DeepSeek vs Kimi
Efficient, open, and built for deep reasoning vs long-context specialist for large documents
DeepSeek vs Kimi at a glance
| DeepSeek | Kimi | |
|---|---|---|
| Developer | DeepSeek AI | Moonshot AI |
| Latest version | DeepSeek V4 Pro | Kimi K3 |
| Known for | Technical | Long-context |
| Context window | Up to 1 million tokens | Up to 1 million tokens |
| Multimodal support | Text and code | Text, images, and video |
| Open source | Yes | Partially |
| Official pricing | Free to chat here; low-cost API from $0.14 per million tokens | Free to chat here; API from $0.30 per million cached tokens |
DeepSeek benchmark highlight
Scores 80.6% on SWE-bench Verified and 90.1% on GPQA Diamond, top-tier among open-weight models
Kimi benchmark highlight
Ranked #1 on LMArena's Frontend Code Arena, ahead of GPT and Claude on that leaderboard
Which one should you use?
Choose DeepSeek if you want:
- Technical & reasoning-heavy tasks
- Cost-conscious usage
- Developers wanting open weights
- Structured, multi-step problems
Choose Kimi if you want:
- Large document analysis
- Long-context research
- Cost-efficient agentic tasks
- Processing books & lengthy reports
Standout strengths
DeepSeek
Efficient architecture
Activates only a fraction of its total parameters per task, cutting cost and memory use sharply.
Dedicated reasoning model
Uses reinforcement learning to work through complex, multi-step problems methodically.
Open-weight availability
Popular with developers who want to self-host, fine-tune, or build on top of the model directly.
Kimi
1M-token context window
Reads entire books, research papers, or codebases in a single pass without losing track.
Fast decoding at scale
A hybrid attention mechanism keeps long-context responses fast instead of slowing to a crawl.
Native vision understanding
Combines strong vision and language understanding in one model, not a separate add-on.
Frequently asked questions
Try DeepSeek and Kimi for free
No credit card, no account required — pick DeepSeek for technical & reasoning-heavy tasks, or Kimi for large document analysis.