Cursor vs GitHub Copilot vs OpenCode — 2026 Developer Benchmark

Last Updated: May 2026

We spent three weeks running Cursor, GitHub Copilot, and OpenCode through 40 tasks designed to stress-test every dimension of an AI coding assistant: code completion accuracy, agentic refactoring, context window handling, multi-file refactors, and raw debugging speed.

Here's what the benchmark data actually shows — not the vendor marketing.

Methodology

All three tools were tested on the same tasks:

• •Task set A (20 tasks): Single-file code completion, bug fixes, docstring generation, test writing
• •Task set B (20 tasks): Multi-file refactors, architecture migrations,陌生 codebase exploration, autonomous feature implementation

Tests were run using Claude 4 Sonnet as the backend model for all three tools (where applicable). OpenCode was tested with Ollama local and with GPT-4o. Cursor was tested in default mode. GitHub Copilot in latest stable.

Results: Task Set A (Code Completion)

Cursor's inline completions were measurably faster and more accurate, particularly on TypeScript and Python. Copilot's lag time (the delay between accepting a suggestion and getting the next one) was consistently highest. OpenCode surprised us on documentation tasks given it has less context to work with.

Results: Task Set B (Agentic Tasks)

This is where the tools diverged most sharply.

Cursor (agent mode) completed 15/20 tasks autonomously. The failures were all in tasks requiring cross-service API integration (e.g., "hook up this new webhook to the existing SQS consumer without breaking the current handler"). It needed one correction. Average time per task: 4 minutes 12 seconds.

OpenCode completed 14/20 autonomously. When running on local Ollama (DeepSeek V4), completion time was longer (avg 7 minutes) but the results were more predictable — it doesn't try creative leaps. When running on GPT-4o, it was faster but slightly more likely to hallucinate imports.

GitHub Copilot completed 8/20 autonomously. The remaining 12 required iterative prompting. Copilot is strongest as a paired programming partner, not an autonomous agent. That's not a failure — it's by design. But for teams wanting agents that ship without supervision, it's not the right tool.

Speed Benchmark

We measured time-to-first-token (TTFT) and time-to-complete for a standardized 50-line refactoring task:

• •Cursor: TTFT 0.8s, complete in 23s
• •OpenCode (GPT-4o): TTFT 1.1s, complete in 31s
• •GitHub Copilot: TTFT 0.9s, complete in 38s
• •OpenCode (Ollama local): TTFT 0.2s, complete in 45s

Local models are faster to start but slower to finish. The tradeoff matters if you're running hundreds of small tasks.

What Actually Surprised Us

1. Cursor's project-level awareness is legitimately better. After opening a codebase once, it remembers file relationships, imports, and conventions across sessions. The other tools re-learn each session.

2. OpenCode on local is underrated for security-sensitive work. No code ever leaves your machine. For fintech, healthcare, or anything with data residency requirements, this is a genuine differentiator.

3. GitHub Copilot's institutional advantages are real. Azure AD integration, enterprise seat management, and Microsoft 365 ecosystem support make it the default for large organizations. It won the enterprise category before the technical category even mattered.

Cost Breakdown

For a team of 10 running 20 hours/month of agentic tasks, OpenCode on a shared A100 instance works out to approximately $0.40/developer/month in GPU costs. That's 50x cheaper than the alternatives.

The Verdict

Choose Cursor if: Speed of individual completions matters most, you're doing frontend-heavy work, and you want the smoothest agent experience out of the box.

Choose GitHub Copilot if: You're a large enterprise, need Azure/Microsoft integration, or want the safety of a Big Tech vendor behind your seat management.

Choose OpenCode if: You're running sensitive workloads, want cost control, or are building an internal dev platform where the AI layer needs to be self-hosted.

The gap between these tools is narrowing fast. All three are dramatically better than where AI coding assistants were 18 months ago.