moonshot-v1-8k

moonshot-v1-8k is a efficient-tier language model from Moonshot AI. moonshot-v1-8k is a proprietary model available exclusively through Moonshot AI's API. The Moonshot family is Moonshot AI's model series for this capability tier.

moonshot-v1-8k is priced at $0.12/1M input tokens and $0.12/1M output tokens. Output tokens are 1× more expensive than input tokens — this ratio reflects the sequential nature of autoregressive generation, which requires a separate forward pass per output token versus parallel processing of all input tokens simultaneously. At a typical usage pattern of 1,000 input tokens and 500 output tokens per call, moonshot-v1-8k costs approximately $0.18 per 1,000 API calls. Moonshot AI does not publish a prompt caching rate for this model, so repeated long contexts are billed at the standard $0.12/1M input rate.

Its 8K-token context window is standard for this tier — adequate for most chat and completion tasks, but may require chunking for long-document workloads. Median latency (P50 time-to-first-token) is approximately 300ms — suitable for interactive applications where sub-second response is acceptable. Throughput is approximately 200 tokens/second — fast enough to stream responses at reading speed with headroom for concurrent requests. Maximum output length is 4K tokens — adequate for most generation tasks including essays, code functions, and detailed responses.

moonshot-v1-8k is best suited to chat, cost. Supported capabilities include function calling / tool use for agentic workflows, streaming token output for real-time UIs. Function calling support makes it suitable for building AI agents, structured data extraction pipelines, and any workflow requiring the model to invoke external APIs or return typed JSON. When evaluating this model for a specific workload, the most important factors are: context window (does your input fit?), output price (how long are your generations?), and latency (is this interactive or batch?). The pricing table above shows how moonshot-v1-8k compares to similar models across these dimensions.

moonshot-v1-8k sits in the efficient tier — optimized for speed and cost while maintaining solid quality, ideal for high-volume or latency-sensitive workloads. Unlike open-weights alternatives, moonshot-v1-8k is only available via Moonshot AI's API — there is no self-hosted option, which simplifies deployment but creates vendor dependency. When comparing moonshot-v1-8k to alternatives, use the similar models section on this page to find models at comparable price points with different capability tradeoffs. The intelligence score (55/100) reflects aggregate benchmark performance — a useful proxy for general capability, though real-world performance varies significantly by task type. Always test with your specific prompts and use case before committing to a model for production.