Moonshot Kimi K2 Thinking — most advanced open reasoning model in the K2 series, agentic long-horizon tasks, 256k context.
Kimi K2.6 is a flagship multimodal model from Kimi, optimized for tasks that require reasoning over long contexts and multiple input modalities. It processes text, images, and video, with a context…
Kimi K2.6 excels at multi-step reasoning, including mathematical, logical, and tool-use reasoning. Its strong performance on τ²-Bench (95.9) demonstrates its ability to follow complex instructions, call external tools, and synthesize results. The model can handle chain-of-thought prompts, decompose problems into sub-tasks, and maintain consistency over long exchanges. It is also capable of reasoning about visual content—for example, answering questions about a series of images or a video clip—and combining visual cues with textual context. While it is not specifically benchmarked on pure code generation or creative writing, its general reasoning ability suggests it can handle such tasks when given clear instructions. The model's large context window supports reasoning over lengthy documents, enabling tasks like contract analysis or research synthesis.
Yes, Kimi K2.6 accepts video as input, making it suitable for video understanding tasks. The model can process video clips and answer questions about their content, such as identifying objects, actions, or scenes. Because the context window is 262,144 tokens, longer videos may need to be sampled or summarized to fit within the token budget. The model does not provide frame-by-frame output but rather a single text response based on the entire video input. Use cases include video summarization, event detection, and content moderation. For best results, ensure video files are encoded in a widely supported format and consider using lower resolution to reduce token consumption. The model's pricing applies to video input tokens as well, so large videos can quickly accumulate costs.
While Kimi K2.6 supports text, image, and video inputs, it does not natively process audio. Any audio in video files is not interpreted unless it is transcribed to text separately. The model's understanding of visual content is limited to what can be represented within the token budget—very high-resolution images or long videos may be downsized or truncated. The model is also not optimized for real-time processing; response latency will vary based on input size and complexity. For tasks requiring precise spatial reasoning (e.g., object detection coordinates), the model may provide approximate descriptions rather than exact numeric outputs. Developers should test the model on representative samples of their visual data to ensure acceptable accuracy. When visual fidelity is critical, consider using specialized computer vision models and combining their output with Kimi K2.6's reasoning pipeline.
Kimi K2.6 is a flagship model with premium pricing ($0.95/1M input, $4.00/1M output). For tasks that do not require its unique strengths—such as short text generation, simple chat, or basic summarization—a cheaper model can deliver adequate results at a fraction of the cost. Examples of cheaper alternatives available on OrcaRouter include smaller Kimi models or other providers' budget tiers. If your task involves no visual inputs and can be completed within a smaller context window (e.g., 8k tokens), a model with lower token costs may be more economical. Additionally, if latency is a primary concern and you do not need the highest reasoning accuracy, a faster, cheaper model might be preferable. Always evaluate whether the performance gain from Kimi K2.6 justifies the additional expense for your specific use case.
τ²-Bench is a benchmark designed to evaluate tool-use and reasoning capabilities in AI agents. A score of 95.9 indicates that Kimi K2.6 can successfully complete complex tasks that involve calling external tools, following multi-step instructions, and synthesizing outputs. This benchmark tests realistic scenarios such as browsing the web, querying databases, or using APIs. The high score suggests that Kimi K2.6 is especially suited for agentic workflows where reliable tool execution is critical. Note that this single metric does not cover all aspects of performance, such as creativity or factual accuracy in open-ended generation. Developers should supplement with custom evaluations relevant to their domain. The score is reported by the model provider and has not been independently verified by OrcaRouter.
Beyond τ²-Bench, Kimi K2.6's performance on other common benchmarks (e.g., MMLU, HumanEval) has not been provided. Its τ²-Bench score of 95.9 is a strong indicator of reasoning and tool-use ability, but without additional numbers, direct comparisons to other flagship models are limited. Models from other providers may outperform Kimi K2.6 on code generation or mathematical reasoning benchmarks. When selecting a model, consider the specific benchmarks that matter for your application. If your use case is not centered on tool-use, the τ²-Bench score alone should not be the deciding factor. OrcaRouter offers multiple flagship models; you can run your own test suites to compare performance on your data.
