MoDev vs Zero: Features, Pricing & Which Is Better (2026)

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

MoDev

Paid

A mobile-first full development environment integrating Claude, GitHub, Vercel, and Supabase to build and deploy apps from a phone.

Claude AI Integration: Built-in access to Claude for code generation, suggestions, and debugging directly within the mobile app to accelerate development tasks.
GitHub Integration: Connect and sync with GitHub repositories to browse code, edit files, commit changes, and keep project history synchronized from a phone.
Vercel Deployment Integration: Trigger and manage Vercel deployments and view preview URLs so you can deploy web apps and verify changes from mobile.
Supabase Backend Management: Connect to Supabase to manage project databases, authentication, and storage resources as part of the mobile development workflow.
Mobile-first Development Environment: A touch-optimized, end-to-end workflow that enables editing, testing, and deploying projects on a phone without requiring a laptop.
Service Orchestration UI: Unified interface that brings together AI assistance, source control, hosting, and backend services to streamline building and releasing apps on the go.
Mobile-first development environment for phones
Integration with Claude for code assistance
GitHub integration for repository access and management
Vercel integration for deployments
Supabase integration for backend/database management
Designed to enable development workflows without a laptop

Prototyping and deploying web or mobile app prototypes entirely from a smartphone while traveling or away from a workstation.
Applying hotfixes and urgent patches by editing code, committing to GitHub, and triggering Vercel deployments directly from a phone.
Using Claude-powered code generation and debugging on the go to accelerate feature development, refactoring, or troubleshooting.
Managing Supabase-backed backend tasks—such as inspecting data, adjusting auth settings, or testing storage—during development or QA on mobile.
Reviewing, merging, and collaborating on GitHub pull requests and monitoring deployment previews and status from a mobile device.
Develop and iterate on projects directly from a mobile device
Use Claude-powered code assistance for writing and refactoring code on the go
Manage GitHub repositories and perform basic source control tasks from a phone
Deploy web apps or frontends to Vercel from mobile
Manage Supabase backends and databases while away from a workstation

Vercel Labs

Free

An experimental graph-first programming language where agents edit a compiler-checked program graph instead of raw source text.

Graph as the Program: A compiler-owned semantic graph of symbols, calls, types, effects and node IDs is the source of truth, so agents reason over program structure rather than parsing and regenerating text.
Hash-Guarded Patches: Every edit carries an expected graph hash and expected field values, so a stale or conflicting patch is rejected before it reaches the store instead of silently corrupting the program.
Compiler in the Loop: Shape, type, stale-state and repository metadata checks run as part of applying a patch, collapsing the write-build-test-inspect cycle into a single checked operation.
Readable Text Projections: The graph renders to reviewable .0 source projections so humans can read diffs, audit what an agent changed and make rare manual edits.
Structured JSON Diagnostics: The compiler emits machine-readable diagnostics rather than prose error text, so agents can act on failures without parsing terminal output.
Explicit Effects via World: Side effects are passed through an explicit World capability parameter, making what a function can touch visible in its signature.
Runtime Constraints by Design: Targets token efficiency, low memory, fast startup, fast builds, low latency and zero dependencies rather than relaxing systems goals for agent ergonomics.
Query and Patch CLI: zero init, zero query, zero patch and zero run give agents a direct command surface over the graph, with agent skills carrying the graph discipline instead of rigid human prompts.

Reliable Agent Code Edits: Let a coding agent make semantic changes that are rejected outright if its view of the program is stale, instead of producing plausible-looking but broken text diffs.
Reducing Agent Token Spend: Query the specific symbols, types and nodes relevant to a task rather than feeding whole files into context on every turn.
Outcome-Driven Development: Describe a desired result in conversation — add auth, fix a failing route, build a CRM API — and review the resulting projection rather than writing the code.
Auditable AI-Written Code: Review what changed through readable .0 projections and graph hashes, keeping a human checkpoint over agent-authored programs.
Language and Tooling Research: Explore what a compiler and program representation look like when machine editors, not human typists, are the primary writers.
Sandboxed Experimentation: Prototype agent-driven codebases in an isolated environment where breaking changes and pre-1.0 churn are acceptable.