# Perplexity Decider v1.3: Training, eval, and use cases

> Perplexity releases Decider v1.3 and Flash v1.3 decision models with improved accuracy and new locate feature for text and vision tasks.
- Title: Perplexity Decider v1.3: Training, eval, and use cases
- Summary: Perplexity releases Decider v1.3 and Flash v1.3 decision models with improved accuracy and new locate feature for text and vision tasks.
- Keywords: perplexity, decider, v1.3, locate, decision model, text classification, vision tasks, hugging face, openrouter, perplexity decisions api
- Source: Perplexity — https://perplexity.ai/hub/blog/perplexity-decider
- Read time: 2 min
- Topics: perplexity, decider, v1.3, locate, decision model, text classification, vision tasks, hugging face, openrouter, perplexity decisions api, technology, startups
## What is a decision model?
A decision model is a general-purpose classifier that returns probabilities for multiple questions about an input in a single forward pass.
## Decider v1.3 features
Decider v1.3 and Flash v1.3 models improve performance and robustness across text and vision tasks through bidirectional attention, better-curated training data, and refined training recipes.
## New locate feature
Locate extracts text from the state for named entity recognition, personal information identification, and extractive question-answering tasks without changing ordinary request handling.
## Training process
Decider is trained using three stages: generalist training, fine-grained task-specific tuning with LoRA, and vision-specific specialization to balance text and visual performance.
## Bidirectional attention
Models use bidirectional attention to let each token incorporate context from the entire input, improving performance and stability over causal attention.
## Locate training
Locate is trained with an adapted InfoNCE loss to push correct spans above a threshold while keeping wrong spans below, using a span head and LoRA adapters.
## Evaluation benchmarks
Decider v1.3 outperforms Jev 1.14 across 200+ internal tasks, multilingual classification, and vision tasks with 76.99% accuracy on five visual benchmarks.
## Real-world performance
In production, Decider v1.3 achieves 93.98% agreement with LLM judges across 2.3 million user queries, outperforming previous versions by 0.34 percentage points.
## Self-improving security
Decider identifies flaws in security rules, leading to 14 rule fixes in one day with 6 approved within two days, cutting rule volume by 52%.
## Monitoring RL rollouts
Decider monitors reinforcement learning rollouts by answering multiple-choice questions about failed tool calls and incorrect interpretations.
## Page quality filtering
Decider filters low-quality web pages by assessing content quality, domain, audience, and harmful information in real-time during crawling.
## Visual QA capabilities
Decider reviews style changes to web pages or apps, identifying clipped text, overlapping elements, broken layouts, and misleading charts.
## Use cases
Decider supports planning research tasks, browser navigation, and visual QA by estimating complexity before search begins and providing detailed state information.
## Model selection
Choose Decider v1.3 for nuanced judgments and Decider Flash v1.3 for high-volume routing where latency and cost are critical.
## Availability
Both models are available on Hugging Face under Apache license, through OpenRouter, and the Perplexity Decisions API.
## Key takeaway

Perplexity launches Decider v1.3 and Flash v1.3 decision models that outperform benchmarks in classification, reranking, and entity extraction tasks.