Token Maxing unlocks up to 85% savings on your daily GPT usage — no upfront cost, no lock‑in.
Drop-in API proxy delivering 70% to 85% token cost reduction via real-time cascading, prompt-caching, and hardware arbitrage. 100% risk-free: We take a 30% performance fee of documented savings. 0 kr. saved = 0 kr. paid.
Calculate Your Documented Net Savings
Slide to your organization's monthly token volume and model complexity to preview your 70/30 split.
Ornn Token Price Index (OTPI)
Real-time benchmark spreads across top frontier reasoning engines, dynamically calibrated against Artificial Analysis Intelligence Index & Cost Data.
| Model / Inference Engine | Provider Retail Rate | Tokenmaxing Cleared Rate | Effective Arbitrage Spread | Arbitrage Method |
|---|---|---|---|---|
| $4.50 / $22.50 | $1.12 / $5.60 | -75.1% | Dynamic Ephemeral Prefix Cache + Speculative Fallback | |
| $15.00 / $75.00 | $3.75 / $18.75 | -75.0% | Multi-Token Speculative Drafting & KV Compression | |
| $3.50 / $17.50 | $0.90 / $4.50 | -74.3% | Sub-agent Reasoning Pruning & Cascade Routing | |
| $3.00 / $15.00 | $0.85 / $4.20 | -71.6% | Dynamic Ephemeral Cache + Speculative Thinking | |
| $3.00 / $15.00 | $0.85 / $4.20 | -71.6% | Prefix KV Cache Optimization | |
| $0.80 / $4.00 | $0.16 / $0.80 | -80.0% | Hardware Multiplexing & Batch Routing | |
| $12.00 / $48.00 | $2.88 / $11.50 | -76.0% | Deep Ephemeral KV Partitioning & Speculative Routing | |
| $8.00 / $32.00 | $1.95 / $7.80 | -75.6% | Chain-of-Thought Pruning & Prompt Cache Arbitrage | |
| $4.00 / $16.00 | $0.98 / $3.90 | -75.5% | Dynamic Model Cascading & Fast-Path Inference | |
| $1.50 / $6.00 | $0.32 / $1.30 | -78.7% | Edge Cache Multiplexing & Quantized Speculation | |
| $75.00 / $150.00 | $18.50 / $37.50 | -75.3% | Deep Cache Multiplexing & Selective Gateway | |
| $1.10 / $4.40 | $0.28 / $1.10 | -75.0% | Reasoning Token Pruning & KV Compression | |
| $15.00 / $60.00 | $3.75 / $15.00 | -75.0% | CoT Speculative Compression & Drafting | |
| $2.50 / $10.00 | $0.62 / $2.50 | -75.2% | Low-Entropy Cascading + Prefix Cache | |
| $0.15 / $0.60 | $0.03 / $0.12 | -80.0% | Edge Quantized Neocloud Routing | |
| $0.25 / $1.00 | $0.05 / $0.20 | -80.0% | TPU v6e Sovereign Cluster Direct Routing | |
| $0.08 / $0.30 | $0.015 / $0.06 | -81.3% | Sub-millisecond Hardware Batching | |
| $1.25 / $5.00 | $0.28 / $1.15 | -77.0% | TPU v5p Tensor Cores Cluster Routing | |
| $0.10 / $0.40 | $0.02 / $0.08 | -80.0% | High-Throughput Multimodal KV Compression | |
| $1.25 / $5.00 | $0.31 / $1.25 | -75.0% | Long-Context Ephemeral Cache Reads | |
| $4.50 / $20.00 | $1.10 / $4.80 | -75.6% | Memphis Colossus Supercluster Spot Partition | |
| $3.00 / $15.00 | $0.75 / $3.75 | -75.0% | Colossus 100k H100 Supercluster Spot Interconnect | |
| $0.30 / $1.50 | $0.06 / $0.30 | -80.0% | FP8 Kernel Multiplexing & Speculative Execution | |
| $2.00 / $10.00 | $0.50 / $2.50 | -75.0% | Low-Latency Streaming KV Arbitrage | |
| $0.65 / $2.60 | $0.15 / $0.62 | -76.2% | Native Multi-Head Latent Attention Direct KV Spot Routing | |
| $0.55 / $2.19 | $0.14 / $0.55 | -74.5% | Neocloud Sovereign Inference Cluster (SXM5) | |
