kimi-k2.6
Input price$0.95
Output price$4
Moonshot AITextVision
kimi-k3
kimi-k3 is Moonshot AI's model at $3.00 input and $15.00 output per million tokens, with a 1000K context window. On a typical 3:1 input-to-output mix that blends to roughly $6.00 per million tokens, placing it in the premium band of the 68 models tracked here — cheaper than 7% of them.
Context window1000K
Input price$3 / 1M
Output price$15 / 1M
Output speed60 tok/s
Quality score95
Data updatedUpdated Aug 2026
Price verified2026-08-06 · Official
Price noteFlat across the full context; every request runs a thinking pass billed as output tokens
By price, kimi-k3 ranks 59th cheapest of 68 on input and 63rd on output. Its quality score of 95 ranks 6th of 68, and on value (quality score ÷ output price) it ranks 59th of 68. Its blended rate sits 118% above the catalog average.
In real billing terms: a support chatbot handling 50,000 turns a month (2,000 input / 500 output tokens each) costs about $675 per month on kimi-k3, while a RAG workload of 20,000 queries (8,000 input / 800 output tokens each) runs about $720. Because output is priced 5.0× input here, long-generation tasks scale cost fastest — cap output length before you optimise anything else.
A 1000K context window holds roughly 1,333 pages of A4 text (at ~750 tokens per page), ranking 22nd of 68 here. That is enough to drop in an entire technical manual or several source files at once, which suits long-document summarization and codebase-wide analysis. Output runs at about 60 tok/s (55th fastest of 68), acceptable interactively, though long responses will feel slow.
We track 2 Moonshot AI models, and kimi-k3's blended rate sits 56% above that provider's average. If your task tolerates a little less headroom, deepseek-reasoner-v4 (DeepSeek) blends about 91% cheaper at a quality score of 92, and is worth benchmarking first. Going the other way, claude-opus-5 scores 97 on quality for about 11% less.
The closest model in Moonshot AI's line-up is kimi-k2.6 ($0.95 input / $4.00 output, quality 90). Against it, kimi-k3 scores 5 points higher on quality, runs 25 tok/s slower (about 29%), has a 738K larger context window, costs 71% more blended. When two models sit this close on price, the deciding factor is usually the quality ceiling — run both against your real prompts before picking.
Estimated monthly cost
Monthly API spend across four common workloads, next to the cheapest model we track.
| Workload | Usage assumption | This model | Cheapest model |
|---|---|---|---|
| Support chatbot | 2,000 input / 500 output tokens per turn, 50,000 turns per month | $675 | $3.00 |
| RAG document Q&A | 8,000 input / 800 output tokens per query, 20,000 queries per month | $720 | $3.84 |
| Coding assistant | 12,000 input / 2,000 output tokens per request, 10,000 requests per month | $660 | $3.20 |
| Batch summarization | 4,000 input / 400 output tokens per item, 200,000 items per month | $3,600 | $19 |
Derived directly from the rates on this page. Excludes prompt caching, batch discounts and free tiers.
How it compares
The alternatives worth benchmarking alongside it, from the same dataset.
| Model | Provider | Blended /1M | Quality | Price delta |
|---|---|---|---|---|
| kimi-k3 | Moonshot AI | $6.00 | 95 | — |
| deepseek-reasoner-v4Cheaper option | DeepSeek | $0.54 | 92 | -91% |
| claude-opus-5Higher quality | Anthropic | $5.31 | 97 | -11% |
| kimi-k2.6Same provider | Moonshot AI | $1.71 | 90 | -71% |
| qwen3.5Best value overall | Alibaba | $0.11 | 91 | -98% |
Best for
ReasoningCodingLong Context
Modalities
TextVision
Where it fits — and where it doesn't
Good fit
- Coding: writing, refactoring and debugging across multi-file project context.
- Reasoning: multi-step decomposition, planning and tasks that need rigorous inference.
- Long documents: a 1000K window ingests a full contract, paper or large codebase in one pass.
- Multimodal input: handles images, screenshots and scanned documents directly.
Poor fit
- High-frequency batch jobs: at $6.00 blended per million tokens, bills scale quickly at volume.
Frequently asked questions
How much does the kimi-k3 API cost?
kimi-k3 costs $3.00 per million input tokens and $15.00 per million output tokens. On a 3:1 input-to-output mix that blends to about $6.00 per million tokens.
What does kimi-k3 cost per month?
It depends on volume. Using the four worked scenarios on this page: a support chatbot runs about $675/month, RAG document Q&A about $720/month, a coding assistant about $660/month, and batch summarization about $3,600/month. Plug your own token counts into the cost calculator for a tailored figure.
How large is the kimi-k3 context window?
1000K tokens — roughly 1,333 pages of A4 text at ~750 tokens per page, ranking 22nd of the 68 models tracked here.
kimi-k3 or deepseek-reasoner-v4 — which is better value?
deepseek-reasoner-v4 blends to about $0.54 per million tokens versus $6.00 for kimi-k3 — roughly 91% cheaper — at a quality score of 92 against 95. If that quality gap does not show up on your task, take the cheaper one; if it does, stay with kimi-k3. Benchmark both on your own data before deciding.
What is kimi-k3 best suited for?
Tagged use cases: reasoning, coding, long-context. Coding: writing, refactoring and debugging across multi-file project context. Less suitable: High-frequency batch jobs: at $6.00 blended per million tokens, bills scale quickly at volume.
How current is this kimi-k3 pricing?
Pricing here was last verified on 2026-08-06 against official documentation. llmprice.app runs a daily collection job, but providers can change rates between runs — confirm against Moonshot AI's official pricing page before you commit.
Estimate the real cost of this model
Bring tokens per request and monthly volume into the calculator to compare total model costs.
Related models
Continue by provider and use case.
View model
claude-opus-5
Input price$5
Output price$6.25
claude-opus-4-8
Input price$5
Output price$6.25
Pricing, speed and quality scores change over time. Confirm official documentation before production use.