What AI models does the agent use?

The agent has access to Claude, DeepSeek, Gemini, and other top models. It automatically routes to the best model for each task — you never have to think about API keys or token limits.

Yes. Each agent runs in an isolated environment. We don't train on your data, and you can export or delete everything at any time. The underlying engine (GoGogot) is open-source, so you can audit exactly how it works.

What can the agent actually do?

Browse the web, process files (CSV, PDF, images), run scheduled tasks, remember context across sessions, and communicate via Telegram or Slack. Think of it as a capable junior employee, not a chatbot.

How does memory work?

The agent maintains persistent memory across sessions. It remembers your preferences, past conversations, and task context. You can also explicitly tell it to remember or forget things.

Can I self-host the agent?

Absolutely. GoGogot is 100% open-source. Run it on your own hardware for free. cowork.ink is the managed version — we handle servers, model costs, updates, and uptime so you don't have to.

Is there a free trial?

We offer a 7-day money-back guarantee. Spin up an agent, give it real tasks, and if it doesn't save you time, we'll refund you — no questions asked.

What is SWE-bench and how does it work?

SWE-bench is a benchmark that tests AI agents on real GitHub issues from popular open-source repositories. Each task gives the agent a codebase and an issue description, and the agent must produce a patch that resolves the problem. Success is measured by running the repository's own test suite against the patch.

What is a good SWE-bench Verified score in 2026?

As of March 2026, top models score around 80% on SWE-bench Verified. Claude Opus 4.5 leads at 80.9%, followed by Claude Opus 4.6 at 80.8% and Gemini 3.1 Pro at 80.6%. Scores above 70% are considered strong. For context, the first agentic system scored just 12.47% in 2024.

What is the difference between SWE-bench, SWE-bench Verified, and SWE-bench Pro?

The original SWE-bench has 2,294 tasks but includes noisy or ambiguous problems. SWE-bench Verified is a human-curated subset of 500 high-quality tasks — the most trusted leaderboard. SWE-bench Pro uses harder, longer tasks from proprietary codebases where top models score only ~23%, revealing the gap between benchmarks and real work.

Can SWE-bench scores be gamed or misleading?

Yes. Scores depend heavily on the scaffolding (the agent harness around the model), not just the model itself. Data contamination is a concern since the tasks come from public GitHub repos. And passing unit tests doesn't always mean the fix is correct — patches can be superficial. SWE-bench Pro and SWE-rebench address some of these issues with contamination controls and harder tasks.

How should I use SWE-bench scores when choosing an AI coding tool?

Use SWE-bench as one signal, not the only signal. High scores indicate strong code understanding and bug-fixing ability, but they don't measure feature building, code review quality, or team collaboration. Compare scores on the same scaffold (like mini-SWE-agent) for fair model comparisons. For team workflows, evaluate tools like [cowork.ink](https://cowork.ink) on real tasks from your own codebase.

SWE-bench Explained: How We Benchmark AI Coding Agents in 2026

COMPLETE guide to SWE-bench in 2026. How the benchmark works, what scores mean, leaderboard leaders & why it matters for AI coding tools.

Frequently Asked Questions

What is SWE-bench and how does it work?: SWE-bench is a benchmark that tests AI agents on real GitHub issues from popular open-source repositories. Each task gives the agent a codebase and an issue description, and the agent must produce a patch that resolves the problem. Success is measured by running the repository's own test suite against the patch.
What is a good SWE-bench Verified score in 2026?: As of March 2026, top models score around 80% on SWE-bench Verified. Claude Opus 4.5 leads at 80.9%, followed by Claude Opus 4.6 at 80.8% and Gemini 3.1 Pro at 80.6%. Scores above 70% are considered strong. For context, the first agentic system scored just 12.47% in 2024.
What is the difference between SWE-bench, SWE-bench Verified, and SWE-bench Pro?: The original SWE-bench has 2,294 tasks but includes noisy or ambiguous problems. SWE-bench Verified is a human-curated subset of 500 high-quality tasks — the most trusted leaderboard. SWE-bench Pro uses harder, longer tasks from proprietary codebases where top models score only ~23%, revealing the gap between benchmarks and real work.
Can SWE-bench scores be gamed or misleading?: Yes. Scores depend heavily on the scaffolding (the agent harness around the model), not just the model itself. Data contamination is a concern since the tasks come from public GitHub repos. And passing unit tests doesn't always mean the fix is correct — patches can be superficial. SWE-bench Pro and SWE-rebench address some of these issues with contamination controls and harder tasks.
How should I use SWE-bench scores when choosing an AI coding tool?: Use SWE-bench as one signal, not the only signal. High scores indicate strong code understanding and bug-fixing ability, but they don't measure feature building, code review quality, or team collaboration. Compare scores on the same scaffold (like mini-SWE-agent) for fair model comparisons. For team workflows, evaluate tools like [cowork.ink](https://cowork.ink) on real tasks from your own codebase.