DeepSeek V4 Pro vs Kimi K2 0711
How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for DeepSeek V4 Pro and Kimi K2 0711.
DeepSeek V4 Pro
DeepSeek V4 Pro is a large-scale Mixture-of-Experts model from DeepSeek with 1.6T total parameters and 49B activated parameters, supporting a 1M-token context window. It is designed for advanced reasoning, coding,...
Kimi K2 0711
Kimi K2 Instruct is a large-scale Mixture-of-Experts (MoE) language model developed by Moonshot AI, featuring 1 trillion total parameters with 32 billion active per forward pass. It is optimized for...
Technical Specifications
| Specification | DeepSeek V4 Pro | Kimi K2 0711 |
|---|---|---|
| Provider | DeepSeek | Moonshot AI |
| Context Window | 1,048,576 tokens | 131,072 tokens |
| Agent Suitability | 94/100 | N/A |
| Time to First Token (TTFT) | 280 ms | N/A |
| Deployment Model | managed api | managed api |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2026-04-24 | 2025-07-11 |
API Pricing Comparison
Input Price per Million Tokens
DeepSeek V4 Pro
$0.43
Kimi K2 0711
$0.57
Output Price per Million Tokens
DeepSeek V4 Pro
$0.87
Kimi K2 0711
$2.30
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Benchmark Performance Metrics
Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.
DeepSeek V4 Pro Quirks & Gotchas
- ▸MoE architecture — cold-start latency on first request, use keep-alive
- ▸Best cost-performance ratio of any frontier model — strong tool calling for agentic use
Kimi K2 0711 Quirks & Gotchas
No developer gotchas reported.