Google: Gemini 3.5 Flash (batch)

Google: Gemini 3.5 Flash (batch) is a text model from Google, offered through api14 under the model id google/gemini-3.5-flash:batch. It belongs to the Gemini 3.5 Flash line, which Google describes as its high-efficiency multimodal model, positioned to bring near-Pro level coding and reasoning at Flash-tier cost and speed. The batch variant is the same model surfaced for batch style usage on api14, so the underlying provider and behaviour are Google's rather than a third party's.

The model accepts text, image, video, file and audio as input, and returns text only. Its context window is 1,048,576 tokens, which is large enough to hold very long documents, long transcripts or extended agent histories in a single request. The maximum output is 65,536 tokens, and the knowledge cutoff is 2025-01-01. Because input is multimodal but output is text, it fits tasks where you feed rich material in and expect written answers, summaries, code or structured data out.

That combination points to a fairly specific set of uses. Long context plus mixed input suits document and codebase analysis, where you paste or upload large files and ask for explanations, refactors or reviews. Video and audio input support transcription style workflows, meeting or lecture summarisation, and question answering over recorded material. The provider description emphasises coding proficiency and parallel agentic execution, so tool driven pipelines, multi step agents and batch processing of many similar requests are natural fits. Structured outputs and response_format also make it reasonable for extraction jobs where downstream code needs predictable shapes.

To call it through api14, use the OpenAI-compatible chat completions interface with the model id google/gemini-3.5-flash:batch. The supported request parameters are include_reasoning, max_tokens, reasoning, reasoning_effort, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools and top_p. In practice that means you can control sampling with temperature and top_p, cap length with max_tokens, constrain output with response_format or structured_outputs, and wire up function calling with tools and tool_choice. The reasoning related parameters let you decide whether reasoning content is returned and how much effort the model spends on it, and seed plus stop give you reproducibility and clean truncation points.

There are a couple of things to weigh before committing. Output is text only, so if you need generated images, audio or video, this model will not produce them. The knowledge cutoff is 2025-01-01, so anything after that date has to come from your prompt or from tools you attach. If your workload is small, interactive and latency sensitive rather than batch oriented, a non-batch Gemini 3.5 Flash endpoint may be the better match, and if you need a different provider's ecosystem or a model with a smaller footprint, it is worth comparing alternatives before standardising on this one.