Exact latency figures for Kimi K2.6 are not published. As a flagship model with a 262k context window, inference times are expected to be higher than smaller or specialized models. Factors affecting latency include input token count, output token count, and server load. On OrcaRouter, the model is accessed via the standard OpenAI-compatible API, so typical response times may range from a few seconds for short inputs to tens of seconds for long, complex queries. For throughput, the number of concurrent requests you can send is subject to rate limits defined by the provider and OrcaRouter's infrastructure. Developers should plan for higher latency when using the full context window and consider caching or asynchronous processing for production workloads.
While Kimi K2.6 excels in tool-use reasoning (τ²-Bench 95.9), it may have weaknesses in other areas. No benchmark scores are provided for coding, mathematics, or multilingual tasks, so its performance in these domains is unknown. Like all large language models, Kimi K2.6 can produce plausible-sounding but incorrect information, especially on niche or recent topics. Its reasoning can be brittle if prompts are not carefully structured. The model's multimodal understanding may miss subtle details in images or video, particularly when objects are small or occluded. There is no information on its performance in adversarial settings or under constrained budgets. Developers should conduct their own evaluation on representative tasks and be aware that a single benchmark does not guarantee real-world reliability.
Kimi K2.6 is billed at the provider's rate with zero markup through OrcaRouter. The cost is $0.95 per 1 million input tokens and $4.00 per 1 million output tokens. Both input and output tokens include all text, image, and video tokens processed. There are no additional platform fees or per-request charges. Pricing is transparent and you only pay for the tokens used. Because there is no markup, the price you see is the same as the provider's direct rate. This makes it easy to estimate costs based on your expected token usage. For example, a query with 5,000 input tokens and 1,000 output tokens would cost $0.00475 for input and $0.004 for output, totaling $0.00875.
Kimi K2.6's pricing is higher than many smaller models. For tasks that can be accomplished with fewer tokens or with a cheaper model, the cost difference can be significant. For instance, a cheaper model might cost $0.15 per 1M input tokens, making Kimi K2.6 about six times more expensive for input tokens and even more for output. However, if the flagship model can complete a task in one call that would require multiple calls with a cheaper model, the total cost may be comparable. Also, the 262k context window allows for large inputs, but filling that context drives up costs proportionally. Consider batching requests or using prompt compression to reduce token count. OrcaRouter does not offer caching discounts or special pricing tiers for this model; you pay per token at the listed rates.
No, OrcaRouter does not currently offer caching, volume discounts, or special pricing tiers for Kimi K2.6. The model is billed strictly on a per-token basis at the provider's rate with zero markup. There is no discount for repeated prompts or for high-volume usage. If you have very high token consumption, you may want to contact OrcaRouter for custom enterprise agreements, but standard pricing applies by default. Note that caching of responses is not managed by OrcaRouter; you can implement your own cache layer to avoid re-sending identical prompts, thereby reducing token costs. Because the provider rate is passed through directly, there is no opportunity for OrcaRouter to offer a lower price than the provider's listed rate.
Kimi K2.6 is accessed via OrcaRouter's OpenAI-compatible API. Set the base URL to https://api.orcarouter.ai/v1 and use the model identifier "kimi/kimi-k2.6". You will need an API key from OrcaRouter. The API supports the same endpoints as OpenAI's Chat Completions API, including optional parameters such as temperature, max_tokens, top_p, frequency_penalty, and presence_penalty. To pass images or videos, use the content array format with type "image_url" or "video_url" (with appropriate URLs). Note that video input is experimental; check OrcaRouter documentation for supported formats. A typical request body looks like: {"model": "kimi/kimi-k2.6", "messages": [{"role": "user", "content": [{"type": "text", "text": "Describe this image"}, {"type": "image_url", "image_url": {"url": "..."}}]}]}.
When using the OrcaRouter API to call Kimi K2.6, you can set standard OpenAI parameters: temperature (0-2, default 1), max_tokens (up to 32768), top_p, frequency_penalty, presence_penalty, stop sequences, and stream (boolean). The model also respects the system message for setting context. For multimodal inputs, include content items of type "text", "image_url", or "video_url". The "video_url" type may require additional fields like "format" and "duration"; refer to OrcaRouter's documentation for exact syntax. Currently no parameter for controlling visual detail level (like "low" or "high") is confirmed. The model does not support function calling or tools directly; however, you can simulate tool use by including tool descriptions in the system prompt and parsing the output. Streaming is supported for real-time output.
