> This is the markdown version of https://www.maniac.ai/. Visit the full page for interactive content. \[ MANIAC \] # high throughput task specialized background agents Outperform Opus 4.6 on niche repetitive tasks. 1/50 the cost of large frontier models, so your agents can run 24/7. [SLM Audit - See how an SLM can improve your product](/slm-audit) [Book a Demo](/book-demo)[Agent Docs](https://docs.maniac.ai/agent-setup/agent-setup) \[ CALCULATOR \] ## Same Budget. 50x More Throughput. See what happens when you swap GPT-5.2 for Maniac-optimized models — same quality, a fraction of the cost. Agent calls per day1M 10K100K500K1M5M10M Daily cost GPT-5.2$10K/day Maniac$200/day Annual with GPT-5.2 $3.6M per year Annual with Maniac $73K per year You save $3.6M per year 50x cheaper maniac — architecture live Client Your Agents Background tasks at scale calls today 2,847,291 req res Maniac Engine Router Routes each request to optimal model variant Models Fine-tuned, distilled, compressed for your domain Optimization Loop traffic → curate → finetune → eval → promote → repeat Opus 4.6 quality · 1/50 of the cost \>Opus 4.6 on-domain accuracy 1/50 of the cost <400ms p50 latency 10M+ calls / day \[ WHAT BECOMES POSSIBLE \] ## Stop Sampling. Process Everything. Frontier models are too expensive to run on every input. At 1/50 the cost, your agents can cover 100% of your data — not just a slice. DATA EXTRACTION BeforeSample 5% of incoming documents AfterProcess every document, 24/7 Extract structured data from every PDF, contract, and invoice — not just a sample. At 1/50 the cost, exhaustive processing becomes the default. 100% coverage, not 5% LEAD SCORING BeforeScore top 10% of leads daily AfterScore every lead, every hour Frontier models force you to prioritize which leads get scored. Maniac lets you score all of them, continuously — so nothing slips through. Every lead scored CONTENT MODERATION BeforeRandom sample QA on tickets AfterReview 100% of conversations Stop randomly sampling support tickets for quality. Monitor every conversation, classify every message, flag every issue — in real time. 0% blind spots CHURN PREDICTION BeforeMonthly batch prediction AfterDaily prediction on every user Run churn models on your entire user base every day instead of monthly batches. Catch at-risk users 30x sooner. 30x faster detection ## How it Works Three steps. No AI team required. A few engineering hours to get started. 01 ### Point your agents at Maniac Swap your API endpoint. Maniac exposes an OpenAI-compatible interface—your existing code, SDKs, and frameworks work unchanged. [Follow the setup guide →](https://docs.maniac.ai/agent-setup/agent-setup) pythonagent.py from maniac import Maniac client = Maniac() container = client.containers.create( label="extraction-agent", initial\_model="openai/gpt-5", ) response = client.chat.completions.create( container="extraction-agent", messages=\[{"role": "user", "content": prompt}\], ) 02 ### We optimize automatically Maniac captures production traffic, builds domain-specific training sets, and runs continuous experiments. Winners are promoted automatically. pythonmaniac logs \[maniac\] Collecting telemetry from extraction-agent \[maniac\] Samples: 12,847 — building dataset \[maniac\] Training candidate: qwen3-14b-lora-r64 \[maniac\] Eval accuracy: 91.2% vs baseline 77.8% \[maniac\] ✓ Candidate promoted to production \# Zero code changes required. \# Maniac handles everything. 03 ### Ship frontier quality at 1/50 cost Optimized models go live through seamless routing. Your agents get frontier-quality responses. Models only get better over time. pythonoutput \# Nothing changes in your code. \# Maniac handles routing automatically. \# Before: $15.00 / 1M tokens \# After: $0.20 / 1M tokens \# Quality: \>Frontier on-domain \# Latency: <400ms p50 \# Uptime: 99.97% ## Engineering Blog Deep dives on model optimization, agent throughput, and the economics of running intelligence at scale — plus updates from the Maniac team. [View the blog](/blog) [ Model LandscapeFeb 28, 2026 ### Chinese frontier models compared: GLM-5, MiniMax M2.5, Kimi K2.5, and Qwen 3.5 A benchmark-driven comparison of the new wave of Chinese frontier models against Claude Opus 4.6 — with pricing, architecture, and practical guidance for production teams. ](/blog/chinese-frontier-models-compared-glm5-minimax-kimi-qwen)[ Case StudyJan 23, 2026 ### Autonomously Beating GPT-5.2 and Gemini 3 Pro in Prediction Accuracy, with 30x Cheaper Inference for Commerce AI Our autonomous pipeline took production traffic hooks as input and output frontier-beating Small Language Models — no ML team required. Here's how it works, and why it generalizes to any predictive task. ](/blog/autonomous-enterprise-ai-engineer) \[ GET STARTED \] ## Start shipping in minutes OpenAI-compatible API. No infrastructure changes. Start free, scale to millions of agent calls. [Book a Demo](/book-demo)[Agent Docs](https://docs.maniac.ai/agent-setup/agent-setup) terminal $ pip install maniac $ maniac init --container my-extraction-agent ✓ Container created: my-extraction-agent ✓ Initial model: openai/gpt-5 ✓ Endpoint: https://api.maniac.ai/v1 $ maniac status ┌─────────────────────────────────────────────┐ │ Container: my-extraction-agent │ │ Status: ● active │ │ Model: maniac-opt-v3 (promoted) │ │ Quality: 99.1% vs opus 4.6 │ │ Cost: $0.20 / 1M tokens │ │ Calls: 2.4M today │ └─────────────────────────────────────────────┘ --- *Maniac — High throughput background agents. Opus-quality outputs at 1/50 of the cost. Learn more at [maniac.ai](https://www.maniac.ai).*