Qwen3 14B
Neural Network
Qwen3 14B is a Qwen model with a switchable reasoning mode: suitable for complex tasks and coding.
Specifications
- Input
- text, images, documents
- Output
- text
- Context size
- 40 960 tokens
- Max answer length
- 16 384 tokens
- Reasoning effort
- low, medium, high
- Capabilities
- reasoning, function calling
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works Qwen3 14B?
Qwen3 14B is a dense 14.8B parameter causal model from the Qwen3 line, designed to confidently handle both dialogue and reasoning tasks. Its key feature is the ability to switch between a reasoning mode, where the model unfolds a chain of thought, and a standard fast response mode: you choose what matters most for your specific request—depth or speed. In BotHub, Qwen3 14B is accessible without a VPN or foreign card; payment is made with Russian cards in rubles, only for tokens actually consumed, which do not expire. With over 250 models available in the same window, you can compare responses or switch models in a few clicks, or use the unified OpenAI-compatible API without rewriting your integration. Chats are encrypted, and for companies, we offer contracts, invoices, and an admin panel with limits. It is useful for breaking down logical or mathematical problems step-by-step, writing and debugging code, maintaining long dialogues, summarizing voluminous materials, or building an assistant prototype via API.Frequently asked questions about Qwen3 14B
You can use generated results for commercial purposes. You own all rights to the content you create. The only restriction: make sure your prompt does not include copyrighted third-party material. You are responsible for respecting the rights to any input data.
Qwen3-14B is a text model from Qwen with a 40,960 token context. It is suitable for working with code: writing functions, refactoring, and debugging, as well as for reasoning, STEM tasks, and document analysis. You can input images and documents and receive structured text in response.
The model generates up to 16,384 tokens per response—enough for a large code module, detailed analysis, or long material. The total context window is 40,960 tokens, shared between your request, dialogue history, and the response, so it is better to provide large input data in parts.
Yes, the model has a reasoning mode: before the final answer, it breaks down the task step-by-step. This significantly helps with math, logic, requirements analysis, and finding bugs in code. Note that reasoning consumes output tokens from the total response limit.
Yes, the model supports function calling and structured JSON output, so it can be embedded into agents and pipelines with external tools. In BotHub, it is available via a unified OpenAI-compatible API: you can switch models without rewriting your integration.
The model accepts images and documents: you can ask it to analyze a diagram, interface screenshot, or file content and describe it in text. Audio and video are not listed among the supported input formats, so for such materials, it is better to prepare a text transcript or description in advance.
Qwen3 is a multilingual family, and Russian is supported, although we do not have specific quality measurements by language in our data. We recommend testing the model on your task and comparing phrasing and terminology. In BotHub, this is convenient: the interface is in Russian, payment is via Russian cards, and access is without VPN.