AionLabs: Aion-2.0 vs inclusionAI: Ling 3.0 Flash VL

AionLabs: Aion-2.0 is a text model from AionLabs, released on 2026-02-23. InclusionAI: Ling 3.0 Flash VL is a text model from inclusionAI, released on 2026-09-10. Both are served through api14 under the text category, and both accept a single model id string when you call them. The main structural difference is that Aion-2.0 handles text only, while Ling 3.0 Flash VL is built to take in images and video alongside text.

On capabilities, the two share the same context window of 131072 tokens, so neither gives you more room for long prompts or large documents. The difference is in modalities and tuning knobs. Aion-2.0 accepts text input and returns text output. Ling 3.0 Flash VL accepts text, image and video input and returns text output, which makes it the only one of the two that can look at visual material. Ling 3.0 Flash VL also exposes a wider set of sampling and control parameters, including frequency penalty, logit bias, min p, presence penalty, repetition penalty, seed, stop, top k and structured outputs. Aion-2.0 supports a narrower set: include reasoning, max tokens, reasoning, response format, temperature, tool choice, tools and top p. Structured outputs are listed only for Ling 3.0 Flash VL, so if you need schema constrained responses, that is the side to check first. Both list tool and tool choice support, so function calling is not a differentiator here.

For picking between them, start with the input type. If your task is pure text, such as summarising documents, drafting code, running a tool calling loop or generating structured JSON, Aion-2.0 covers it and is the simpler surface. If your task involves screenshots, diagrams, photos or video frames, Ling 3.0 Flash VL is the one that can accept them, since Aion-2.0 takes text only. If you need fine grained sampling control, such as a fixed seed for reproducibility or penalties to steer repetition, Ling 3.0 Flash VL offers those parameters and Aion-2.0 does not. On cost, Ling 3.0 Flash VL is the cheaper option for both input and output, so for high volume text work it is also the more economical pick, though you should read the live price table for the actual numbers.

Calling either model through api14 works the same way because the API is OpenAI compatible. You point your client at the api14 base URL, send your request to the chat completions endpoint with your api14 key, and set the model field to either "aion-labs/aion-2.0" or "inclusionai/ling-3.0-flash-vl". Everything else in the request body, such as messages, temperature, max tokens and tools, keeps the same shape. To switch models you change only that model id string. If you move from Aion-2.0 to Ling 3.0 Flash VL, you can additionally send image or video content parts in your messages and use the extra parameters it supports, but a plain text request needs no other edits.

There is still a case for Aion-2.0 even though Ling 3.0 Flash VL is cheaper and handles more input types. If your pipeline is text only and you want the smallest set of parameters to reason about, Aion-2.0 keeps the request surface tight. It also supports reasoning and include reasoning, so for text tasks that benefit from a reasoning pass it remains a valid choice. Neither model is strictly better across the board; the decision comes down to whether you need vision input and the wider parameter set, in which case Ling 3.0 Flash VL wins, or whether a text only model at a comparable context length is enough for your workload.