Developers are increasingly running the same coding task across multiple AI tools to see where the real differences are - and a recent comparison pitting GitHubb Copilot](/tools/github-copilot/), Claudee Code](/tools/claude-code/), and opencode against each other using Qwen3 27B is a good example of what that looks like in practice.
The setup is worth understanding. GitHub Copilot and Claude Code are proprietary tools backed by their respective cloud models. Opencode is an open-source terminal coding agent (similar in interface to Claude Code) that can point at whatever model you choose - including local ones. Qwen3 27B is a 27-billion parameter open-source model from Alibaba's Qwen team that can run on a high-end consumer GPU, meaning no API costs and no data leaving your machine.
Running the same task across all four gives a clearer picture than any single benchmark: how does a locally-run open-source model compare to the cloud-backed incumbents in real coding scenarios, and does opencode as an interface add or subtract from that experience?
This kind of community testing matters because the gap between local and cloud models has narrowed considerably in 2025. Qwen3 27B scores competitively on coding benchmarks against models that cost real money per token. For developers who do a lot of agentic coding work - where the tool writes, runs, and debugs code in loops - the per-token cost adds up fast. A local alternative that gets 80% of the output quality at zero marginal cost is genuinely worth testing.
Opencode in particular has attracted attention as a Claude Code alternative for teams who want the terminal-first workflow without the subscription or the cloud dependency. The comparison puts numbers to a question a lot of developers are already asking.