| $0.27 / $1.10 | $0.07 / $0.28 | -74.1% | Multi-Head Latent Attention (MLA) Direct Cache | |
| $0.60 / $2.20 | $0.14 / $0.50 | -76.7% | Bare-Metal H100/H200 NVLink Cluster Speculative Decoding | |
| $0.50 / $1.80 | $0.12 / $0.42 | -76.0% | Multi-Node vLLM KV Prefix Optimization | |
| $0.15 / $0.50 | $0.03 / $0.10 | -80.0% | Edge FP8 Engine Quantized Streaming | |
| $0.40 / $0.90 | $0.09 / $0.20 | -77.8% | FP8 vLLM Engine + Dedicated Bare-Metal Pool | |
| $2.50 / $6.50 | $0.62 / $1.62 | -75.2% | Multi-Node 8xH100 NVLink Direct Interconnect | |
| $0.60 / $2.40 | $0.14 / $0.55 | -76.7% | Sovereign Long-Context KV Cache Partitioning | |
| $0.50 / $2.00 | $0.12 / $0.48 | -76.0% | Dynamic MoE Routing & Sparse Kernel Multiplexing | |
| $0.45 / $1.60 | $0.10 / $0.35 | -77.8% | Neocloud Bare-Metal Spot Arbitrage | |
| $0.20 / $0.80 | $0.04 / $0.16 | -80.0% | Fast-Path Kernel Compilation | |
| $0.15 / $0.50 | $0.03 / $0.10 | -80.0% | Edge Quantized TensorRT-LLM | |
| $0.11 / $0.28 | $0.02 / $0.06 | -78.6% | Sovereign Asian Datacenter Spot Routing | |
| $0.20 / $0.60 | $0.04 / $0.12 | -80.0% | High-Throughput Batch Inference Compression | |
| $2.20 / $5.50 | $0.52 / $1.30 | -76.4% | Blackwell GB200 NVLink Cluster Speculative TensorRT-LLM | |
| $1.80 / $4.50 | $0.45 / $1.12 | -75.1% | TensorRT-LLM Optimized Kernel Multiplexing | |
| $3.85 / hr | $1.89 / hr | -50.9% | Nordic Green Datacenter Spot Arbitrage |
The Three Pillars of Token Arbitrage
How Tokenmaxing guarantees identical output quality while drastically reducing inference bills.
Dynamic Model Cascading
High-complexity queries receive full frontier compute. Low-entropy extraction, summarization, and simple conversational turns automatically cascade down to ultra-efficient models without loss of fidelity.
- Sub-millisecond prompt complexity parser
- Automated fallback on ambiguity
- Zero loss in benchmark accuracy
Automated Prompt-Caching
Massive system prompts, company schemas, and chat histories are transformed into ephemeral caches, cutting read costs by up to 90% and latency by 80%.
- Zero code changes required
- Automated cache-breakpoint injection
- High multi-tenant memory reuse
Dual-Rate Telemetry
Every API response includes documented proofs showing baseline provider cost vs. cleared cost, maintaining 100% auditable accounting for our 70/30 shared-savings model.
- Real-time JSON audit telemetry
- End-of-month verified reconciliation
- 0 kr. saved = 0 kr. invoiced
100% Drop-In Replacement
No SDK rewrites. Simply point your existing client to our high-performance endpoint.
# Simply override base_url in your standard OpenAI/Anthropic client
from openai import OpenAI
client = OpenAI(
base_url="https://api.tokenmaxing.online/v1",
api_key="tk_live_your_tokenmaxing_key"
)
response = client.chat.completions.create(
model="claude-3-5-sonnet", # Cascades automatically if simple
messages=[
{"role": "system", "content": "You are a specialized enterprise AI agent."},
{"role": "user", "content": "Analyze our quarterly EBITDA variance."}
]
)
print(response.choices[0].message.content)
# Real-time documented savings metrics returned in response:
print(response.tokenmaxing_metrics)