AionLabs: Aion-2.0 vs LiquidAI: LFM2.5-24B-A2B

AionLabs: Aion-2.0 is a text model from AionLabs, released on 2026-02-23. LiquidAI: LFM2.5-24B-A2B is a text model from LiquidAI, released on 2026-10-13. Both are served through api14 as chat models, and both take text in and return text out, so neither one adds image, audio or video handling to your pipeline. The choice between them comes down to context size, sampling controls and price rather than to a difference in modality.

The two context windows are close enough that the difference is unlikely to decide anything for you: Aion-2.0 offers 131072 tokens and LFM2.5-24B-A2B offers 128000. Both are large enough for long documents, multi file code review or extended conversation history. The more meaningful split is in the parameter surface. LFM2.5-24B-A2B exposes a much wider set of sampling and decoding controls, including frequency_penalty, presence_penalty, repetition_penalty, min_p, top_k, seed, stop, logprobs and top_logprobs, and it is the only one of the two that supports structured_outputs. Aion-2.0 supports a narrower set: include_reasoning, max_tokens, reasoning, response_format, temperature, tool_choice, tools and top_p. Both accept tools and both accept reasoning related parameters, so function calling and reasoning style requests are available on either side.

For most application work the practical question is how much control you need over generation. If you want deterministic sampling, token level log probabilities, or schema constrained output, LFM2.5-24B-A2B is the one to call, because structured_outputs and the logprob parameters are only on that model. If your workload is straightforward chat, summarisation, extraction or tool calling with a fixed temperature and top_p, Aion-2.0 covers it and gives you a slightly larger context window. Cost also leans one way: LFM2.5-24B-A2B is the cheaper option on both input and output, so at volume it is the more economical choice, while Aion-2.0 sits at a higher price point. Neither model is flagged as uniquely capable at reasoning or tool use in the comparison data, so do not pick on that basis alone.

Calling either model through api14 works the same way, because the API is OpenAI compatible. You point your existing OpenAI client at the api14 base URL, send your key, and set the model field to the id you want. To switch from one to the other you change only that string, between "aion-labs/aion-2.0" and "liquid/lfm-2.5-24b-a2b". The request shape, the messages array and the response parsing stay identical, so you can route between them per request, run one as a fallback for the other, or A/B them behind a flag without touching the rest of your code. Just remember that any parameter you send must be supported by the model you are calling, so a request using structured_outputs or logprobs will only work against LFM2.5-24B-A2B.

There are still cases where Aion-2.0 is the better pick. If you are already running it in production and your prompts are tuned to its behaviour, or if you want the marginally larger context window and do not need the extra sampling controls, there is no strong reason to move. It also remains a reasonable default when you want a single model id for general text work and are not optimising hard on price. The honest summary is that LFM2.5-24B-A2B gives you more knobs and a lower cost, while Aion-2.0 gives you a slightly longer context and a simpler parameter set, and both are reachable by changing one string in an otherwise unchanged OpenAI style call.