AionLabs: Aion-2.0 vs Inception: Mercury 3

AionLabs: Aion-2.0 is a text model from AionLabs, released on 2026-02-23. Inception: Mercury 3 is a text model from Inception, released on 2026-10-16. Both are served on api14 under the text category, and both accept text input and return text output. Neither model takes images, audio or any other modality, so if your workload is purely language, both are candidates and the choice comes down to context, parameters and price rather than to what you can feed them.

The clearest split is context length. Aion-2.0 supports 131072 tokens, while Mercury 3 supports 1000000 tokens, so Mercury 3 holds roughly seven to eight times more text in a single request. That matters for whole-repository reads, long transcripts, large document sets or any prompt where you would otherwise chunk and stitch. On parameters the two overlap on the basics: both accept max_tokens, temperature, tool_choice, tools, response_format, reasoning and include_reasoning, so function calling and reasoning output are available on either side. Mercury 3 adds reasoning_effort, stop and structured_outputs, which gives finer control over how hard it reasons and over enforcing a schema. Mercury 3 is the cheaper of the two on both input and output, so it is also the lower cost per token, though the live price table on this page is the place to check current numbers.

For everyday work such as summarisation, classification, extraction, chat assistants and moderate code help, either model will do, and the deciding factors are usually context and cost. If your prompts fit comfortably inside 131072 tokens and you want a straightforward text model with tool calling and reasoning, Aion-2.0 is a reasonable default. If you routinely push very long inputs, or you need structured_outputs to guarantee a JSON shape, or you want to tune reasoning_effort, Mercury 3 is the better fit, and its larger context means fewer chunking pipelines to maintain. Because Mercury 3 is cheaper on input and output, it is also the sensible pick when volume is high and the task is not context bound.

Calling either model through api14 is the same operation. The API is OpenAI compatible, so you point your existing client at the api14 base URL, send your key, and set the model field. To use Aion-2.0, set the model id to "aion-labs/aion-2.0". To use Mercury 3, set it to "inception/mercury-3". Everything else in the request body, messages, temperature, tools, response_format and the rest, keeps the same shape, so switching between the two is a one line change and you can compare them on your own traffic without rewriting integration code.

There are still cases where Aion-2.0 is the better pick. If your inputs are short or medium length and you do not need structured_outputs or reasoning_effort, the extra context and parameters on Mercury 3 buy you nothing, and a simpler request surface is easier to reason about. If you have already tuned prompts and evaluation harnesses against Aion-2.0 and they pass, there is no reason to move unless cost or context forces it. Treat the two as interchangeable at the API level and choose per task: Aion-2.0 for contained text work, Mercury 3 when length, schema enforcement or price per token dominates.