Gotcha vs Prime Agent: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Gotcha and Prime Agent — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Gotcha

Samosa-AI

Freemium

On-device Android AI copilot that turns natural language into 100+ real device actions with dual safety modes.

On-device Copilot: Runs entirely on the user's Android phone with a bring-your-own-model architecture, so prompts, screen context, and actions never require a cloud round-trip.
Dual Copilot Modes: Instant switch between Monitor mode (40+ read-only tools for planning) and Operator mode (all 100+ tools for execution) so users choose inspection vs action per task.
100+ Native Device Tools: Send SMS, place calls, manage storage, toggle torch, set wallpapers, read the screen, control volume, and automate any app through a curated Android tool library.
Tiered Permission Model: Four permission tiers (from everyday battery/storage access to Tier 4 privileged root actions) with explicit gates so nothing runs without user consent.
Assistive Ball & Push-to-Talk: A floating orb accessible from any app supports 'Hey Gotcha' voice calls where Gotcha sees the current screen and acts on the user's behalf.
Visual Safety Indicators: A colored ring around the screen shows when Gotcha is reading the display (blue) or working in the background (orange), backed by an append-only audit log.
BYOK Model Choice: Bring your own local or cloud model — or use free Samosa AI credits routed through the OpenAI-compatible Samosa AIR API for zero setup.
Skill Hub Extensibility: Add third-party skills through the Gotcha Skill Hub so the copilot's action library grows with the community.

Hands-free Messaging: Say 'Text mom I'm running late' and Gotcha finds the contact and sends the SMS without touching the screen.
Storage Cleanup: Ask Gotcha to free up space and it inspects app usage, then confirms uninstalls of games or apps you haven't opened in months.
Screen-aware Shopping: While browsing a page, ask 'find similar shirts to the one worn here' and Gotcha reads the screen, searches the web, and returns matches.
Quick Device Control: Toggle the torch, set the volume, change wallpaper, or open a specific playlist in Spotify with one spoken sentence.
Privacy-first Automation: Users who don't want cloud LLMs running their phones can plug in a local model and keep prompts, screen content, and actions on-device.
Developer Copilot Extensions: Ship a Gotcha Skill so a niche workflow (e.g., custom-app automation) becomes a first-class action inside the copilot.

Prime Intellect

Freemium

A self-improving RLM coding agent from Prime Intellect that can refine its own harness on a training-inference-compute stack you own.

Continual Harness: The agent can modify and refine its own scaffolding — tools, prompts, and evaluation criteria — during long-running work.
RLM Foundation: Built on Reasoning Language Models rather than plain chat models, so multi-step planning and self-critique are first-class.
One-Line Install: Bootstrap the agent locally with a single curl-piped shell script — no infra setup, no configuration.
Integrated Training Loop: Capture production traces, cluster failures, convert misses into RL environments, and train adapters that make the model cheaper and more reliable for your workflow.
2,500+ RL Environments: Train and evaluate against a community-curated environment hub (verifiers-based), including SWE, terminal, search, and science tasks.
Owned Inference Stack: Deploy the improved agent on dedicated GPUs, serverless APIs, or LoRA adapters served alongside base models with a 1-click flow.
Global GPU Access: On-demand H100/H200/B200/B300 or reserved clusters from 50+ datacenters, orchestrated with SLURM/K8s and Grafana monitoring.

Autonomous Coding: Run a self-improving harness over your repository that plans, edits, and validates changes over long sessions.
SWE-Bench Style Benchmarks: Iterate the agent against tasks like mini-swe-agent-plus and Verifiers-based SWE environments.
Training Custom Agents: Post-train your own domain-specific coding agent on captured traces (Ramp beat frontier models on spreadsheet search this way).
Enterprise Deployment: Serve the improved agent on private dedicated inference with LoRA adapters and OpenAI-compatible APIs.
Research on Continual Learning: Study how agents self-modify their harness while progress remains auditable and reversible.
Cost Reduction: Turn expensive frontier calls into cheaper fine-tuned adapters that specialize in your codebase and workflow.