form, such as a translated message or a record of fields found in a document.

For translation, provide the source text, target language, audience and terminology that should remain intact. Google’s example uses instructions that return only the translation. For a support-message translation, preserve product names and order references while translating the rest naturally. Specify how ambiguity should be handled so the result does not add a date, promise or detail missing from the source. Keep the requested form appropriate to the destination, such as a short reply or a labelled translation record.

For transcription, supply the recording and define whether the answer should be spoken words, a readable edited transcript or a summary. These are different outputs. Identify the relevant interval when a long recording contains only one passage needed for the task. The maker’s developer example passes audio directly and requests a text transcript. Check names, numbers and unclear passages against the recording, retaining the original source for review. This general multimodal text profile does not itself imply an audio-output or dedicated speaker-diarization result.

For classification or extraction, define the categories, schema and missing-value rule. The developer guide shows structured JSON for analysing a customer review, with a supporting quotation and defined fields. A structured record can contain the mentioned product, issue category and sentence that justifies the choice. Include a representative case that should stay unknown rather than be forced into a category. A syntactically valid record still needs comparison with the actual source text.

Document processing can turn a PDF into a concise summary or a field record. State the question and preserve page or section references where the finding needs checking. A table review should specify the columns and rule that matter; a visual task should identify the label or region needed for the answer. Model routing is another documented use: the model classifies a request’s complexity and returns an allowed choice. The connected application, rather than the classification text itself, decides what to run next.

Thinking is supported, and the maker shows higher effort for tasks that benefit from more analysis. The exact stable page also lists function calling, code execution, file search, search grounding, Maps grounding, URL context and structured output. Computer Use is not supported in this stable 3.1 profile; the later 3.5 Flash-Lite profile has a different capability table. Define tool purposes and parameters clearly and continue from real returned results. A function suggestion does not establish that the operation has taken place.

The native model card lists up to 1M context and 64K text output; the stable API table specifies 1,048,576 input tokens and 65,536 output tokens. Include the instructions, conversation and source media in the input preparation, and request an output sized for the intended artifact. Caching, batch, Flex and Priority processing are documented for this producer profile. For repeated independent items, carry the input identifier into the result and retain a consistent schema so a missing answer can be identified.

This stable model identifier is distinct from Gemini 3.1 Flash-Lite Preview and later Flash-Lite editions. Preserve the exact edition when preparing repeated processing rather than treating a familiar family name as a fixed revision guarantee. Review translations for retained meaning and terms, transcripts against the audio and extracted fields against the document. The profile’s practical role is focused multimodal text processing with a clear result, leaving a broader task’s reasoning or external actions to the appropriate subsequent step.