DeepSeek vs Kimi
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
An open-source agent harness built on a plugin-first design, run from a local Web UI.
AI tool · Freemium
Moonshot AI's assistant with long-context reading, web research and agentic modes.
The short answer
Choose DeepSeek if you need…
- Developers who want to experiment with an agent harness they can extend with plugins
- Developers already interested in the Cordis plugin model
- People comfortable running a local dev tool from the command line
- Plugin authors who want to publish to the dsh-plugin topic
Choose Kimi if you need…
- Reading very long documents
- Deep research tasks
Side-by-side
| DeepSeek | Kimi | |
|---|---|---|
| Type | Dev tool | AI assistants · General assistants |
| Price | Free and open source (hosted plans: freemium) | Freemium |
| Licence | MIT | — |
| GitHub stars | 269k | — |
| Setup difficulty | Intermediate | — |
| How you get it | Install with a package manager | — |
| Platforms | Web, iOS, Android, API | Web, iOS, Android, API |
| Works with | — | — |
| Main language | TypeScript | — |
| Last code update | 7 days ago | — |
| Hands-on tested by us | — | Not yet |
| Last checked | — | 2026-10-29 |
What each one is
DeepSeek
DeepSeek Harness (dsh) is an open-source agent harness from DeepSeek AI, built so that nearly every part is a plugin. It runs on Cordis, a plugin framework the README links to, and can be started as a local Web UI. The README describes it as being in developer preview, so the interface and APIs may change.
Kimi
Moonshot AI's assistant with long-context reading, web research and agentic modes. Impressive long-context and agent features. As with other China-based services, review data-residency terms.
Best for
DeepSeek
- Developers who want to experiment with an agent harness they can extend with plugins
- Developers already interested in the Cordis plugin model
- People comfortable running a local dev tool from the command line
- Plugin authors who want to publish to the dsh-plugin topic
Kimi
- Reading very long documents
- Deep research tasks
Strengths
DeepSeek
- Built on a stated everything-is-a-plugin architecture
- Can be started quickly with a single npx command, per the README
- Documentation, a Discord community and GitHub Discussions are linked from the README
Kimi
- Long-context file reading
- Researcher mode
- Open-weight models
- Slides and docs generation
Not ideal for
DeepSeek
- Users who need a stable production system, since the README warns of compatibility-breaking changes during developer preview
- Anyone who has not read the SAFETY.md notice, which the README asks users to review before running it
- Non-technical users looking for a finished consumer app; the README assumes Node.js or a repository checkout
Kimi
—
Trade-offs
DeepSeek
- The README explicitly says it is in developer preview with breaking changes expected
- The README is truncated and gives limited detail on features and models supported
- Requires understanding of Node.js or pnpm and a local dev workflow
Kimi
—
Getting started
DeepSeek
Run from npm
npx @deepseek-ai/dsh webClone the repository
git clone https://github.com/deepseek-ai/deepseek-harness.gitFrequently asked questions
What is the difference between DeepSeek and Kimi?
DeepSeek: An open-source agent harness built on a plugin-first design, run from a local Web UI. Kimi: Moonshot AI's assistant with long-context reading, web research and agentic modes. DeepSeek suits developers who want to experiment with an agent harness they can extend with plugins, while Kimi suits reading very long documents.
Are DeepSeek and Kimi free?
DeepSeek: Free and open source (hosted plans: freemium). Kimi: Freemium.
What are other alternatives to DeepSeek and Kimi?
Other options people compare them with include LangChain, CrewAI, Our Free Model, Claude, ChatGPT.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.