NVIDIA: Nemotron 3.5 Lightning (free)

Nemotron 3.5 Lightning is an open mixture-of-experts model from NVIDIA. It has 3B active parameters out of 30B total, which means only a fraction of the network runs per token while the full model still holds a broad base of learned knowledge. It sits in the text category, so it is built for language work rather than images or audio. The "free" tag on api14 marks this variant as one you can try without a paid plan.

The model takes text in and returns text out. Its context window is 1,000,000 tokens, which is large enough to hold whole codebases, long document sets or extended agent transcripts in a single request. The maximum output is 65,536 tokens, so a single call can produce a long report, a large refactor or a multi-step plan without splitting the work. Because it is a mixture-of-experts design with a small active parameter count, it is positioned for high-throughput agentic workloads and specialized tasks, where many calls happen in parallel and latency per call matters.

Those traits point to a few practical patterns. Long-context retrieval and summarization fit well: you can pass a large corpus and ask for a synthesis in one shot. Agent loops are another fit, since the model supports tool calling and can decide when to invoke a function and how to use the result. Batch classification, extraction and routing over large volumes of text also make sense given the throughput orientation. Reasoning-heavy prompts are supported too, so tasks that benefit from a visible chain of thought before the final answer can be handled in the same model.

You call it through api14's OpenAI-compatible API using the model id "nvidia/nemotron-3.5-lightning:free". Point your existing OpenAI client at the api14 base URL and pass that id in the model field. The request parameters it supports are include_reasoning, max_tokens, reasoning, seed, temperature, tool_choice, tools and top_p. Use tools and tool_choice for function calling, temperature and top_p to shape sampling, seed for reproducible runs, and max_tokens to cap the response. The reasoning and include_reasoning parameters control whether reasoning content is requested and returned.

One thing to weigh is the output ceiling. At 65,536 tokens per response, very large single-shot generations may need to be split across calls even though the input window is far larger. If your task depends on modalities other than text, this model will not cover it. And if you need the strongest possible reasoning quality rather than throughput on agentic and specialized work, a larger model may be the better pick.