What is Kimi K3?
Kimi K3 is Moonshot AI’s most advanced model, designed for high-end reasoning, software engineering, multimodal understanding, knowledge work, and agentic AI applications. The model has 2.8 trillion parameters and is built on Kimi Delta Attention, a hybrid linear attention mechanism created for long-context performance. It also uses Attention Residuals and supports a native context window of up to 1 million tokens. This makes Kimi K3 suitable for tasks involving large codebases, long research materials, enterprise documentation, multi-file analysis, legal documents, technical manuals, and complex workflows. Kimi K3 always has thinking mode enabled, with reasoning effort configured through the reasoning_effort field and maximum effort currently supported as the default. Developers can use the model through an OpenAI-compatible API, making it easier to integrate with existing SDKs, clients, and application infrastructure. The model supports streaming responses with separate reasoning and final-answer deltas, allowing applications to display reasoning progress and final content differently. Kimi K3 also supports strict structured output with JSON Schema, partial mode for continuing from a prefix, custom tool calling, required tool use, and dynamic tool loading through system messages. Its vision capabilities support image and video inputs through base64 or uploaded files, enabling analysis of visual content alongside text. Automatic context caching helps workflows that reuse long prefixes, such as large knowledge bases or persistent system context, without requiring developers to manage cache IDs manually. By combining frontier-scale parameters, long-context processing, visual input, structured outputs, tool orchestration, and developer-friendly API compatibility, Kimi K3 gives teams a strong foundation for advanced AI agents, coding assistants, research systems, enterprise automation, and multimodal applications.
Pricing
Cached input: $0.30
Uncached input: $3.00
Output: $15.00
Context window: 1,048,576 tokens
Cached inputs cost 90% less than uncached inputs, while generated output is the most expensive token category. Prices exclude applicable taxes, which are calculated based on the customer’s jurisdiction.
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Epic model
Date: Jul 16 2026SummaryFive stars from me. Kimi K3 looks like one of the more interesting models right now for developers building serious AI agents, coding assistants, and knowledge-work automation. The combination of 1M-token context, native vision, tool calling, long-horizon coding focus, and massive model scale makes it feel purpose-built for the next wave of agentic development.
PositiveKimi K3 looks seriously exciting from the perspective of a developer and AI agent builder. The 1M-token context window is the kind of thing that actually matters when you are working with large repos, long docs, product specs, logs, and messy multi-step agent workflows.
I also like that Kimi is positioning it around long-horizon coding and end-to-end knowledge work, not just generic chat. The native visual understanding, tool calling support, and deep reasoning focus make it feel like a model built for agents that need to read, plan, inspect, code, and iterate across a real workflow.
The 2.8T-parameter scale is also hard to ignore. If the real-world performance matches the positioning, Kimi K3 could be a very strong option for developers who want frontier-level capability with long context and multimodal inputs in the same stack.NegativeIt is still new, so I would want to test it heavily before depending on it for production agents. Big context windows are useful, but they do not automatically guarantee perfect repo understanding, reliable tool use, or consistent long-running execution.
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