Google: Gemini 3.8 Flash (batch)

Google: Gemini 3.8 Flash (batch) is a text model from Google, offered through api14 under the model id google/gemini-3.8-flash:batch. It belongs to Google's Flash line, which the provider describes as its most intelligent Flash model, with significant gains over 3.7 Flash in software engineering, agentic tasks, and multi-step reasoning. The batch variant is the same model surfaced for batch style workloads, so you can treat it as the Flash model you would reach for when throughput matters more than interactive latency.

The model accepts text, image, video, file, and audio as input, and it returns text only. That mix makes it a fit for tasks where the prompt is not just a string: screenshots, screen recordings, PDFs, audio clips, and plain text can all go in, and the answer comes back as text. The context window is 1,048,576 tokens, and the maximum output is 65,536 tokens. Supported request parameters include max_tokens, stop, seed, response_format, structured_outputs, tool_choice, tools, and the reasoning controls include_reasoning, reasoning, and reasoning_effort.

Those capabilities point to a few practical patterns. Long documents, codebases, or transcript archives fit inside the context window, so you can ask for summaries, extraction, or question answering without chunking first. Because images, video, audio, and files are accepted, you can build pipelines that read a screen recording and produce a written walkthrough, transcribe and reason over a call recording, or pull structured fields out of scanned documents. Tool calling and structured outputs suit agentic workflows: the model can decide which function to invoke, return JSON that matches a schema, and chain several steps of reasoning before answering. The reasoning controls let you dial effort up for multi-step problems or down when you want faster, cheaper responses.

To call it through api14, use the OpenAI-compatible chat completions endpoint with the model id google/gemini-3.8-flash:batch. Because the interface follows the OpenAI schema, existing clients and SDKs work with a base URL change and an api14 key. You can send multimodal content in the messages array, set max_tokens to cap the reply, use stop sequences, pass a seed for reproducibility, and request JSON via response_format or structured_outputs. If you want the model to call functions, supply tools and tool_choice. The reasoning parameters, include_reasoning, reasoning, and reasoning_effort, control how much internal reasoning is exposed or applied.

The main constraint to keep in mind is that output is text only, so this is not the model for generating images, audio, or video. If your task is purely short text generation with no need for long context or multimodal input, a smaller model may be sufficient. If you need output modalities other than text, look for a model that supports them. For long-context, multimodal, tool-using batch work, this model covers the ground described above.