Kimi K3 Pricing: API Cost, App Tiers, and What You Actually Pay

Kimi K3 costs $3.00 per million input tokens and $15.00 per million output tokens through the Moonshot API. Cached input drops to $0.30, a 90% discount. The price is flat across the full one million token context window, with no long-context surcharge.

Those are the numbers. The rest of this article explains what they mean in practice, because the sticker price is only part of the story.

Kimi K3 API pricing

All prices are USD per one million tokens, from Moonshot's official pricing page.

Pricing as of July 2026. Source: Moonshot AI.

Three things about this pricing matter more than the headline numbers.

The cache discount is the real story. A cached input token costs one-tenth of a fresh one. For any workload where you send the same system prompt, documentation, or repository context across requests (coding agents, RAG pipelines, long chats), most input hits the cache. OpenRouter reports a 92% cache hit rate on K3 traffic. At that rate, the weighted average input price drops from $3.00 to about $0.52 per million tokens. Moonshot credits this to its Mooncake split-inference architecture, which handles caching automatically with no separate write fee.

There is only one K3 SKU. The model always reasons at maximum effort. The reasoning_effort parameter only supports max at launch. There is no cheaper "non-thinking" variant and no way to dial reasoning down for simple tasks. Every token of internal reasoning counts as output and is billed at $15 per million. For routine queries where a lighter thinking trace would suffice, you pay the full reasoning rate regardless.

Flat pricing across the full 1M context. K3 charges the same rate whether your prompt is 1,000 tokens or 1,000,000 tokens. GPT-5.6 Sol applies a 2x input and 1.5x output surcharge above 272K tokens. Claude and most other current models also price flat, but Sol's surcharge makes K3's flat rate a specific advantage for long-context workloads against OpenAI's flagship.

For a comparison of how K3 competes with GLM 5.2 on price (GLM is roughly 3x cheaper per output token), see our dedicated breakdown.

What K3 pricing means for real workloads

The per-token price is easy to compare. The per-task price is what actually matters for budgeting.

Artificial Analysis measures cost per completed task across its Intelligence Index evaluation suite, which tests coding, reasoning, agentic work, and knowledge retrieval. K3 costs $0.95 per task. For the full Intelligence Index evaluation (all nine benchmarks), K3 cost $2,710 in total and generated 130 million output tokens.

That output volume is the hidden cost driver. K3 is verbose. It generated roughly 2x the median output volume among comparable reasoning models (130 million tokens vs a 63 million median). Cheaper per token, but it uses more tokens. The sticker savings are real but smaller than the rate card implies.

Cost per task: K3 vs the field

All figures from Artificial Analysis, July 2026. Cost per task uses weighted average across nine Intelligence Index evaluations.

K3 sits in the middle of the field on cost per task. It is cheaper than every model that scores higher (Sol, Fable 5) and more expensive than every model that scores lower (GLM, Terra). That is roughly what you would expect from a well-priced model: you pay for what you get.

The comparison that surprises: K3 generates 24K output tokens per task while Sol generates only 15K, yet K3 still costs less ($0.95 vs $1.04). K3 is more verbose but its lower per-token rate ($15 vs $30 output) more than compensates.

What $100 of API spend buys you

Calculated from official output pricing and Artificial Analysis cost-per-task data.

For a team running 1,000 agent tasks per month, the monthly API bill looks roughly like: K3 at $950, Sol at $1,040, GLM 5.2 at $470, Opus 4.8 at $1,800. Those numbers shift with cache hit rates and workload mix, but the ranking is stable.

The always-on reasoning cost

This is the pricing detail most articles skip, and it is the one most likely to surprise your budget.

K3 always thinks. Every request runs through a full reasoning trace before producing the visible answer. Those reasoning tokens are billed as output at $15 per million. You cannot turn reasoning off. You cannot reduce it. The reasoning_effort parameter only accepts max.

What this means in practice: a simple question that a non-reasoning model would answer in 200 tokens might cost K3 1,000-3,000 tokens (200 answer + 800-2,800 reasoning). That is 5-15x more output tokens for the same visible response.

