MoonshotAI: Kimi K2 0711
MoonshotAI • LLM
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...
Specifications
Context Window
131K tokens
Input Price/1M
$0.57
Output Price/1M
$2.30
Parameters
—
Max Output
33K tokens
Benchmarks
MoonshotAI: Kimi K2 0711 results on the main AI model evaluation benchmarks. Higher scores indicate better performance.
overall
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| LMArena Elo | 1417.0 | 2000.0 | Crowdsourced blind pairwise comparisons |
Information
- Tool Calling
- ✅ Supported
- Vision
- ❌ Not supported
- Audio
- ❌ Not supported
Full Analysis: MoonshotAI: Kimi K2 0711
What is MoonshotAI: Kimi K2 0711?
MoonshotAI: Kimi K2 0711 is an AI model developed by MoonshotAI, classified as a large language model (LLM). It focuses on text processing and natural language generation. As an open source model, it is available for download, customization, and on-premises deployment. With a context window of 131K tokens, it is suitable for processing long documents such as contracts, books, and complete codebases.
Pricing & Costs in 2026
MoonshotAI: Kimi K2 0711 is usage-based, priced at $0.57/1M input tokens and $2.3/1M output tokens. For context: 1 million tokens is approximately 750,000 words, or about 10 average-length books. At this aggressive price point, it is one of the most cost-effective options on the market, ideal for high-volume applications like chatbots, bulk document analysis, and automation.
Benchmarks & Performance
MoonshotAI: Kimi K2 0711 was evaluated on 1 different benchmarks, covering categories like overall. Results show exceptional performance across available evaluations.
It's important to note that benchmarks measure specific aspects and don't capture the full user experience. Factors like instruction adherence, behavior in long conversations, and real-world task quality vary significantly between models and aren't always reflected in standard scores.
Recommended Use Cases
MoonshotAI: Kimi K2 0711 is suitable for a wide range of AI applications: long document analysis (contracts, legal proceedings, codebases), automation with tool calling (API integration, databases, external systems), high-volume chatbots and automated support, text generation, summarization, translation, and general assistance.
Comparison with Alternatives
In the 2026 AI model ecosystem, MoonshotAI: Kimi K2 0711 competes directly with similarly capable models. MoonshotAI competes in this segment against OpenAI, Anthropic, Google, and Meta. The choice between models depends on the specific use case, budget, latency requirements, and need for features like multimodality and tool calling.
For a detailed side-by-side comparison, use our comparison tool or check the overall model ranking.
Frequently Asked Questions
What is MoonshotAI: 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...
How much does MoonshotAI: Kimi K2 0711 cost?
MoonshotAI: Kimi K2 0711 costs $0.57/1M input tokens and $2.3/1M output tokens. For heavy usage (e.g., a chatbot handling 100k messages/month), costs can range from $10 to $1,000 depending on volume.
How does MoonshotAI: Kimi K2 0711 compare with other models?
In available benchmarks, MoonshotAI: Kimi K2 0711 scored: LMArena Elo: 1417/2000. See the full table above for a detailed comparison.
Is MoonshotAI: Kimi K2 0711 open source?
Yes, MoonshotAI: Kimi K2 0711 is an open source model. You can deploy it on-premises, customize it via fine-tuning, and maintain full control over your data. Check the official repository for the specific license.
What is MoonshotAI: Kimi K2 0711 best for?
MoonshotAI: Kimi K2 0711 excels at general-purpose language tasks. With its large context window, it handles long documents, codebases, and extended conversations. It supports tool calling for API integrations and automation.
Last updated: May 15, 2026 • View methodology →