maap vs Needle 3

A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.

maap compared with Needle 3
Detail	maap	Needle 3
What it does	A 4B model that estimates numbers from text with calibrated intervals	Foundation model for mobile, wearables, robots, smart home, automotive and microcontrollers
Stage	Not given	Startup
Founded	Not given	Not given
Based in	Not given	Not given
Pricing model	Freemium	Open source
Pricing	First 1,000 estimates free. Pricing for additional estimates available upon request.	Not given
Key features	Returns median, quantiles, and calibrated intervals for numerical estimates Processes text records in a single forward pass without text generation Responds in approximately 160 milliseconds Flags when targets are outside training distribution or intervals lack calibration guarantees Supports few-shot learning with at least eight labeled examples Trained on 36 public sources Calculates probability of exceeding user-specified thresholds For count targets can return probability of each whole number Accepts text input without requiring schema declaration or preprocessing Costs roughly two orders of magnitude less per prediction than frontier models	Tool calling with function selection and argument filling Structured extraction with guaranteed JSON parsing Text embeddings for local semantic search and matching Confidence scoring for routing and gating calls Laddered subnetwork architecture from 2 to 20 layers LoRA fine-tuning support for customization 400-4k tokens per second inference speed on Raspberry Pi 5 Python package with pip install and Hugging Face distribution
What makes it different	Provides calibrated uncertainty quantification where language models cannot Transparently reports when calibration guarantees are absent Dramatically faster inference than text-generating alternatives at 160ms Designed for unstructured text without requiring labeled training data Explicitly signals new, unseen targets with fallback capability for code	Beats models 10x its size on mobile tool calling benchmarks Matches models 2-3x larger on structured extraction tasks Entire model in a single 8-29 MB binary for tiny devices Simple Attention Network architecture uses 2x fewer MFLOPs than transformers Fine-tuned subnetworks outperform larger models like DeepSeek V4 Flash
Community votes	0	0
DR (Domain Rating by Ahrefs)	2 no change since last check	56 no change since last check
Trust Flow (Majestic)	4	14
Citation Flow (Majestic)	23	39
Referring domains (Majestic)	23	1592

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