TrilogyAI's StackPerf benchmark exposed the variance directly. Across 30 identical prompts sent through Kimi Code, reasoning token usage varied by 275%. Four of those requests consumed 4,093 of the available 4,096 completion tokens on reasoning alone and returned no visible answer. The reasoning trace ate the entire budget.

For teams running mixed workloads (easy and hard tasks together), this is where Claude and GPT-5.6 have a structural advantage. Claude Opus 4.8 offers five effort levels (low, medium, high, xhigh, and max). GPT-5.6 Sol, Terra, and Luna offer full effort control plus a three-tier family for routing by difficulty. K3 gives you one setting. Moonshot has promised lighter modes but has not given a date.

Kimi K3 compared to other models on price

Sorted by output price, lowest first.

Pricing as of July 2026. Sources: official vendor pricing pages. DeepSeek V4 figures verified against DeepSeek API docs; V4 Pro reflects the standing 75% promotional discount.

K3 lands at the same price point as Claude Sonnet 5 ($3/$15). It is a notable step up from its predecessor: Kimi K2.6 ran roughly $0.95/$4, so K3 is approximately 3.2x more expensive on input and 3.75x on output.

The pricing positions K3 as a capability-priced model, not a budget option. Moonshot is charging for the quality, not undercutting Western models on price. Compared to the K2 generation, the narrative has shifted from "cheap Chinese AI" to "competitive with the Western frontier on both capability and price."

Kimi app subscription tiers

If you are using the Kimi consumer app (not the API), pricing works through subscription tiers. All plans include K3 access.

Pricing from kimi.com/membership/pricing, July 2026.

Two details worth noting. First, Kimi's pricing page carries a banner that new plans are coming and that Kimi and Kimi Code benefits will be separated into different products. If you are subscribing primarily for the coding tool, expect that to become its own subscription. Second, only the Allegro ($99) and Vivace ($199) tiers include K3's full 1M context window. The lower tiers cap context at a shorter length.

For coding-heavy users, the Allegro tier at $99 has drawn favorable comparisons. One Hacker News user reported that the Allegro tier's Kimi Code quota roughly matched what they got from Anthropic's $200 plan when primarily using Fable 5. (Note: Kimi's pricing page indicates Kimi Code benefits may be separated into their own subscription in the future, which would change this comparison.)

The price of self-hosting K3

K3's open weights arrive July 27, 2026 under a Modified MIT license. Once they do, the API becomes optional, but "free weights" does not mean "free to run."

At 2.8 trillion parameters, K3 is a large model to serve. Moonshot recommends 64+ accelerators. Even with MXFP4 quantization, the model footprint is roughly 1.4 TB before KV cache. That puts the hardware requirement in a fundamentally different class from models like GLM 5.2 (which fits on a single 8x H200 node at FP8).

For most teams, the API is the practical path. Self-hosting makes sense for organizations with existing GPU clusters, strict data residency requirements, or high enough volume that the per-token API cost exceeds the amortized cost of owned hardware.

One compliance consideration that Layer3Labs flags: the Moonshot API is China-hosted. For regulated industries (healthcare, finance, government), data-residency rules may require self-hosting or a compliant deployment through a third-party provider like Fireworks or OpenRouter, rather than sending data to Moonshot's infrastructure directly.

For Kimi K3 alternatives that offer open weights at a lower self-hosting bar, see Emergent's dedicated roundup.

Beyond the pricing comparison

If you are comparing K3 pricing to figure out which model to build your app on, there is a simpler path. Emergent lets you describe an application in plain language and get a production-ready, full-stack product with a real backend, real integrations like Stripe, MongoDB, and Shopify, and code you own. It runs Claude, OpenAI GPT, and Google Gemini under the hood through its Universal LLM Key, so you get frontier model capabilities without managing API keys or comparing token prices.

Skip the pricing spreadsheets. Describe your app and let Emergent handle the rest. Start Building and see how far a prompt gets you.