Google: Gemini 3.6 Flash

Google: Gemini 3.6 Flash is a text model from Google, listed on api14 under the model id google/gemini-3.6-flash. It sits in the text category and is described by its provider as a high-efficiency model built for coding, agentic workflows, and web and app development, with an emphasis on polished outputs and fewer unnecessary edits. If you are already working with Google's Gemini line, this is the Flash tier option available through api14.

The model accepts text, image, video, file, and audio as inputs, and it returns text. That mix matters if your pipeline needs to reason over a screenshot, a recorded clip, a document, or an audio file without routing each format through a separate model. The context window is 1,048,576 tokens, which is large enough to hold substantial codebases, long transcripts, or multi-file project dumps in a single request. Output is capped at 65,536 tokens, so long generations are possible but bounded.

Those capabilities point to a few practical patterns. Coding assistance is a natural fit: you can send whole repositories or large diffs as context and ask for changes, reviews, or explanations. Agentic workflows benefit from the tool calling support and the long context, since an agent can carry conversation history, tool results, and retrieved documents without aggressive trimming. Web and app development tasks, such as generating components from a design image or a screen recording, follow from the combination of image and video input with text output. Document and audio inputs also suit summarization, extraction, and question answering over meeting recordings or uploaded files.

Calling it through api14 works the same way as any other OpenAI-compatible endpoint: send a chat completion request with the model field set to google/gemini-3.6-flash. The supported 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, get deterministic-ish repeats with seed, constrain output with response_format or structured_outputs, and wire up function calling with tools and tool_choice. The reasoning-related parameters let you surface or tune internal reasoning where the model supports it.

One thing to keep in mind is that output is text only, so if you need generated images, audio, or video, this model will not produce them even though it can read those formats as input. The 65,536 token output ceiling is generous but finite, so very large single-shot generations may need to be split. Beyond that, the facts here do not indicate specific weaknesses, so the choice mostly comes down to whether you want a Google Flash-tier model with broad input modality support and a very large context window for coding and agent work.