LLM Tier Router & Token Economics Optimizer

Inspired by new frontier architectures (e.g. GPT-6 Sol and Luna compact tiers), this tool simulates multi-tier model routing, dynamic cascades, prompt cache hits, and latency budgets for enterprise AI workloads.

Architecture Presets:

Workload & Routing Policy

Routing Strategy How incoming requests are triage-routed across model sizes.

Daily Request Volume 50,000

Average Input Tokens 1,200

Includes system prompt, tool definitions, and user conversation history.

Average Output Tokens 350

Prompt Cache Hit Rate 45%

Cached prefix tokens receive 50%–80% cost reduction.

Escalation / Verification Threshold 0.78

Quality confidence cutoff: queries below this score re-run on a higher tier.

Monthly Estimated Spend $4,215 vs $18,450 baseline

Net Monthly Savings $14,235 77.2% reduction

p90 Latency Target 480 ms 1.9x faster TTFT

Effective Quality Index 96.4% 14.8% escalation rate

Routed Traffic Distribution

Luna (Compact / Edge)

Sol (Balanced Workhorse)

Frontier (Flagship / Astra)

55% Luna

30% Sol

15% Frontier

GPT-6 Luna (Fast) 55%

$0.15 / $0.60 per 1M tok • ~180 ms TTFT

$318 / mo

GPT-6 Sol (Balanced) 30%

$0.80 / $3.20 per 1M tok • ~420 ms TTFT

$1,128 / mo

Frontier / Astra Flagship 15%

$3.00 / $12.00 per 1M tok • ~920 ms TTFT

$2,769 / mo

Synthesized Routing Traces (Sample Batch)

Displaying recent 8 queries from simulation

Trace ID	Intent / Task	Complexity	Routed Model	Tokens In/Out	Latency	Cost	Status

Architecture simulation active. All metrics derived in browser.

Intelligent Tiering Dynamics

Modern production AI workflows increasingly rely on hierarchical routing rather than directing every query to monolithic frontier models. Compact tiers (such as Luna or nano-class models) excel at text classification, entity extraction, and structured output filtering with sub-200ms time-to-first-token (TTFT).

Mid-sized balanced tiers (such as Sol) deliver Astra-grade multi-modal context understanding and complex function calling at roughly one-fourth the token expense of top-tier frontier models.

Caching & Verification Economics

Prefix Caching: Repetitive system instructions and API schema definitions achieve up to an 80% discount when request traffic is routed to persistent warm inference pods.
Speculative Verification: The compact model drafts the candidate completion, while the supervisor tier validates token logits only if the confidence score violates the confidence threshold.
Failover Cascades: Queries generating malformed JSON schemas or low logit confidence automatically trigger seamless tier escalation without exposing errors to client apps.