Google: Gemini 3.8 Flash

Google: Gemini 3.8 Flash is a text model from Google, listed on api14 under the model id google/gemini-3.8-flash. It sits in the Flash line, which Google positions as its fast, efficient tier, and the provider describes it as Google's most intelligent Flash model, with significant gains over 3.7 Flash across software engineering, agentic tasks, and multi-step reasoning. It is a general text model rather than a specialised one, so it is intended for the broad range of generation and reasoning work that developers send to a hosted chat model.

The model accepts text, image, video, file, and audio as input, and returns text only. That mix makes it a multimodal reader with a text writer: you can hand it a document, a screenshot, a recording, or a video alongside your instructions, and read the answer as text. The context window is 1,048,576 tokens, and the maximum output is 65,536 tokens. The large input budget matters most when you are working with long files or long transcripts, since you can keep more source material in a single request instead of chunking it yourself.

Those capabilities point to a few practical patterns. Long document and file analysis, where a contract, report, or codebase is passed in whole and summarised or queried. Media understanding, where images, audio, or video are described, transcribed, or checked against a question. Agentic and tool-driven workflows, since the model supports tools and tool_choice, which suits multi-step tasks that need to call functions or fetch data between steps. Structured extraction is another fit, using response_format or structured_outputs to get predictable JSON back for downstream code. Reasoning-heavy work such as debugging, planning, or multi-step analysis also follows from the stated gains in software engineering and reasoning.

You call it through api14's OpenAI-compatible API by setting the model field to google/gemini-3.8-flash. 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 covers sampling control (temperature, top_p, seed, stop), output length (max_tokens), structured responses (response_format, structured_outputs), tool use (tools, tool_choice), and control over reasoning behaviour (reasoning, reasoning_effort, include_reasoning). Because the interface is OpenAI-compatible, existing clients and SDKs generally work with only the model id changed.

There is no stated restriction in these facts that would rule the model out for a given task, so the main consideration is fit rather than a hard limit. If your workload is text only and does not need the multimodal inputs or the very large context, a smaller or more specialised model may be simpler to work with. If you need output in a modality other than text, this model does not provide it. Otherwise, the combination of a 1,048,576 token context, multimodal input, tool support, and structured output makes it a reasonable default for document, media, and agentic workloads.