Model Detail
Perplexity: Sonar Deep Research
—Perplexity: Sonar Deep Research is a large language model released by Perplexity. And supports text->text inputs.
Perplexity: Sonar Deep Research is priced at $2/M input tokens and $8/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.
Perplexity: Sonar Deep Research 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.
Perplexity: Sonar Deep Research 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.
BenthicDINO: Physics-Informed Self-Distillation for View-Invariant Side-Scan Sonar Representations
arXiv:2608.23215v2 Announce Type: replace-cross Abstract: Automated interpretation of side-scan sonar (SSS) imagery is traditionally hindered by view-dependence, as recorded intensities inextricably conflate intrinsic seabed reflectivity with transient acquisition geometries and acoustic artifacts.
BatSLAM 2.0: Sequence-Verified Sonar Place Recognition in a Robust Pose Graph
arXiv:2609.40085v1 Announce Type: cross Abstract: Echolocating bats can navigate dark and cluttered spaces using echolocation. Over a decade ago, BatSLAM showed that a robot with a biomimetic binaural sonar can build a topological map of the environment, by recognizing places from the received acous
Beyond Pixel Similarity: Task-Aware Evaluation of GAN-Based Synthetic Sonar Data for Robotic Perception
arXiv:2609.18100v1 Announce Type: cross Abstract: Synthetic data can reduce the cost of collecting and annotating training data for robotic perception, but generating sensor observations that preserve the characteristics relevant to downstream perception remains challenging, particularly for sonar i
SonarLLM: A Native Sonar--Optical Multimodal Large Language Model for Underwater Perception
arXiv:2608.24325v1 Announce Type: new Abstract: Reliable underwater perception requires complementary sensing under variable visibility. Optical cameras capture appearance and semantics but degrade rapidly with turbidity, whereas imaging sonar preserves geometry while exhibiting distinct range-azimu
A Comparison of Data Augmentation Methods for Training Deep Neural Networks on Synthetic Aperture Sonar
arXiv:2607.23770v3 Announce Type: replace Abstract: In this work we study Automatic Target Recognition (ATR) for Synthetic Aperture Sonar (SAS) data with a focus on deep neural networks (DNNs). The main challenge in training DNNs for SAS-ATR arises from the limited quantity of labeled target example
