Kimi K2.6
kimi-k2.6Compare kimi-k2.6 API pricing, supported endpoints, capabilities and access options on Modelsell.
- Starting price
- Input / Output · 1M
- Context
- 262.1K
- Maximum input window
- Max output
- 235.9K
- Maximum tokens per response
- Modalities
- →
Pricing by Supplier
| Input/ 1M tokens | Output/ 1M tokens | Cache Read/ 1M tokens |
|---|---|---|
$0.95 | $4 | $0.16 |
- Input/ 1M tokens
- $0.95
- Output/ 1M tokens
- $4
- Cache Read/ 1M tokens
- $0.16
| Input/ 1M tokens | Output/ 1M tokens | Cache Read/ 1M tokens |
|---|---|---|
$0.95 | $4 | $0.16 |
- Input/ 1M tokens
- $0.95
- Output/ 1M tokens
- $4
- Cache Read/ 1M tokens
- $0.16
Capabilities / Supported modalities
Provider & data privacy
Kimi tokenizerPerformance
Benchmarks
Scores on standardized evaluations. Higher percentages are better — and rank percentile shows
Metrics sourced fromArtificial Analysis 2026-09-30·Kimi K2.6
This model is not included in the current benchmark snapshot.
Missing models or measurements are not zero scores.
Benchmark charts preserve the source model selection and reasoning settings. Missing models or measurements are not zero scores, and benchmark cost or speed is not this site’s service commitment.
About Kimi K2.6
把视觉想法推进到交互实现
Kimi K2.6 是原生多模态智能体模型，适合较长流程的编程、用代码实现设计和任务编排。它可以理解文字与图片，从简单需求或视觉参考出发，协助搭建页面布局、交互元素与轻量全栈流程。
对于设计与开发协作，可以同时提供参考截图、品牌风格和功能要求，让它先解释页面结构，再生成实现方案。想要动画或交互时，应具体描述触发条件、状态变化和用户反馈，而不只写“做得高级”。结合运行截图继续迭代，可以逐步修正间距、层级和交互问题。
它也面向前端、运维和性能优化等工程任务，能够结合代码与工具反馈持续处理问题。复杂工作可以拆成相对独立的研究、实现和检查任务；在支持多智能体调度的应用中，由不同角色并行推进，再汇总为文档、网站或表格成果。
这些执行能力需要文件、终端、浏览器或文档工具支持。纯对话可以得到分析与代码，实际运行和生成文件还要由应用完成。建议为每个阶段设定可检查的输出，例如页面能否完成目标操作、接口失败是否有反馈、最终布局是否符合参考，让迭代围绕具体效果进行。
Use cases and prompting
从设计参考生成页面
“请依据这张参考图实现一个课程目录页。保留清楚的标题层级和卡片节奏，加入主题筛选、课程详情和收藏交互。先拆解组件与状态，再生成代码；运行后根据截图检查排版，最后列出已完成交互和仍需后端支持的部分。”
常见问题
- 生成页面与参考差别很大怎么办？指出具体区域和差异，提供当前运行截图，要求逐项调整布局与样式。
- 能自动运行网站吗？需要应用提供项目与浏览器工具，并检查实际启动和访问结果。
- 多智能体适合什么任务？资料收集、独立模块实现或检查等能分开推进的工作；有依赖的步骤应按顺序执行。
API access
API documentation
Code samples
/v1/chat/completions| Parameter | Type | Default / range | Description |
|---|---|---|---|
temperature | number | = 10 ~ 2 | Sampling temperature; lower is more deterministic |
top_p | number | = 10 ~ 1 | Nucleus sampling probability mass |
max_tokens | integer | >= 1 | Maximum number of tokens in the response |
frequency_penalty | number | = 0-2 ~ 2 | Penalises repetition of frequent tokens |
presence_penalty | number | = 0-2 ~ 2 | Encourages introducing new topics |
stop | array | — | Up to 4 strings that stop generation |
seed | integer | — | Deterministic sampling seed (best-effort) |
n | integer | = 1>= 1 | Number of completions to generate |
stream | boolean | = false | Stream tokens via Server-Sent Events |
response_format | object | — | Force JSON object or schema-conforming output |
tools | array | — | Tool / function declarations the model may call |
tool_choice | string | autononerequired | Tool-choice policy or specific tool name |
logprobs | boolean | = false | Return per-token log probabilities |
top_logprobs | integer | 0 ~ 20 | Number of top log probabilities returned per token |
logit_bias | object | — | Per-token logit bias map |
user | string | — | End-user identifier for abuse monitoring |
Replace <YOUR_API_KEY> with the API key from your token settings.
Authentication
All requests must include Authorization: Bearer <TOKEN> header. Anthropic-formatted endpoints accept the x-api-key header instead.
Generate tokens from the Tokens page; you can scope them to specific models, groups, IPs, and rate-limits.
Supported parameters
| Parameter | Type | Default / range | Description |
|---|---|---|---|
temperature | number | = 10 ~ 2 | Sampling temperature; lower is more deterministic |
top_p | number | = 10 ~ 1 | Nucleus sampling probability mass |
max_tokens | integer | >= 1 | Maximum number of tokens in the response |
frequency_penalty | number | = 0-2 ~ 2 | Penalises repetition of frequent tokens |
presence_penalty | number | = 0-2 ~ 2 | Encourages introducing new topics |
stop | array | — | Up to 4 strings that stop generation |
seed | integer | — | Deterministic sampling seed (best-effort) |
n | integer | = 1>= 1 | Number of completions to generate |
stream | boolean | = false | Stream tokens via Server-Sent Events |
response_format | object | — | Force JSON object or schema-conforming output |
tools | array | — | Tool / function declarations the model may call |
tool_choice | string | autononerequired | Tool-choice policy or specific tool name |
logprobs | boolean | = false | Return per-token log probabilities |
top_logprobs | integer | 0 ~ 20 | Number of top log probabilities returned per token |
logit_bias | object | — | Per-token logit bias map |
user | string | — | End-user identifier for abuse monitoring |
Rate limits
| Supplier | RPM | TPM | RPD |
|---|---|---|---|
| Alibaba | Unlimited | Unlimited | Unlimited |
| official | Unlimited | Unlimited | Unlimited |
No restriction
Frequently asked questions about kimi-k2.6
What is kimi-k2.6?
Compare kimi-k2.6 API pricing, supported endpoints, capabilities and access options on Modelsell.
How do I call kimi-k2.6?
Create an API key with access to kimi-k2.6, then use the exact model ID and a supported endpoint from the API access section. Request fields depend on the selected endpoint.
How is kimi-k2.6 priced?
Pricing depends on the selected provider group and the model billing unit. The current input, output, request, or media prices are shown on this page before sign-up.
What is the context window of kimi-k2.6?
The model catalog lists a context window of 262144 tokens. Check the selected endpoint for request limits.
What is the maximum output of kimi-k2.6?
The model catalog lists a maximum output of 235929 tokens. Your request settings may set a lower limit.
How should I evaluate kimi-k2.6 for my project?
Start with the use cases and prompting guidance on this page, then evaluate the model with representative inputs from your project.