Blitzy vs Devin vs Factory: Enterprise AI Coding Agents Compared

Introduction

Three of the most-hyped enterprise AI coding platforms in 2026 — Blitzy, Devin, and Factory — all pitch themselves as “AI software engineers” that can take a real task end-to-end. SWE-bench scores get quoted everywhere. The marketing makes them sound interchangeable. They aren’t.

This is a head-to-head test with the same brutal real-world task: take the AWS Card Demo Modernization repo (an open-source mainframe credit-card management app written in COBOL, CICS, VSAM, and JCL) and modernize it to a Java 21 cloud-native microservice architecture. Same multi-page prompt to all three. The differences in approach — and output — turned out to be far bigger than the benchmarks suggest.

📚 Table of contents

The three platforms and what they claim

The test: AWS Card Demo modernization

AWS publishes the Card Demo as a deliberately legacy app for modernization benchmarks. The stack: COBOL programs orchestrated by CICS transactions, VSAM file storage, JCL for batch jobs. Pure mainframe DNA. Real enterprises run software that looks exactly like this.

The same multi-page prompt was fed to each platform: refactor this COBOL mainframe implementation into a modern Java 21 application with Spring Boot, microservice boundaries, proper databases, and AWS-native deployment. Four things being tested:

• Can it understand an existing — legacy — codebase?
• Can it plan a coherent modernization strategy?
• Can it generate the actual service code and infrastructure?
• Can it validate that the final system works?

SWE-bench results — and why they don’t tell the whole story

All three vendors publish SWE-bench numbers. The reported scores:

• Devin — 13.68% on SWE-bench full (their published number).
• Factory (Code Droid) — 19.27% on SWE-bench full, 31.67% on SWE-bench Lite.
• Blitzy — 86.8% on SWE-bench Verified (the 500-issue curated subset), reported with no scaffolding, hints, or best-of-K attempts.

The numbers aren’t directly comparable. SWE-bench has multiple variants (full, Lite, Verified) with very different difficulty curves. Some approaches use scaffolding, retries, or multiple attempts; some don’t. Treat the published percentages as vendor marketing floors, not apples-to-apples comparisons.

The real test is the practical run.

Blitzy — infinite-context planner with multi-day runs

Blitzy’s flow is unusual:

1. Connect the repository.
2. Blitzy ingests it, taking 2–3 days, and produces a 150–200 page technical specification covering every module, data structure, transaction flow, and assembly-level detail.
3. You write a detailed task prompt against that spec.
4. Blitzy generates code for another 3–4 days.
5. Output: a single pull request with the full modernization.

Total real time: ~1 week, mostly idle. Total active human time: a few hours writing prompts.

The output on the Card Demo test: 1 PR, 179 files changed, ~144,000 insertions, 354 deletions. Full Java 21 microservice scaffold, Spring Boot wiring, COBOL-to-Java program mapping, Postgres-backed entity model, AWS deployment scripts. Plus a project guide documenting what was done and what human tasks remain — mostly deployment, security audit, and integration testing.

Blitzy estimated 560 hours of equivalent human work were generated, with roughly 32 hours of operational follow-up tasks for the developer.

Devin — fast iterative AI software engineer

Devin works completely differently. Connect the repo, drop a prompt in chat, Devin spins up its own coding environment with a terminal and browser, and starts working in real time. You can watch every command, every file edit, every browser action.

On the same prompt, Devin’s first PR came back in ~25 minutes with 38 files changed and 3,341 insertions. Surface-level Java 21 / Spring Boot scaffold, basic API definitions, partial COBOL-to-Java mapping. Workable as a starting point. Not the complete modernization.

Multiple follow-up prompts (“you missed these features,” “implement the daily transaction ingest,” “match the original feature set”) brought the file count up to ~77 and the line count to ~6,400. Significant ground covered, but nowhere near Blitzy’s output. The iterative model needed continuous human steering, with each prompt surfacing new gaps.

Where Devin shines: well-scoped tickets that fit one head’s worth of context. Where it struggles: cross-cutting modernization that needs the whole codebase understood at once.

Factory — droids across local and remote environments

Factory’s differentiator is the local bridge. The Factory Bridge connects a session running in the cloud to a project open on your local machine (Cursor, VS Code, whatever). The agent reads and writes files locally; you watch it happen in your editor.

On the same prompt, Factory took ~10 minutes to produce an initial plan, then needed an explicit “proceed” before scaffolding. Subsequent prompts iterated on functionality. After ~45 minutes of active steering, the output looked similar to Devin’s — partial scaffold, some endpoints, missing major chunks of the original COBOL logic.

Same fundamental limitation as Devin: without the full-codebase context that Blitzy spends days building, Factory needs the human to know exactly what’s missing and prompt for each piece explicitly. The droid hand-off model is genuinely interesting on smaller tasks where one droid can finish before the next picks up — but on a monolithic modernization, it added coordination overhead rather than removing it.

Side-by-side results

Pricing reality

• Blitzy — free to generate the technical spec; Pro plan ~$10K/year; Team plan ~$100K/year. Aimed squarely at enterprises with real refactoring budgets.
• Devin — Team plan $500/month, pay-as-you-go Core from $20, Enterprise on request.
• Factory — free tier, Pro plan, $200/month Max, Enterprise on request.

The pricing isn’t arbitrary. Blitzy’s value proposition is “replaces 500+ engineering hours per project” — if it works, $10K/year is trivial. Devin and Factory are priced like advanced AI coding assistants, which is what they functionally are.

When to pick which

❌ Common mistakes

💡 Pro tips

Conclusion

These three platforms aren’t competing on the same axis. Blitzy is an enterprise modernization batch system. Devin is an iterative AI engineer for ticket-sized work. Factory is a droid-network coordination layer that bridges local and remote work. The benchmarks blur the distinction; the real-world tests make it obvious.

For most teams in 2026, Devin or Factory is a useful augmentation to existing IDE-based AI tools (Cursor, Claude Code). For organizations with serious modernization budgets, Blitzy is the only realistic option on this list — the others can’t do what it does, no matter how many follow-up prompts you write.