AionLabs: Aion-2.0 vs Meituan: LongCat 2.0

AionLabs: Aion-2.0 is a text model from AionLabs, released on 2026-02-23. Meituan: LongCat 2.0 is a text model from Meituan, released on 2026-07-20. Both are served on api14 under the text category, and both take text in and return text out, so neither one gives you image, audio or video handling. The choice between them comes down to context size, parameter surface and price rather than to a difference in modality.

The clearest split is context length. LongCat 2.0 accepts up to 1,048,756 tokens, while Aion-2.0 accepts up to 131,072 tokens, so LongCat 2.0 is the one to reach for when a single request has to carry a very large body of material, such as a long repository, a stack of documents or a lengthy transcript. On sampling and control parameters the two are not identical either. Aion-2.0 exposes include_reasoning, max_tokens, reasoning, response_format, temperature, tool_choice, tools and top_p. LongCat 2.0 exposes those same broad areas plus frequency_penalty, logit_bias, min_p, presence_penalty, repetition_penalty, seed, stop and top_k, which gives you finer control over repetition, determinism and decoding. Both support tools and reasoning, so neither has an exclusive claim on tool calling or on reasoning output.

For everyday work where the prompt and the answer both fit comfortably inside a moderate window, Aion-2.0 is a reasonable default: it is a capable text model with the parameters most applications actually set, and its smaller context is not a limitation if you are summarizing, classifying, extracting or generating short to medium responses. Pick LongCat 2.0 when the task is defined by volume or by control. Retrieval over a large corpus, whole codebase analysis, long multi document synthesis and anything where you want to pin a seed or tune penalties belong on LongCat 2.0. If your workload is sensitive to spend, note that LongCat 2.0 is the cheaper of the two on both input and output, so high volume jobs lean its way as well.

Calling either model through api14 works the same way, because the gateway is OpenAI compatible. You point your client at the api14 base URL, send your key, and use the chat completions style request you already use. The only thing that changes between the two is the model id string: use "aion-labs/aion-2.0" for AionLabs and "meituan/longcat-2.0" for Meituan. Your messages array, your tool definitions and your other request fields stay as they are, subject to the parameters each model accepts. That means you can keep one code path and switch models by swapping a single value, which makes it easy to route short requests to one model and long ones to the other.

There is still a case for Aion-2.0 even when context is not the constraint. If your prompts are modest in size and you want the parameter set that most OpenAI compatible tooling assumes by default, Aion-2.0 keeps things simple, and it does not ask you to think about the extra decoding knobs LongCat 2.0 offers. Neither model is presented here as better on tools, structured output or reasoning, so those are not reasons to prefer one. Decide on context length, on how much sampling control you need, and on which side is cheaper for your traffic, then set the model id accordingly.