Upstage: Solar Decide Flash

Solar Decide Flash is a model from Upstage, listed here under the identifier upstage/solar-decide-flash. It belongs to the decisions category, which is its main and only category, and it is positioned as Upstage's low-latency structured decision model. It is a faster variant of Solar Decide, built on Solar Mini 4 and served through the System One API at /v1/systemone. Upstage released it on 2026-10-08.

The model takes text as its input and returns decisions as its output, so it is not a general chat or free-form text generator. Its context window is 524,288 tokens, and the maximum output is 471,859 tokens. Those two numbers define the practical shape of a request: a large body of text can be supplied as context, and a correspondingly large decision result can come back. The provider description indicates that the model produces structured decisions rather than open-ended prose.

Because the output modality is decisions, the natural fits are workloads where a system needs a deterministic or structured judgement over supplied text rather than a written answer. Examples that follow from that shape include classifying or routing incoming text, selecting among predefined options, and producing structured decision output that downstream code can act on. The long context window also suits cases where the decision depends on a large amount of reference material supplied in the same request. It is less suited to tasks whose goal is a conversational reply or generated narrative, since text output is not what it produces.

You call the model through api14's OpenAI-compatible API using the model id "upstage/solar-decide-flash". No request parameters are listed as supported, so the request should rely on the standard chat structure and this model id rather than on optional tuning parameters such as temperature, top_p, or max tokens. Since no parameters are declared, do not assume any are accepted; send the model id and the input, and treat the decision output as the result. Because the provider serves this model through the System One endpoint, the underlying route differs from a plain text model, but from your side the model id is what selects it.

Given the empty list of supported parameters, this model may not fit work that depends on fine control over sampling or output shaping, and a text-output model would be a better choice when the task requires written language rather than a decision. The published maximum output of 471,859 tokens is large but still smaller than the 524,288 token context window, so very long inputs combined with very long outputs require attention to both limits.