Model Detail
Cohere: Command R+ (08-2024)
—Cohere: Command R+ (08-2024) is a large language model released by Cohere. And supports text->text inputs.
Cohere: Command R+ (08-2024) is priced at $2.5/M input tokens and $10/M output tokens. Operationally the model offers a 128K-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 2024-03-31, so newer events will not be reflected in zero-shot answers without retrieval.
Cohere: Command R+ (08-2024) 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.
Autopilot-Preserving Residual Q-Learning with HJB-Inspired Finite-Action Risk Filtering for Fixed-Wing UAV Command Supervision
arXiv:2606.01397v1 Announce Type: cross Abstract: A fixed-wing UAV must hold airspeed, altitude, and heading references under wind, gusts, and turbulence, channels coupled so that correcting one can degrade another. Classical autopilots stabilize the airframe well but adapt poorly when a hard crossw
MiCU: End-to-End Smart Home Command Understanding with Large Language Model
arXiv:2606.01099v1 Announce Type: cross Abstract: Command understanding systems in smart home ecosystems can automate device control and substantially improve user experience. However, while they perform well on precise utterances (e.g., "turn on the bedroom light"), they struggle with ambiguous or
ClawForge: Generating Executable Interactive Benchmarks for Command-Line Agents
arXiv:2605.14133v2 Announce Type: replace Abstract: Interactive agent benchmarks face a tension between scalable construction and realistic workflow evaluation. Hand-authored tasks are expensive to extend and revise, while static prompt evaluation misses failures that only appear when agents operate
Commanding Humanoid by Free-form Language: A Large Language Action Model with Unified Motion Vocabulary
arXiv:2511.22963v3 Announce Type: replace-cross Abstract: Enabling humanoid robots to follow free-form natural language commands is a critical step toward seamless human-robot interaction and general-purpose embodied AI. However, existing methods remain limited, often constrained to simple instructi
UNCOM: Zero-shot Context-Aware Command Understanding for Tabletop Scenarios
arXiv:2410.06355v3 Announce Type: replace-cross Abstract: This paper presents UNCOM, a novel hybrid framework for interpreting natural human commands in tabletop scenarios. The system integrates multiple sources of information -- speech, gestures, and scene context -- to extract structured, actionab
