Kimi K3 is Moonshot AI's flagship model and its most capable release to date — a 2.8-trillion-parameter Mixture-of-Experts model built for long-horizon coding and end-to-end knowledge work. It pairs a 1M-token context window with native visual understanding, accepting text and image input with text output, and is designed to stay coherent across very long agentic sessions. Moonshot positions K3 for programming-agent scenarios such as Codex, Claude Code, Cline, and RooCode, as well as deep reasoning and knowledge work. It exposes a top-level reasoning_effort control and native tool calling, and speaks the OpenAI API format for drop-in integration. Note that K3 does not accept sampling parameters (no temperature / top_p / seed) — reasoning depth is controlled through reasoning_effort instead.
MoonshotAI Kimi K3 is a large language model developed by MoonshotAI, a Chinese AI company. It supports a context window of 1,048,576 tokens and accepts both text and image inputs. The model is…
Kimi K3's primary capability is processing a context window of up to 1,048,576 tokens, allowing it to incorporate entire novels, lengthy technical manuals, or extensive conversation histories in a single prompt. It also accepts images, enabling multimodal reasoning. The model can perform tasks such as summarization, question answering, and generation across very long inputs. It is capable of understanding complex instructions and following them over long sequences. It supports OpenAI-compatible API parameters like temperature, max_tokens, top_p, and stop sequences through OrcaRouter.
Kimi K3 is best used when the task inherently requires a very large context—for example, analyzing a 1,000-page document in one go, or maintaining a conversation with a full history of hundreds of messages. For shorter inputs, its higher cost per token ($3/$15 per million) may not be justified. Cheaper models with smaller context windows can handle most typical tasks more efficiently. Use Kimi K3 only when the task demands the full context window to avoid truncation or multiple chunk-and-summarize steps.
Kimi K3 excels at tasks where information is spread across a very long text or includes images. Examples include extracting key facts from a book-length report, answering questions about a complete codebase, comparing multiple research papers, or analyzing a series of diagrams. It can also maintain consistency over long dialogs. Its strength lies in its ability to attend to all parts of the context simultaneously, reducing the need for external retrieval or chunking.
While Kimi K3 has a large context window, it may not always perform as well as smaller, fine-tuned models on narrow tasks. The large context can also increase latency and computational cost. Exact limitations (e.g., maximum image resolution, supported file types, language proficiency) are not specified in the given facts but are inherent to the model's design. Users should be aware that very long prompts consume many tokens and thus incur higher costs. The model is accessed through OrcaRouter; provider uptime and response times apply.
The provided facts do not include specific benchmark scores for Kimi K3. Generally, large-context models are evaluated on tasks like the Needle in a Haystack test, long-document QA, and summarization. MoonshotAI may have published results; users should consult official documentation. The model's performance on standard NLP benchmarks (e.g., MMLU, GSM8K) is not stated. We recommend testing Kimi K3 on your own evaluation set to gauge its effectiveness for your use case.
A 1,048,576-token context window means the model can process roughly 1.5 million English words or 800,000 Chinese characters in a single pass. In practice, this allows handling of entire books, extensive conversation logs, or multimodal data with many images. However, not all tokens may be equally utilized; attention mechanisms sometimes focus more on recent or salient parts. The model's ability to retrieve information from the middle of long contexts can be tested. Performance on such tasks is often better than smaller-context models but may still have room for improvement.
Latency and throughput are not specified in the given facts. Models with very large context windows generally take longer to process each request because the attention mechanism scales quadratically with sequence length. For a full 1M-token output, expect high latency. Through OrcaRouter, you can set max_tokens to limit output length and control response time. For real-time applications, consider using smaller models. Batch processing of longer tasks may be more practical. Actual performance will depend on the provider's infrastructure and current load.
Kimi K3's large context window is a strength but also a challenge. It may not be as accurate on tasks requiring precise reasoning over short inputs compared to specialized models. The model might exhibit lower quality in areas like creative writing or code generation compared to models fine-tuned for those tasks. Additionally, the cost per token is relatively high, so using it for short prompts is inefficient. The model is relatively new; long-term stability and support from MoonshotAI are factors to consider.
Kimi K3 is priced at $3.00 per 1 million input tokens and $15.00 per 1 million output tokens. These are the provider rates with zero markup added by OrcaRouter. Input tokens include both text and image tokens (images are billed based on their token equivalent). Output tokens are any tokens generated by the model. There are no additional fees beyond the per-token costs. Pricing is subject to change by the provider, but OrcaRouter passes through rates without adding a margin.
Because Kimi K3 can handle extremely long contexts, it can replace multiple API calls that would be needed with smaller context models, potentially reducing overall cost for tasks that require full document processing. However, each token is more expensive than many compact models. For example, a 1M-token input at $3.00 is fixed, while a smaller model might cost $0.15 per million but require multiple calls to process the same data. The trade-off is between simplicity and per-token cost. Users should estimate total token usage per task.
