Model Detail
Upstage: Solar Pro 3
—Upstage: Solar Pro 3 is a large language model released by Upstage. And supports text->text inputs.
Upstage: Solar Pro 3 is priced at $0.15/M input tokens and $0.6/M output tokens. Operationally the model offers a 131K-token context window, which matters when sizing it for prompt-heavy or latency-sensitive workloads. At this input rate the model sits in the commodity tier and is suitable for high-volume workloads where per-call cost dominates the decision.
Upstage: Solar Pro 3 is published on Hugging Face but our pipeline has not yet captured architecture, license, or parameter-count metadata for this entry. The data is refreshed daily, so these fields typically populate within 24–48 hours of release.
Upstage: Solar Pro 3 is best fit for general-purpose chat and instruction-following workloads, and high-volume batch jobs where per-call cost dominates the budget. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
Solar Open 2 Technical Report
arXiv:2607.20062v2 Announce Type: replace Abstract: We present Solar Open 2, a 250B-A15B Mixture-of-Experts language model built for long-horizon agentic tasks, scaled up from Solar Open 1 (Solar Open 100B). To hold entire agent trajectories in a single context, Solar Open 2 reaches a 1M-token windo
A Deep Learning Framework for Predicting Solar EUV Irradiance During Significant Flares
arXiv:2607.19597v1 Announce Type: cross Abstract: We present FlareEUV, a multimodal deep learning framework for predicting daily extreme ultraviolet (EUV) irradiance at 6.5 nm over three consecutive days during significant solar flares, using multi-instrument observations from NASA's Solar Dynamics
Gritt exits stealth with $32 million for robots to build solar plants — then, everything else
Gritt is coming out of stealth with $34 million and plans to automate the hardest tasks on construction sites.
Distributed solar generation forecasting using attention-based deep neural networks for cloud movement prediction
arXiv:2411.10921v2 Announce Type: replace Abstract: Accurate forecasts of distributed solar generation are necessary to maintain grid stability amid the increased uptake of distributed solar photovoltaic (PV) systems. However, the high variability of solar generation over short time intervals (secon
Improving Wind and Solar Power Prediction with Efficient Wrapper-based Feature Selection: An Empirical Study
arXiv:2607.14024v1 Announce Type: cross Abstract: With rising global energy demand and growing awareness of climate change and its impacts, the share of renewable energies in the global energy mix continues to grow. Unlike conventional power generation, the output of renewable energy sources cannot
