Model Detail
Arcee AI: Maestro Reasoning
—Arcee AI: Maestro Reasoning is a large language model released by Arcee-ai. And supports text->text inputs.
Arcee AI: Maestro Reasoning is priced at $0.9/M input tokens and $3.3/M output tokens. Operationally the model offers a 131K-token context window, which matters when sizing it for prompt-heavy or latency-sensitive workloads. Pricing in this range is the working middle of the API market — neither the cheapest nor the most expensive option per token, so cost-fit is usually a function of how much output you generate.
The published knowledge cutoff is 2025-03-31, so newer events will not be reflected in zero-shot answers without retrieval.
Arcee AI: Maestro Reasoning is best fit for general-purpose chat and instruction-following workloads. 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.
It Takes a MAESTRO To Prune Bad Experts
arXiv:2607.08601v1 Announce Type: new Abstract: Sparsely-activated Mixture-of-Experts (MoE) language models achieve remarkable inference efficiency by activating only a small fraction of parameters per token, yet their full expert banks reside in memory at all times, creating a prohibitive deploymen
Maestro Order: A Model-Agnostic Orchestration Harness
arXiv:2606.23983v1 Announce Type: cross Abstract: A single forward pass of a capable model is a fast, fluent, and unreliable problem-solver: it is right often enough to be useful and wrong often enough to be dangerous; in language models, such confident errors are known as hallucinations. We present
Maestro: Reinforcement Learning to Orchestrate Hierarchical Model-Skill Ensembles
arXiv:2605.22177v1 Announce Type: cross Abstract: The proliferation of large language models (LLMs) and modular skills has endowed autonomous agents with increasingly powerful capabilities. Existing frameworks typically rely on monolithic LLMs and fixed logic to interface with these skills. This giv
MAESTRO: Meta-learning Adaptive Estimation of Scalarization Trade-offs for Reward Optimization
arXiv:2601.07208v2 Announce Type: replace-cross Abstract: Group-Relative Policy Optimization (GRPO) has emerged as an efficient paradigm for aligning Large Language Models (LLMs), yet its efficacy is primarily confined to domains with verifiable ground truths. Extending GRPO to open-domain settings