The provided facts do not mention any caching or volume discounts for Kimi K3 on OrcaRouter. Pricing is strictly per-token as billed by the provider. OrcaRouter may offer features like request logging or retries, but no specific price reductions are indicated. Users should check OrcaRouter's current pricing page for any updates. In general, high-volume users may negotiate directly with the provider, but through OrcaRouter, the listed rates apply.
Images are converted into tokens for billing purposes. The exact tokenization of images depends on their resolution and the provider's algorithm. Typically, a standard image (e.g., 1024x1024) might cost on the order of hundreds of tokens. Since Kimi K3's input price is $3.00 per 1M tokens, including images in a prompt increases input token count and therefore cost. For workloads with many images, the total cost can add up quickly. It is advisable to minimize image size or include only relevant images to control expenses.
Kimi K3 is accessed through OrcaRouter's OpenAI-compatible API. You send HTTP POST requests to https://api.orcarouter.ai/v1/chat/completions with the model parameter set to "kimi/kimi-k3". The request format is identical to OpenAI's Chat Completions API: include a messages array with system, user, and assistant roles. For image inputs, use the content array with type "image_url". Ensure you have an API key from OrcaRouter. No special configuration is needed; standard parameters like temperature, max_tokens, top_p, and stop are supported.
Through OrcaRouter, Kimi K3 supports standard OpenAI-compatible parameters: temperature (default 0.7), max_tokens (up to the model's output limit, which is not specified but likely less than 32K), top_p, frequency_penalty, presence_penalty, stop sequences, and n (number of completions). The model also accepts a stream parameter for real-time output. For image inputs, you can use the multimodal content format. Unsupported parameters will be ignored. The large context window allows setting a high max_tokens for long outputs, but be mindful of cost.
Migration is straightforward if you already use an OpenAI-compatible API. Change the base URL to https://api.orcarouter.ai/v1, update the model ID to "kimi/kimi-k3", and set your OrcaRouter API key. No code changes are needed if you use the same message format. For image support, ensure your prompts include image URLs or base64 data. You may need to adjust max_tokens and other parameters to fit the model's capabilities. Test with a small sample first to verify output quality and cost.
You need an API key from OrcaRouter. Include it in the Authorization header as Bearer YOUR_API_KEY. OrcaRouter manages authentication and billing. For security, do not share your key. OrcaRouter may also support API key rotation. There is no additional authentication needed from MoonshotAI. All requests go through OrcaRouter's infrastructure, which handles rate limiting and error handling. Ensure your requests comply with OrcaRouter's terms of service.
Kimi K3 offers a context window of 1,048,576 tokens, which is among the largest available. It is comparable to models from other providers that also support million-token contexts. Its pricing of $3/$15 per million tokens is competitive. Unlike some models, Kimi K3 supports multimodal inputs (text and image). A key differentiator is that it is accessed via OrcaRouter with no markup. When compared to models like GPT-4 Turbo (128K context, higher cost), Kimi K3 may be cheaper for very long sequences but may have different quality profiles. Benchmark comparisons are not provided.
Choose Kimi K3 when the task requires the full large context window—for example, analyzing a 500-page document in one chunk, or including many images in a single prompt. Cheaper models with smaller context windows (e.g., 128K tokens) may force you to chunk the input, losing cross-references. If the task can be split without loss of quality, a cheaper model is preferable due to lower per-token cost. Also consider if the model's specific strengths (multimodal, long-context) are critical.
The provided facts only cover Kimi K3. MoonshotAI has previous models like Kimi and Kimi K2. Kimi K3 is the latest with a larger context window and multimodal support. Earlier models likely have smaller contexts and may not accept images. Pricing for other models is not given. For existing users of MoonshotAI's older models, upgrading to Kimi K3 offers expanded capabilities but at a higher cost per token. The decision depends on whether the larger context and image inputs justify the premium.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1from openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key="$ORCAROUTER_API_KEY",
)
response = client.chat.completions.create(
model="kimi/kimi-k3",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)frequency_penaltyinclude_reasoningmax_tokenspresence_penaltyreasoningreasoning_effortresponse_formatstopstructured_outputstool_choicetools| Input / 1M tokens | $3.00 |
| Output / 1M tokens | $15.00 |
| Cache read / 1M | $0.300 |
| Currency | USD |
Estimate based on list price
Estimate only — actual token counts depend on the provider's tokenizer.
@misc{orcarouter_kimi_k3,
title = {Kimi K3 API},
author = {MoonshotAI},
year = {2026},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/kimi/kimi-k3}
}MoonshotAI. (2026). Kimi K3 API. OrcaRouter. https://www.orcarouter.ai/models/kimi/kimi-k3