An in-depth comparison of Amp and OpenClaw across output quality, autonomy, reliability, speed, value, and ease of use. Vote for your favorite.
Pick a winner in each category — you can change your vote anytime.
Choose Amp if you are teams that want maximum-capability agentic coding and will pay for tokens at cost. Choose OpenClaw if you are tinkerers who want a self-hosted JARVIS that actually does things.
In our editorial scoring, Amp leads in 3 of six categories (output quality, reliability and ease of use), while OpenClaw leads in 2 (autonomy and value). On price, Amp runs usage-based credits / free tier and is proprietary; OpenClaw runs free (mit) / models via standard compute and is open source.
Amp is Sourcegraph's take on agentic coding: no model picker, no knobs — it always runs frontier models with maximum reasoning and leans into autonomy. Work happens in shareable threads across the VS Code extension and CLI, with subagents for parallelizable work and team visibility into how colleagues prompt. It's deliberately opinionated and token-hungry; credits are consumed at cost, so sustained heavy use gets expensive, and there's no BYO-key escape hatch.
OpenClaw is the open-source autonomous agent created by Peter Steinberger (it began as Clawdbot in 2025, became Moltbot, then OpenClaw in January 2026 — gaining 60,000+ GitHub stars within days). It runs locally, uses messaging platforms as its main interface, and acts rather than advises: with 100+ skills it browses the web, sends email, manages files, runs shell commands, and drives APIs. Since Steinberger joined OpenAI in February 2026, the MIT-licensed project is stewarded by the independent OpenClaw Foundation.
Both work with any OpenAI-compatible provider. Point the base URL at Standard Compute and get unlimited frontier-model compute from $9/mo flat — no per-token billing, no 429 rate limits.
Whichever AI agent you choose, Standard Compute gives you unlimited LLM compute at one flat monthly price. No rate limits, no per-token billing.