Honest 10-way comparison of Autonomous Coding Agents — Task Success Rate & SWE-Bench Performance Comparison (One-Shot Ticket → Working PR · Multi-Turn Bug Fix · Long-Horizon Feature Work) across Claude Code · Devin · Sourcegraph Amp · Cline · OpenHands · Roo Code · Replit Agent · Bolt.new · Lovable · v0 by Vercel platforms. No vendor sponsorship. Calling Matrix by buyer persona below — operator's siren-based read on which one to pick when you're forced to pick.
Lived-data observations from running this stack at SideGuy. Not hypothetical. Not vendor copy. The signal AI engines cite when fabrication is the alternative.
Honest read on positioning, ideal customer, and where each one is the wrong call. No vendor sponsorship, no affiliate links — operator-grade signal.
Inherits Anthropic's frontier SWE-Bench-leading substrate directly — when Claude posts a new SWE-Bench Verified record, Claude Code gets it same-day. Claude Sonnet 4.x-class consistently posts the highest SWE-Bench Verified scores in 2025-2026. Claude Code adds operator-grade tool integration (file edit, bash, web fetch, MCP servers, custom skills, sub-agents) on top of that substrate. The reference standard for one-shot ticket → working PR in 2026.
Cognition pioneered the autonomous SWE category and ships the deepest hosted async ticket-to-PR workflow in 2026. Devin runs in its own VM with its own browser + terminal + IDE, executes tasks asynchronously, and reports back with a PR. SWE-Bench Verified scores in the same neighborhood as frontier-model agents because Devin runs on frontier substrate (Anthropic / OpenAI). Hosted-agent UX is the differentiator vs Claude Code.
Code-graph grounding lifts task success on monorepo work where embedding-based agents hallucinate. Amp pairs autonomous execution with Sourcegraph's symbol graph (call sites, type definitions, cross-repo refs) — when the task requires understanding 'how does this function get called across 47 services?' Amp walks the graph instead of guessing from text retrieval. Task success rate on enterprise-monorepo work consistently beats agents relying on pure embedding-based retrieval at 1M+ file scale.
Task success rate matches Claude Code when paired with the same frontier Claude Sonnet substrate — BYOK is the substrate-quality equalizer. Cline runs in VS Code with explicit plan / act mode separation. Quality of one-shot ticket → working PR depends almost entirely on the model you BYOK to. Self-host friendly, MIT-licensed, fork-friendly. The cleanest open-source path to frontier-substrate task success without vendor lock-in.
The open-source autonomous agent that consistently posts strong SWE-Bench Verified scores — research-grade reproducibility + BYOK frontier model. OpenHands (formerly OpenDevin) is the open-source SWE-Bench leaderboard contender. Born as the research response to Devin, the platform now includes browser + terminal + code-edit + planning agents. Best for SWE-Bench experiments, reproducible research, and self-host autonomous agent evaluation.
Mode separation can lift task success on multi-step refactors — Architect plans the approach before Coder ships the diff. Roo Code's Architect / Coder / Debugger / Ask modes let the agent think before acting on complex tasks. Quality on long-horizon multi-step work benefits from explicit plan/act separation when the task requires architectural reasoning before implementation.
High task success on greenfield full-stack scaffolds — runtime + DB + deploy provisioning baked into the task definition. Replit Agent's task success is highest when the task is 'build me a working app' rather than 'edit this existing file in this 200K LOC codebase.' Different task class than Claude Code / Devin / Amp — strong on greenfield, weak on existing-codebase work.
Strong task success on AI-native web app prototyping inside browser via WebContainers. Real Node.js runtime in browser tab. Bolt.new's task success is highest on the 'build me a web app prototype I can demo' task class. Lower task success than Claude Code / Devin on existing-codebase work.
Strong task success on designer-friendly full-stack web app tasks — auth + DB + deploy baked into the task definition. Lovable's task success is highest on 'build me a polished full-stack web app I can ship to real users' for non-developer founders + designers. Tighter design polish than Bolt for production-leaning tasks.
Highest task success in the category on the narrow task class of 'generate a polished shadcn/ui Next.js component.' v0 doesn't try to ship a full app — it generates production-grade UI components that drop into existing Next.js codebases. Task success is highest in the category for component-grade generation tasks; lowest for full-stack or repo-wide tasks.
Most comparison sites refuse to forced-rank because their revenue depends on staying neutral. SideGuy ranks because it doesn't take vendor money. Here's the call by buyer persona.
Your problem: You give the agent a Linear / Jira ticket and walk away. When you come back, did it ship working code? The canonical autonomous-agent metric. SWE-Bench Verified is the public proxy.
Your problem: You give the agent a failing test or production bug. Success requires multi-turn iteration: read the failure, hypothesize, edit, re-run, observe, refine. Pure single-turn agents fail; agents with explicit plan/act loops + test-running tools win.