Migrating to OrcaRouter's API for Kimi K2.6 requires changing only the base URL and model ID in your existing code. If you are using the OpenAI Python client, set the base_url to "https://api.orcarouter.ai/v1" and model to "kimi/kimi-k2.6". Update your authentication to use an OrcaRouter API key. No changes are needed to the message format or parameter names, as they are fully compatible. If you previously used a different provider's API that was also OpenAI-compatible, the migration is straightforward. Note that token pricing may differ, so adjust cost monitoring accordingly. Test with a small sample to ensure expected behavior. OrcaRouter's API maintains the same streaming and non-streaming endpoints, so existing logic for handling responses should work without modification.
When you send data to Kimi K2.6 through OrcaRouter, the request is forwarded to the Kimi provider's servers for inference. OrcaRouter does not store your prompts or responses beyond the minimal duration needed to pass them to the provider. However, the Kimi provider may have its own data handling policies. OrcaRouter recommends reviewing the provider's privacy and data retention terms. For sensitive data, consider whether the provider's jurisdiction and policies align with your data governance requirements. OrcaRouter itself does not train on your data, and it does not share data with third parties beyond the provider. To minimize exposure, avoid sending personally identifiable information (PII) unless necessary and evaluate if the use case justifies the risk. No data retention guarantees are provided by OrcaRouter for this model beyond what is stated in their terms of service.
Kimi K2.6 is one of several multimodal models available on OrcaRouter. Its key differentiators are the large context window (262k tokens) and strong τ²-Bench score (95.9). For comparison, other multimodal models may have smaller context windows (e.g., 128k or 32k) but may offer lower pricing or better performance on visual tasks like object detection. Some models specialize in image generation or have higher frame processing rates for video. Kimi K2.6's pricing is at the higher end among multimodal models, though some proprietary models can be more expensive. When selecting a multimodal model, consider not only input modalities but also output modality (text only here), context length, and benchmark scores. OrcaRouter provides a model comparison table in the catalog to help you evaluate trade-offs.
Kimi K2.6 is the flagship model from Kimi. It sits above Kimi's smaller or cheaper models in terms of performance and pricing. For instance, Kimi may offer a lightweight model with a 128k context window at a lower token cost. The exact lineup of Kimi models on OrcaRouter is subject to change, but typically the trade-off is between lower cost versus higher accuracy, larger context, and multimodal support. If your use case does not require the extreme context length or the τ²-Bench performance, a cheaper Kimi model may suffice. However, only Kimi K2.6 supports video input at present. Benchmark scores for other Kimi models have not been provided, so direct comparison on reasoning is not possible. Check OrcaRouter's model list for current offerings.
Without direct benchmark comparisons, the choice is driven by available specifications. Kimi K2.6 offers a 262k context window, which is larger than GPT-4o (128k) and Claude Opus (200k). Its τ²-Bench score of 95.9 is competitive, but Claude and GPT-4o may have better performance on other benchmarks like MMLU or coding. Kimi K2.6's pricing is moderate ($0.95/$4.00 per 1M tokens) versus GPT-4o ($5.00/$15.00) and Claude Opus ($15.00/$75.00) in their standard rates—though those have different context lengths and features. Kimi K2.6 also supports video input, which not all models do. Ultimately, choose Kimi K2.6 if you need maximum context or strong tool-use reasoning, and if you are comfortable with its provider's data policies. OrcaRouter allows you to test multiple models side-by-side to find the best fit.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key=os.environ["ORCAROUTER_API_KEY"],
)
response = client.chat.completions.create(
model="kimi/kimi-k2.6",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)frequency_penaltyinclude_reasoningmax_completion_tokensmax_tokensnpresence_penaltyprompt_cache_keyreasoningresponse_formatsafety_identifierstopstreamstream_optionstemperaturethinkingtoolstop_p| Input / 1M tokens | $0.950 |
| Output / 1M tokens | $4.00 |
| Cache read / 1M | $0.160 |
| Currency | USD |
Estimate based on list price
Estimate only — actual token counts depend on the provider's tokenizer.
What developers are saying this week
GET /api/public/models/kimi/kimi-k2.6Open @misc{orcarouter_kimi_k2_6,
title = {kimi/kimi-k2.6 API},
author = {kimi},
year = {n.d.},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/kimi/kimi-k2.6}
}kimi. (n.d.). kimi/kimi-k2.6 API. OrcaRouter. https://www.orcarouter.ai/models/kimi/kimi-k2.6