Qwen: Qwen3.5-27B

Qwen3.5-27B is a text generation model from Qwen, listed on api14 as qwen/qwen3.5-27b. It belongs to the Qwen3.5 family and, according to the provider's own description, is a native vision-language Dense model built with a linear attention mechanism, which the provider says delivers fast response times while balancing inference speed and performance. The provider states that its overall capabilities are comparable to those of larger models in the family. It is a general purpose model rather than a task specific one, so it fits the usual text and reasoning workloads developers bring to a hosted chat completion endpoint.

The model accepts text, images and video as input, and produces text as output. Its context window is 262144 tokens, which is large enough to hold very long documents, long conversations, or a mix of text and media in a single request, and its maximum output length is 81920 tokens. Being able to read images and video alongside text means the same endpoint handles both plain language tasks and tasks where the input includes visual material, without needing a separate model for each.

Those capabilities point to a few practical patterns. Long context work such as summarising or querying large documents, codebases or transcripts is a natural fit, especially where the material is far too long for a smaller window. Because input can include images and video, the model can be used for captioning, visual question answering, extracting structured data from screenshots or frames, and describing what happens in a clip. The provider's emphasis on linear attention and fast responses suggests it is also reasonable for interactive applications where latency matters, such as chat assistants and tool calling agents.

Calling it through api14 is the same as calling any OpenAI compatible chat model. Send a request to the chat completions endpoint with the model id qwen/qwen3.5-27b. The API supports a wide set of request parameters: temperature, top_p, top_k, min_p, frequency_penalty, presence_penalty, repetition_penalty, max_tokens, stop, seed, logit_bias, logprobs, top_logprobs, response_format, structured_outputs, tools, tool_choice, reasoning and include_reasoning. So you can control sampling, constrain output to a JSON schema, attach tools for function calling, and request or expose the model's reasoning depending on your needs.

The documented limits are the context window of 262144 tokens and a maximum output of 81920 tokens; anything beyond that has to be split or truncated. Input modalities are text, image and video, and output is text only, so this is not the model for image or audio generation. If you need a cheaper or smaller model for high volume, low complexity text, or a model with output modalities beyond text, another entry in the catalogue may suit better. For long context and mixed text and visual input at moderate scale, this one covers a lot of ground on its own.