Moonshot AI pricing: API cost per model, user, and month

Moonshot AI lists 10 priced models in the ByteCosts index, with Kimi K2 0711 at about $1.17 per million tokens on ByteCosts' 70% input / 30% output blend. For most AI apps the bill is driven by output tokens, retry rate, and prompt-cache hit rate far more than the headline input price, so Moonshot AI is cost-effective when your workload is input-heavy (RAG, classification) or can cache a large shared prefix. Compare Moonshot AI against alternatives on a real workload - seats, requests, and token mix - before committing to a model or a subscription, because a cheaper per-token price can still lose once power users and long outputs are priced in.

Moonshot AI pricing at a glance

• Cheapest tracked chat model: Kimi K2 0711 at $1.17 per 1M tokens (blended)
• Flagship: Kimi K3 at $6.60 per 1M tokens (blended)
• 10 priced models in the index, updated July 19, 2026

Moonshot AI model prices

Per-million-token list prices for current Moonshot AI models, from the ByteCosts pricing index. Output tokens usually cost several times more than input.

When Moonshot AI is cost-effective

Token price alone does not decide cost. The variables that actually move an AI bill are: how many output tokens each call emits (output is the expensive side), how often calls retry, what fraction of the input prefix can be served from prompt cache, and how heavily your top 1% of users use the product.

Moonshot AI tends to win when your workload is input-heavy with short outputs, when you can cache a large shared system prompt or document context, or when a smaller Moonshot AI model clears your quality bar. It tends to lose when outputs are long and uncached, where a cheaper-per-output model compounds the saving across millions of calls.

Hidden costs to watch with Moonshot AI

List pricing rarely equals your invoice. Budget for these before you commit:

• Output-token blow-up: reasoning and verbose responses multiply the most expensive token class.
• Retries and tool loops: a single failed agent step can re-send the whole context.
• Cache misses: prompt caching only saves money above a break-even reuse count; cold prefixes pay the write premium.
• Power users: a small share of heavy users can dominate spend and erase a plan's margin.
• Egress and gateway fees: LLM gateways and observability add a percentage on top of raw model cost.

Limitations before production billing decisions

Treat ByteCosts calculations as planning estimates, not final billing totals. Real invoices can differ because token mix, retry rate, cache hit rate, rate limits, taxes, gateway fees, regional pricing, and negotiated discounts change the effective cost.

Verify the provider source before production billing decisions, then compare the estimate with your own logs or invoice once production traffic is live.

Lower-blend alternatives to Moonshot AI

On ByteCosts' 70% input / 30% output chat blend, these providers screen lower than Moonshot AI. Treat this as a shortlist, not a decision - check the selected model against your real token mix:

• Mistral - $0.040 blended via Ministral 3B (latest), 28 priced models.
• Cohere - $0.071 blended via Command R7B, 8 priced models.
• Alibaba Qwen - $0.095 blended via Qwen Turbo, 43 priced models.

Frequently asked questions

Moonshot AI pricing. ByteCosts. Updated July 19, 2026. https://bytecosts.com/pricing/moonshotai/

Sources

• Moonshot AI website

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