MoonshotAI: Kimi K2 0711
MoonshotAI's Kimi K2 0711 delivers strong agentic performance with solid coding and instruction-following benchmarks at a reasonable price, making it a capable choice for tool-use and agent workflows — though Western businesses should note that API availability and support may vary compared to established providers.
Assessment date: March 12, 2026
Our methodology takes into account a range of factors including pricing, functionality, capabilities, benchmark performance, and real-world applicability. Rankings are reviewed and updated regularly as new models are released. Issues with our rankings? Contact us
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 agentic capabilities, including advanced tool use, reasoning, and code synthesis. Kimi K2 excels across a broad range of benchmarks, particularly in coding (LiveCodeBench, SWE-bench), reasoning (ZebraLogic, GPQA), and tool-use (Tau2, AceBench) tasks. It supports long-context inference up to 128K tokens and is designed with a novel training stack that includes the MuonClip optimizer for stable large-scale MoE training.
Capabilities
Architecture
| Modality | Text → Text |
| Tokenizer | Other |
Performance Indices
Source: Artificial Analysis
Benchmark Scores
Evaluations
Benchmark data from Artificial Analysis and Hugging Face
Model Information
Pricing
| Token Type | Cost per 1M tokens | Cost per 1K tokens |
|---|---|---|
| Input | $0.55 | $0.000550 |
| Output | $2.20 | $0.002200 |
Live Performance
Live endpoint metrics — refreshed every 30 minutes.
