Google: Gemini 2.5 Flash Lite is made by Google, and OpenAI: GPT-4.1 Nano is made by OpenAI. Both are text output models in the lightweight tier of their respective families, and both are served through api14 under the same OpenAI-compatible interface. The two sit in the same rough price band, with input and output pricing about the same on each side, so cost is not the deciding factor between them.
The clearest split is in what they accept as input. Gemini 2.5 Flash Lite takes text, image, file, audio and video, while GPT-4.1 Nano takes image, text and file. If your pipeline needs to hand a model audio or video directly, Gemini 2.5 Flash Lite is the one that can do it. Context length is effectively a wash: Gemini 2.5 Flash Lite lists 1048576 tokens and GPT-4.1 Nano lists 1047576, a difference of about a thousand tokens that will not matter in practice. On parameters, Gemini 2.5 Flash Lite exposes reasoning controls (include_reasoning and reasoning) that GPT-4.1 Nano does not, so it is the only one of the two where you can turn reasoning behaviour on or off. Both support tools, tool choice, structured outputs, response format, seed, temperature and top p, so function calling and schema-constrained output are available either way.
For day to day work, the choice usually comes down to input type and reasoning. If you are summarising documents, classifying text, extracting structured fields or running tool calls over plain text and images, either model will do the job and you can pick on latency or whatever you have already tested. Reach for Gemini 2.5 Flash Lite when the input includes audio or video, or when you want the option of reasoning on a harder prompt without switching to a larger model. Reach for GPT-4.1 Nano when your workload is text and image only and you prefer the parameter set you already use elsewhere in the OpenAI family, since it uses max_completion_tokens rather than max_tokens.
Calling either one through api14 works the same way. Point your client at the api14 base URL, send a standard chat completions request, and set the model field to google/gemini-2.5-flash-lite or openai/gpt-4.1-nano. Nothing else in the request shape changes, so you can keep one code path and swap the model id to compare outputs, run an A/B test, or fall back from one to the other. The one thing to watch when swapping is the token limit parameter name, because Gemini 2.5 Flash Lite accepts max_tokens and GPT-4.1 Nano expects max_completion_tokens.
There are still cases where the other model wins. If you need reasoning controls, GPT-4.1 Nano is not the pick, because only Gemini 2.5 Flash Lite exposes them here. If you want the model with the longer track record in your existing OpenAI-based tooling and you never send audio or video, GPT-4.1 Nano keeps that path unchanged. Otherwise the two are close enough that a quick test on your own prompts will tell you more than the spec sheet.