Introduction

DeepAgent Desktop beats heavyweights like GPT-5 Codex and Claude Code on two hard, real-world tests: TerminalBench and SWE-bench Verified. It scored 48.75% on TerminalBench and 74% on SWE-bench Verified—numbers that reflect end-to-end engineering, not just code completion.

These benchmarks test terminal workflows, repo edits, and passing tests. That is why they matter for teams shipping real software.

Benchmark Performance: How DeepAgent Surpassed GPT-5 Codex

SWE-bench Verified is widely seen as the gold standard because it checks if an agent can fix real GitHub issues end-to-end—edit, test, and submit. Early GPT-4 runs scored ~20–27%, while Claude 3.5 Sonnet hit ~44–47%. DeepAgent's 74% is a major leap.

TerminalBench, launched in 2024, measures command-line skill: navigation, compilation, debugging, and multi-step workflows. Top open-source agents hovered in the 30–40% range. DeepAgent's 48.75% leads.

Core Features That Differentiate DeepAgent

DeepAgent Desktop combines three modes—CLI Agent, Code Editor Agent, and Chat Mode—and adds an automated Testing Agent. This mix enables scaffold → edit → test → iterate inside one surface.

CLI Agent — DeepAgent CLI fastest way to code

Work from the terminal. Create projects, wire routes, and run tests with short prompts. Demos include a retro Snake game and a LinkedIn-style app named ConnectHub. Quick start:

Code Editor Agent — an AI code editor with testing agent

Behaves like an IDE powered by an agent. It reads a resume image (OCR), extracts data, and builds a polished site. It also ships interactive learning guides. The Testing Agent validates code automatically.

Chat Mode — Claude, Gemini, GPT-5

Switch models per task without leaving the app. Use Claude for reasoning, GPT-5 for structured edits, or Gemini for ideation.

Real-World Applications and Demos

• Build a gamified Snake app with visuals, levels, and badges from a single prompt.
• Generate a LinkedIn-style app (ConnectHub) with auth, feeds, and a Django backend.
• Manipulate a live GitHub repo (e.g., add a leaderboard that weights recency and comments).
• Turn a resume image into a responsive portfolio via OCR + codegen.

These demos reflect repo-level work where many models still struggle. Benchmarks like SWE-bench and write-ups on its "Pro" variants raise the bar.

Accessibility and Pricing

The basic tier is $10/month, undercutting or matching popular assistants. Desktop integration and the Testing Agent can replace multiple paid add-ons.

Weekly $2,500 build contests help the community share working examples—an engine for rapid learning and visibility.

Why Developers Should Pay Attention

An all-in-one suite beats tool sprawl: CLI + Editor + Chat + Testing in one place. Less context switching, more flow. Strong scores on TerminalBench and SWE-bench support this approach.

Multi-model Chat Mode reduces vendor lock-in: swap between GPT-5, Claude, and Gemini as needed.

Getting Started

One-Command Install

Open your terminal and run:

Use it to scaffold a small app, wire tests, and ship your first patch.

When to Move Into the Code Editor Agent

• You need multi-file edits and refactors.
• You want the Testing Agent to validate each change.
• You're aligning code with CI/Lint rules.

When to Use Chat Mode

• Compare plans across models in one thread.
• Draft migration notes, README updates, or test plans.
• Blend strengths: Claude for reasoning, GPT-5 for structured edits.

Abacus AI Coding Agent in Team Workflows

Pull-Request Driven Teams

• Generate a branch, commit small changes, and let tests run locally.
• Open a PR with a concise diff summary and test notes.

Bug-Bash Weeks

Aim the agent at labeled queues such as "good first issue" or "chore," mirroring SWE-bench-style tasks for throughput.

Teaching and Onboarding

Run guided fixes with tests and store a shared set of "prompts that work" for your codebase.

Evaluation Checklist

• Speed to first patch: land a fix within an hour.
• Edit precision: clean, localized diffs.
• Test pass rate: leverage the Testing Agent to keep red builds low.
• Terminal fluency: fewer keystrokes on TerminalBench-like tasks.
• Repo awareness: respects your lint and CI rules.

Tips for Better Outcomes

• Be explicit: specify stack, versions, and tests.
• Keep loops short: one feature → run tests → iterate.
• Seed context: share directory layout and coding standards early.
• Lock the stack: pin versions to avoid surprises.
• Own the tests: let the agent draft, you refine assertions.

When DeepAgent Is Not a Fit

• Strict manual gates with no room for automation.
• Monorepos with fragile, undocumented builds.
• Projects with no tests—add a scaffold first.

Roadmap Watch: Benchmarks and Beyond

• Follow SWE-bench updates and "Pro" variants.
• Track enhancements to TerminalBench for richer tool use.
• Watch model updates from OpenAI (GPT-5) and Anthropic (Claude 4).
• Read independent repo-workflow comparisons.