Qwen: Qwen3 14B

Qwen: Qwen3 14B is a text model from Qwen, the model family and provider name given on this page. It belongs to the Qwen3 series and is described as a dense 14.8B parameter causal language model, built for both complex reasoning and efficient dialogue. The provider release date is 2025-04-28, and the stated knowledge cutoff is 2025-03-31. If you are choosing a general purpose text model from a well known family, this sits in the mid size range of that series rather than at either extreme.

The model takes text in and returns text out, so it is a straightforward language model with no image, audio or file handling. Its context window is 40,960 tokens, and the maximum output is 16,384 tokens. According to the provider description, it supports switching between a thinking mode for harder problems and a more direct mode for ordinary dialogue, and the API exposes this through a reasoning parameter alongside include_reasoning. That combination matters when you want the model to work through a problem step by step but still control whether the reasoning is surfaced in the response.

Those capabilities point to a few practical uses. Reasoning heavy work such as multi step analysis, planning, structured problem solving and code related questions fits the thinking mode. Everyday chat, summarisation, drafting, extraction and classification fit the faster dialogue behaviour. Because the model accepts tool definitions and structured output formats, it also suits agent style workflows where the model has to call functions or return JSON that your code can parse. The 40,960 token context is enough for long documents, extended conversations or a sizeable code file, though very large inputs will still need chunking.

You call it through api14's OpenAI-compatible API using the model id "qwen/qwen3-14b". The supported request parameters are frequency_penalty, include_reasoning, logit_bias, logprobs, max_tokens, min_p, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_logprobs and top_p. In practice that covers sampling control, deterministic runs via seed, stop sequences, token limits, log probability inspection, structured outputs and tool calling, so most standard OpenAI style clients work without changes beyond the model id.

One limit worth noting is modality: this is text only, so anything involving images or audio needs a different model. The 16,384 token output ceiling also means long generated documents must be produced in stages. If your workload depends on vision input or on outputs far beyond that ceiling, another model in the catalogue will fit better.