Ling 3.0 Flash
Neural Network
Ling 3.0 Flash API is a 124B parameter Mixture-of-Experts model designed for token efficiency and agentic inference.
Max answer length
(in tokens)
Context size
(in tokens)
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works Ling 3.0 Flash?
Ling 3.0 Flash is a Mixture-of-Experts language model: it has 124 billion parameters, but only about 5.1 billion are activated per token, making each response more computationally efficient. Developers prioritized two things: token economy and agentic inference in production, i.e., long call chains where the model works in a series of steps rather than a single response. In BotHub, Ling 3.0 Flash is accessible without a VPN or foreign card: you pay with a Russian card in rubles and only for the tokens actually used, which do not expire. Over 250 models are available in the same window, allowing you to compare responses and switch, while a unified OpenAI-compatible API lets you change models in your code without rewriting integrations. Traffic is encrypted via AES-GCM; for companies, we offer contracts, invoices, EDI, and an admin panel with limits. It is useful when you need to build an agent with external tools, run mass processing of similar requests, assist with code and log analysis, or maintain a constantly running service where the cost per request matters.Frequently asked questions about Ling 3.0 Flash
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.
This is a text model from inclusionai with a 262,144 token context. It analyzes long documents, works with code, and answers questions about uploaded files and images. It is suitable for analytics, support, text preparation, and scenarios where you need to retain a large volume of material.
In a single response, the model outputs up to 32,768 tokens—dozens of pages of coherent text. This is enough for a comprehensive analysis, a long article, or a large code fragment. If there is more material, continue generation with the next request: the 262,144 token context helps maintain the thread.
Yes, the reasoning mode is confirmed by our data: before the final answer, the model builds a chain of steps. This helps with logic, mathematics, multi-step analysis, and code debugging. On simple requests, the difference is small, but on complex conditions, accuracy is noticeably higher.
Yes, it supports both: the model performs function calling and returns structured JSON. It is easy to connect to external services, databases, and internal tools. In BotHub, this works via a unified OpenAI-compatible API, so the model can be changed without rewriting integrations.
Images and documents—yes, the model accepts them as input along with text: ask it to analyze a screenshot, diagram, table, or an entire file. Audio and video are not noted in our data. The model responds with text—generation of other formats is not specified.
We do not have specific data on the quality of Russian language performance, so we will not make any promises. Test it with your own task: send a typical request and compare it with another model in the same window. The BotHub interface is in Russian, payment is via Russian cards, and no VPN is required.