Google: Gemini 3.5 Flash Lite is made by Google, and MiniMax: MiniMax M1 is made by MiniMax. Both are text output models, meaning whatever you send in, you get text back. Gemini 3.5 Flash Lite was released by its provider on 2026-07-21, while MiniMax M1 was released on 2025-06-17. If you are already routing traffic through api14, both are available under the same OpenAI-compatible surface, so the choice comes down to what your task actually needs rather than which SDK you have installed.
The clearest split is in input modalities. Gemini 3.5 Flash Lite accepts text, image, video, file and audio as input, while MiniMax M1 accepts text only. If your prompt includes a screenshot, a PDF, a recording or a video frame, Gemini 3.5 Flash Lite is the one that can take it. On context, the two are close enough that the difference is not worth planning around: Gemini 3.5 Flash Lite lists 1048576 tokens and MiniMax M1 lists 1000000 tokens. Both support tool calling, so neither has an exclusive advantage there. Gemini 3.5 Flash Lite is the only one of the two with structured outputs, which matters if you need the response to conform to a schema. Both expose reasoning controls through include_reasoning and reasoning, so that is not a deciding factor either. On price, Gemini 3.5 Flash Lite is the cheaper option for input, while MiniMax M1 is the cheaper option for output, so the better deal depends on your input to output ratio.
For a task that is mostly text in and text out, with long outputs and modest prompts, MiniMax M1 is a reasonable default, since its output side is the cheaper one and it supports the usual sampling controls including frequency_penalty, presence_penalty, repetition_penalty and top_k. For anything that involves reading an image, a document, a video or audio, or for workloads where you want the model to emit structured output, Gemini 3.5 Flash Lite is the better fit. Its parameter list also includes response_format, structured_outputs and reasoning_effort, which gives you more control over how the answer is shaped. If your prompts are large and your completions are short, the input pricing advantage also points to Gemini 3.5 Flash Lite.
Calling either model through api14 works the same way. You point your OpenAI-compatible client at the gateway, send a chat completions request, and set the model field. Use google/gemini-3.5-flash-lite for the Google model and minimax/minimax-m1 for the MiniMax model. Everything else in the request body stays as it is: messages, temperature, max_tokens, tools and the rest. Switching between the two is a one line change to the model id, so you can benchmark both on your own traffic without rewriting your integration. Keep in mind that parameters are not perfectly interchangeable. If you send top_k or repetition_penalty to Gemini 3.5 Flash Lite, or structured_outputs to MiniMax M1, expect the gateway or provider to reject or ignore what that model does not list.
There are still cases where MiniMax M1 is the better pick even when Gemini 3.5 Flash Lite looks more capable on paper. If your workload is pure text, your outputs are long, and you do not need schema constrained responses or multimodal input, MiniMax M1's cheaper output side can make it the more economical choice. It also gives you penalty style controls that Gemini 3.5 Flash Lite does not list, which can help when you are fighting repetition in long generations. The two context windows are close enough that neither wins on length alone, so the decision rests on modality, structured output support and the input versus output price balance.