Post
1524
🎯 Introducing Chayan: A Calibrated 4-Model LLM Router Achieving 69% Accuracy on RouterArena
We're excited to share Chayan, a cost-efficient LLM router that intelligently routes queries between 4 models to maximize accuracy while minimizing cost. Chayan just submitted to the RouterArena leaderboard and achieved 69.05% accuracy on the benchmark!
🔗 Model: adaptive-classifier/chayan
🔗 Dataset: RouteWorks/RouterArena
📊 Performance Highlights
Chayan achieves impressive results on the RouterArena benchmark:
• 69.05% accuracy (would rank #1 on current leaderboard)
• $0.333 per 1K queries
• +12.07pp improvement over all-mini baseline (56.98%)
• 99% of perfect 2-model oracle performance at 57% lower cost
Compared to our previous 2-model router (61.43% accuracy), Chayan delivers +7.62pp improvement through smarter 4-model routing.
🧠 How It Works
Chayan uses an Adaptive K-NN classifier with prototype memory to route between 4 models:
• openai/gpt-4o-mini (fast & cheap)
• google/gemini-2.5-flash-lite (balanced)
• google/gemini-2.5-flash (capable)
• openai/gpt-4o (most powerful)
🚀 Getting Started
You can use Chayan directly from HuggingFace:
from adaptive_classifier import AdaptiveClassifier
Load Chayan
router = AdaptiveClassifier.load("adaptive-classifier/chayan")
Route a query
query = "What is the capital of France?"
predictions = router.predict(query, k=4)
Get top model recommendation
best_model = predictions[0][0]
print(f"Recommended model: {best_model}")
Built with the adaptive-classifier library: https://github.com/codelion/adaptive-classifier
We're excited to share Chayan, a cost-efficient LLM router that intelligently routes queries between 4 models to maximize accuracy while minimizing cost. Chayan just submitted to the RouterArena leaderboard and achieved 69.05% accuracy on the benchmark!
🔗 Model: adaptive-classifier/chayan
🔗 Dataset: RouteWorks/RouterArena
📊 Performance Highlights
Chayan achieves impressive results on the RouterArena benchmark:
• 69.05% accuracy (would rank #1 on current leaderboard)
• $0.333 per 1K queries
• +12.07pp improvement over all-mini baseline (56.98%)
• 99% of perfect 2-model oracle performance at 57% lower cost
Compared to our previous 2-model router (61.43% accuracy), Chayan delivers +7.62pp improvement through smarter 4-model routing.
🧠 How It Works
Chayan uses an Adaptive K-NN classifier with prototype memory to route between 4 models:
• openai/gpt-4o-mini (fast & cheap)
• google/gemini-2.5-flash-lite (balanced)
• google/gemini-2.5-flash (capable)
• openai/gpt-4o (most powerful)
🚀 Getting Started
You can use Chayan directly from HuggingFace:
from adaptive_classifier import AdaptiveClassifier
Load Chayan
router = AdaptiveClassifier.load("adaptive-classifier/chayan")
Route a query
query = "What is the capital of France?"
predictions = router.predict(query, k=4)
Get top model recommendation
best_model = predictions[0][0]
print(f"Recommended model: {best_model}")
Built with the adaptive-classifier library: https://github.com/codelion/adaptive-classifier