Home
Articles
Home
Coval
Compared to Sesame
Coval vs Sesame
Comparing the features of Coval to Sesame
Feature
Coval
Sesame
Capability Features
Audio Inputs
Audio Replay
Built-in Metrics
latency
accuracy
tool-call effectiveness
instruction compliance
Consistent Personality
Context Awareness
Conversational Dynamics
Conversational Speech Generation
Custom Environments
Custom Metrics
Dataset Size
1 million hours
Emotional Intelligence
Evaluation Suite
Fully Customizable Voice
Human-in-the-Loop
Model Sizes
Tiny: 1B backbone, 100M decoder
Small: 3B backbone, 250M decoder
Medium: 8B backbone, 300M decoder
Multiple Speaker Handling
Objective Metrics
Word Error Rate
Speaker Similarity
Homograph Disambiguation
Pronunciation Consistency
Partial Multilingual Support Planned
Planned for 20+ languages
Performance Alerts
Production Call Monitoring
Prompt Change Re-simulation
Pronunciation Correction
Regression Tracking
Scenario-Based Testing
Sequence Length
2048
Simulate Conversations
Single-Stage Model
Streaming Alerts
Subjective Metrics
Comparative Mean Opinion Score
Text and Audio Input
Text
Audio
Text Chat Compatible
Training Epochs
5
Transcripts as Input
Voice AI Features
Workflow Tracing
Workflow-Based Simulation
Integration Features
Developer-Focused Integrations
GitHub Release
LLama Architecture Backbone
Mimi Split-RVQ Tokenizer
Limitation Features
Cannot Model Conversation Structure
English Language Dominance
Memory Bottleneck in Training
No Mentioned Integrations
No Pre-trained Language Model Use
No Pricing Information
Real-Time Generation Delay
RVQ time-to-first-audio scales poorly
Pricing Features
Free Preview
Free Trial/Demo
Open Source
Apache 2.0