Google Gemini 3.6 Pro (2M) vs Moonshot Kimi K3.7 (512k)
Side-by-side comparison of pricing, context window, capabilities, and a real-cost sample workload.
Option A
Google Gemini 3.6 Pro (2M)
by Google
Context:2M
Input:$2.00 / 1M
Output:$12.00 / 1M
Released:2026-07
Option B
Moonshot Kimi K3.7 (512k)
by Moonshot AI
Context:512K
Input:$0.60 / 1M
Output:$2.50 / 1M
Released:2026-07
Detailed Comparison
| Dimension | Google Gemini 3.6 Pro (2M) | Moonshot Kimi K3.7 (512k) |
|---|---|---|
| Provider | Moonshot AI | |
| Context window Winner | 2M | 512K |
| Input price ($/1M) Winner | $2.00 | $0.60 |
| Output price ($/1M) Winner | $12.00 | $2.50 |
| Sample workload cost Winner 1M input + 500K output tokens | $8.00 | $1.85 |
| Released TieTie | 2026-07 | 2026-07 |
| Tokenizer | gemini | kimi |
The verdict
On the dimensions we measured, Moonshot Kimi K3.7 (512k) wins more often - particularly on cost-effectiveness for a typical 1M+0.5M workload.
Google Gemini 3.6 Pro (2M) - Key features
Google's flagship Gemini for mid-2026. Gemini 3.6 Pro extends the 2M token window to all paid tiers, improves grounded answers with live Search, and adds native 1 fps video understanding.
- 2M token context window
- Native video understanding at 1 fps
- Grounding with Google Search
- Context caching for whole corpora
- Thinking budget control
Moonshot Kimi K3.7 (512k) - Key features
Moonshot's flagship Kimi K3.7, an open-weight mixture-of-experts model with a 512K token window. K3.7 closes most of the gap to Western frontier models on agentic coding while staying dramatically cheaper.
- 512K token context window
- Open-weight MoE architecture
- Strong agentic tool use
- Aggressive price per token
- Native long-horizon planning
How to choose
- Pick the cheaper model if your workload is mostly straightforward classification, extraction, or summarization.
- Pick the bigger context if you process long documents, large codebases, or multi-document research.
- Pick the more recent release if you need state-of-the-art reasoning quality and don't mind paying a bit more.
- Use both via a routing layer - send simple tasks to the cheaper one and complex tasks to the smarter one. This is the highest-ROI optimization in production AI.
Estimate the real cost of either model for your prompts using our Token Calculator.