Kimi Code CLI vs Qwen Code

Kimi Code CLI

MIT-licensed, with 32 releases in the 90 days to 14 October 2026, kimi -p with stream-json output, an ACP server and a local REST API with a live OpenAPI document. Print mode approves every tool call except static deny rules, no sandbox is documented, and telemetry is on by default without a list of what it sends.

Qwen Code

Apache-2.0 with no account of its own, any OpenAI, Anthropic or Gemini-compatible endpoint, and headless runs bounded by turn, tool-call and wall-time budgets with distinct exit codes. Out of the box, Auto mode lets an LLM classifier approve tool calls, with the sandbox and folder trust both off. Usage statistics are on by default.

Kimi Code CLI

1. kimi -p approves every tool call. Add [[permission.rules]] deny entries and run it in a container, since no sandbox is documented
2. Set KIMI_LOOP_MAX_STEPS_PER_TURN for unattended runs. Steps per turn are unlimited by default
3. Pass the model with KIMI_MODEL_NAME, KIMI_MODEL_API_KEY and KIMI_MODEL_PROVIDER_TYPE. OPENAI_API_KEY or ANTHROPIC_API_KEY in the shell is ignored otherwise
4. Set KIMI_DISABLE_TELEMETRY=1 and use 0.41.0 or later, since earlier print-mode runs ignored it
5. Install @moonshot-ai/kimi-code or the kimi-code formula. The kimi-cli and kimi-code packages on PyPI are the archived Python CLI

Qwen Code

1. Pass --approval-mode on every run. The settings default is auto, and one docs page says headless runs default to asking, so don't rely on either
2. Add --max-session-turns, --max-wall-time and --max-tool-calls. All three are unlimited by default. Exit 53 is the turn limit and 55 a budget
3. --yolo doesn't turn on a sandbox. Add --sandbox, or on Linux set tools.executionSandbox with network set to closed
4. Set QWEN_USAGE_STATISTICS_ENABLED=false to stop usage statistics
5. Authenticate with OPENAI_API_KEY, OPENAI_BASE_URL and OPENAI_MODEL in CI. The browser sign-in is discontinued