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 128K-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: A Self-Optimizing Open-Ended Autonomous Agent for Lifelong Learning and Continual Adaptation
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Privacy-Preserving Generation Fraud Detection for Distributed Photovoltaic Systems: A Solar Irradiance-Fused Federated Learning Framework
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A Quantum Inspired Variational Kernel and Explainable AI Framework for Cross Region Solar and Wind Energy Forecasting
arXiv:2605.09032v1 Announce Type: cross Abstract: Reliable short horizon forecasting of solar and wind generation is a structural prerequisite of any modern power system yet most published forecasters are tuned and evaluated on a single climatic regime and most algorithmic novelty has been concentra
AI and Open-data Driven Scalable Solar Power Profiling
arXiv:2605.02738v1 Announce Type: new Abstract: Solar photovoltaic (PV) deployment is expanding rapidly, yet detailed, up-to-date information on the spatial distribution and capacity of rooftop PV remains limited. This paper presents an open, scalable framework for detecting solar panels from open d
Integrating Weather Foundation Model and Satellite to Enable Fine-Grained Solar Irradiance Forecasting
arXiv:2603.14845v3 Announce Type: replace-cross Abstract: Accurate day-ahead solar irradiance forecasting is essential for integrating solar energy into the power grid. However, it remains challenging due to the pronounced diurnal cycle and inherently complex cloud dynamics. Current methods either l
SolarTformer: A Transformer Based Deep Learning Approach for Short Term Solar Power Forecasting
arXiv:2604.24306v1 Announce Type: cross Abstract: Accurate forecasting of solar power output is essential for efficient integration of renewable energy into the grid. In this study, an attention-based deep learning model, inspired by transformer architecture, is used for short-term solar power forec
