Maniac LogoManiac

[ 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.

Book a DemoAgent Docs

[ 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
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
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 →
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
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 and GPT-5.2 — with pricing, architecture, and practical guidance for production teams.

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.

[ GET STARTED ]

Start shipping
in minutes

OpenAI-compatible API. No infrastructure changes. Start free, scale to millions of agent calls.

Book a DemoAgent Docs
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                     │  └─────────────────────────────────────────────┘