Model Comparison
DeepSeek-V4-Pro-Max vs Kimi K2.7 CodeWhich is better in 2026?
Both models are evenly matched across the benchmarks. Kimi K2.7 Code is 1.4x cheaper per token.
Verdict: DeepSeek-V4-Pro-Max vs Kimi K2.7 Code — which is better?
DeepSeek-V4-Pro-Max (by DeepSeek) and Kimi K2.7 Code (by Moonshot AI) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.
DeepSeek-V4-Pro-Max outperforms in 1 benchmarks (LiveBench), while Kimi K2.7 Code is better at 1 benchmark (MCP Atlas). Both models are evenly matched across the benchmarks.
On price, Kimi K2.7 Code is roughly 1.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4-Pro-Max also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.
Choose DeepSeek-V4-Pro-Max if…
- you process long inputs — it offers a 1,048,576 token context window
Choose Kimi K2.7 Code if…
- cost matters — it's about 1.4x cheaper per token
- you want the most recent training data — it shipped Jun 2026
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V4-Pro-Max outperforms in 1 benchmarks (LiveBench), while Kimi K2.7 Code is better at 1 benchmark (MCP Atlas).
Both models are evenly matched across the benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4-Pro-Max ($1.60/1M tokens) is 2.2x more expensive than Kimi K2.7 Code ($0.74/1M tokens).
For output processing, DeepSeek-V4-Pro-Max ($3.20/1M tokens) is 1.1x cheaper than Kimi K2.7 Code ($3.50/1M tokens).
In conclusion, DeepSeek-V4-Pro-Max is more expensive than Kimi K2.7 Code.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4-Pro-Max has 600.0B more parameters than Kimi K2.7 Code, making it 60.0% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4-Pro-Max accepts 1,048,576 input tokens compared to Kimi K2.7 Code's 262,144 tokens. Both models can generate responses up to 131,072 tokens.
Input Capabilities
Supported data types and modalities
Kimi K2.7 Code supports multimodal inputs, whereas DeepSeek-V4-Pro-Max does not.
Kimi K2.7 Code can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4-Pro-Max
Kimi K2.7 Code
License
Usage and distribution terms
DeepSeek-V4-Pro-Max is licensed under MIT, while Kimi K2.7 Code uses Modified MIT License.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Modified MIT License
Open weights
Release Timeline
When each model was launched
DeepSeek-V4-Pro-Max was released on 2026-04-23, while Kimi K2.7 Code was released on 2026-06-12.
Kimi K2.7 Code is 2 months newer than DeepSeek-V4-Pro-Max.
Apr 23, 2026
2 months ago
Jun 12, 2026
1 months ago
1mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-V4-Pro-Max is available from Novita, DeepInfra, DeepSeek, Fireworks, Together. Kimi K2.7 Code is available from DeepInfra, Fireworks, Moonshot AI, Novita, Together.
DeepSeek-V4-Pro-Max
Kimi K2.7 Code
Outputs Comparison
Key Takeaways
DeepSeek-V4-Pro-Max
View detailsDeepSeek
Kimi K2.7 Code
View detailsMoonshot AI
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against DeepSeek-V4-Pro-Max and Kimi K2.7 Code side-by-side, then vote on the output you prefer.
| Feature | | |
|---|
FAQ
Common questions about DeepSeek-V4-Pro-Max vs Kimi K2.7 Code.