Gemini vs Kimi
Google's multimodal powerhouse vs long-context specialist for large documents
Gemini vs Kimi at a glance
| Gemini | Kimi | |
|---|---|---|
| Developer | Google DeepMind | Moonshot AI |
| Latest version | Gemini 3.1 Pro | Kimi K3 |
| Known for | Multimodal | Long-context |
| Context window | Up to 2 million tokens (Pro tier) | Up to 1 million tokens |
| Multimodal support | Text, images, audio, and video | Text, images, and video |
| Open source | No | Partially |
| Official pricing | Free tier available (Flash); Pro tier is paid-only | Free to chat here; API from $0.30 per million cached tokens |
Gemini benchmark highlight
Scores 77.1% on ARC-AGI-2 and 94.3% on GPQA Diamond, leading 13 of 16 benchmarks
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 Gemini if you want:
- Multimodal tasks (image, audio, video)
- Research with huge documents
- Google Workspace users
- Fast, low-cost everyday queries
Choose Kimi if you want:
- Large document analysis
- Long-context research
- Cost-efficient agentic tasks
- Processing books & lengthy reports
Standout strengths
Gemini
True multimodality
Understands and reasons across text, images, audio, and video together, not as separate add-ons.
Massive context window
The Pro tier supports up to 2 million tokens, among the largest context windows of any mainstream model.
Fast, affordable tier
The Flash tier delivers most of the flagship quality at a fraction of the speed and cost.
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 Gemini and Kimi for free
No credit card, no account required. Pick Gemini for multimodal tasks (image, audio, video), or Kimi for large document analysis.