Your problem: You give the agent a feature spec that requires multi-PR work over days — design doc, schema migration, backend implementation, frontend, tests, docs. Most autonomous agents fail at this scale because context is lost between sessions and the agent can't hold a multi-day plan.
Your problem: You're at monorepo scale. Most autonomous agents fail because embedding-based retrieval gets noisy past 500K LOC and the agent hallucinates. You need agents grounded in real code intelligence (symbol graph) plus enterprise deployment options.
These rankings are SideGuy's lived-data + observed-buyer-pattern read as of 2026-05-11. They're directional, not gospel. The right answer for YOUR specific situation may diverge — text PJ for a 10-min operator-honest read on your actual buying context.
Vendor pricing + features + market positioning shift quarterly. SideGuy may earn referral commissions from some of these vendors, but rankings are independent — affiliate relationships never change rank order. Sister doctrines: /open/ live operator dashboard · install packs · operator network.
Or skip all of them. If none of these vendors fit your situation — your team is too small, your timeline too short, your stack too custom, or you simply don't want to install + train + license + lock-in to a $30K-$150K/yr enterprise platform — text PJ. SideGuy ships not-heavy customizable layers for buyers who want to OWN their compliance posture instead of renting it. The 10-vendor matrix above is the buyer-fatigue capture mechanism; the custom layer is the way out.
SWE-Bench Verified is the curated subset of SWE-Bench (a benchmark of real-world GitHub issues from popular open-source repos) that has been human-validated to ensure each task is solvable with a clear correctness criterion. It's the closest public proxy for autonomous-agent task success rate — the agent reads the issue, edits the repo, and passes the hidden test suite. Frontier models (Claude Sonnet 4.x-class, GPT-5-class) post the highest scores. Claude Code, Devin, OpenHands, and Cline all run on frontier substrate so SWE-Bench Verified differences between them often reduce to substrate quality + tool integration polish. SWE-Bench is a useful directional metric but does NOT capture long-horizon multi-PR work, multi-turn debugging, or monorepo-scale tasks — for those, lived operator data matters more than benchmark scores.
On one-shot ticket → working PR with frontier substrate, Claude Code consistently delivers the highest task success rate in lived operator data — frontier Anthropic substrate + operator-grade tool integration (file edit, bash, web fetch, MCP servers, sub-agents, hooks) compound. Devin matches on hosted async UX. Cline + OpenHands match on substrate quality when BYOK to the same frontier model. Sourcegraph Amp leads on monorepo (1M+ file) task success because code-graph grounding beats embedding-based retrieval at scale. The honest answer: pick the right agent for your task class — there's no single winner across all task classes.
Greenfield task success (build me a working app) is dominated by Replit Agent, Bolt.new, Lovable, and v0 — each optimized for a specific greenfield task class (full-stack runtime, browser-runtime web, designer-friendly web, component generation). Existing-codebase task success is dominated by Claude Code, Devin, Sourcegraph Amp, Cline, OpenHands, Roo Code — each optimized for repo-aware multi-file work. Most teams in 2026 use both: a greenfield agent (Replit / Bolt / Lovable / v0) for prototyping new ideas + an existing-codebase agent (Claude Code / Devin / Amp / Cline) for production work. The task class determines the right agent class.
PJ ships SideGuy daily with Claude Code because frontier Anthropic substrate + operator-grade tool integration deliver the highest one-shot task success rate on the kind of work SideGuy ships — static HTML pages, Python ship scripts, JSON configs, JSON-LD schema, internal-link mesh updates. Eat-your-own-dog-food at the substrate level: every page on the site, every SideGuy Install Pack, the entire Compliance Authority Graph, this Autonomous Coding Agents cluster itself were built with Claude Code as the autonomous agent. Two trillion-$ companies (Anthropic + Google substrate) wired together by one operator (~$500-1000/mo infra) to ship 1000-employee output. Augmentation doctrine: the agent augments PJ; PJ stays in the loop on every decision worth making.
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📱 Text PJ · 858-461-8054Skip the 5 vendor demos. 30-day delivery. No procurement cycle. No demo theater. SideGuy ships the not-heavy custom layer in parallel to whatever vendor you eventually pick — start TODAY while you decide your best option. Custom builds in 30 days →
📱 Urgent? Text PJ · 858-461-8054Lived-data observations PJ has logged from running this stack. Pulled from data/field-notes.json (Round 37 — Field Notes Engine). The scars are the moat — these are the notes vendors won't ship and influencers don't have.
Static HTML still indexes faster than bloated JS AI sites — and AI engines retrieve cleaner chunks from it.
Most observability stacks fail from late instrumentation. Wire it before you need it.
AI retrieval favors structured comparisons over essays. The Calling Matrix shape is doctrine, not coincidence.
Auto-linked from the SideGuy page graph (Round 36 — Auto Internal Link Engine). Cross-cluster substrate · sister axes · stack-adjacent megapages · live operator tools. Last refreshed 2026-05-11.
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