kimi-k2-thinking-turbo is a Kimi K2 Thinking series model launched by Moonshot AI, designed for complex reasoning, multi-step instructions, and agent-like tasks. It is suitable for organizing goals, conditions, and materials to be analyzed into text conversations to progressively develop solutions. On this platform, you can choose Chat Completions to manage message history yourself, or use the hosted conversation endpoint to continuously advance tasks.
Clarify capacity, inputs and outputs, and invocation methods before selecting a model.
Model positioning is a publicly available native capability; message organization, streaming format, and conversation management are platform endpoint features.
Learn what kimi-k2-thinking-turbo can bring to your work.
When a problem includes multiple conditions, mutually constraining goals, or requires phased judgment, you can provide the model with known facts, assumptions, and acceptance requirements together. Its purpose is not merely to generate a single conclusion, but to help address complex problems and produce explanations, plans, and items to be validated that are easy to review and continue discussing.
Suitable for requests that require analyzing materials first, then comparing options, and finally delivering an output. Prompts can specify the order of steps, conditions that must not be overlooked, and the final output format, allowing complex reasoning to advance toward the same goal. When collaborating with tools, the application should handle actual execution and feed the results back into the conversation.
Chat Completions is suitable for applications that already have message-management logic, and can separately handle the response body and any reasoning content that may be returned. The hosted conversation endpoint continues discussions through a conversation id, reducing the work of maintaining history. Both approaches can be used for text tasks; the key consideration is how the application manages context and interactions.
Start with specific tasks to find where the model can make an impact.
Provide project goals, resource constraints, dependencies, and candidate solutions, and ask the model to compare them using consistent criteria and deliver a recommended solution, rationale, and questions to be confirmed. This is suitable for organizing scattered requirements into decision materials for discussion; when real budgets or critical business judgments are involved, final decisions should still be made using verified data.
Put error messages, relevant code text, expected behavior, and methods already tried into the message, and ask the model to distinguish possible causes, list the validation order, and propose modification suggestions. Deliverables can be set as a troubleshooting checklist, repair approach, and testing points; running code and confirming repair results should be handled by the actual development environment.
First submit the task background and delivery requirements, then add conditions, correct assumptions, or compare new solutions turn by turn. Using a hosted conversation id can continue the discussion, making it suitable for iterating on requirements analysis, analytical reports, and task plans. Each turn should ideally identify new information and its impact to prevent the model from relying on assumptions that are no longer valid.
Choose based on task complexity, input materials, and expected results.
kimi-k2-thinking and kimi-k2-thinking-turbo are both public models designed for complex reasoning and multi-step tasks. When choosing Turbo, it is recommended to use your own representative tasks to compare answer completeness, instruction following, and the actual response experience, rather than inferring a fixed acceleration ratio based on the name alone. Existing Turbo applications can retain this model and conduct regression testing around task quality.
When you need to precisely organize messages, handle streaming increments, or carry forward tool results, choose /kimi/chat/completions; when you want to continue a conversation through an id, choose /aichat2/conversations. K3's reasoning intensity parameter and K2.6's thinking switch cannot be directly applied to this model; when migrating versions, you should also adjust the request design.
From a small-scale task to formal integration.
Clarify objectives, required inputs, and output requirements, using real business examples as a starting point.
Open the trial page, confirm the parameters supported by this entry point, then submit a small-scale task to review the results.
Retain the complete model ID, use the request format specified in the documentation, and confirm billing rules on the Pricing page.
Before formal use, understand output quality and capability scope.
Answers to common questions about using kimi-k2-thinking-turbo.
No. It is a publicly released Kimi K2 Thinking series model, launched alongside kimi-k2-thinking. Use the full ID kimi-k2-thinking-turbo when calling it; do not reuse K3's capacity specifications or reasoning control parameters just because it belongs to the Kimi family.
This model should not use K3's reasoning_effort, nor should it use the thinking switch from K2.6. A more practical approach is to clearly specify task goals, constraints, and acceptance criteria, and leave enough room for the response; these prompt designs help organize tasks but are not equivalent to native reasoning budget controls.
When using Chat Completions, the application should continue to include the necessary historical messages in messages. When using the hosted session endpoint, enable stateful and return the same id in subsequent requests. When adding conditions, clearly specify which old assumptions need to be replaced to prevent the discussion from drifting away from the latest requirements.
Streaming increments from Chat Completions may separately contain content and reasoning_content. The interface can handle the main text and reasoning content separately, but must allow the reasoning field to be empty. Check finish_reason at completion; tool calls and length truncation should not be treated as ordinary complete responses.
It is designed for Agent-type tasks, but model capabilities and the actual execution environment are two different things. Direct chat applications need to handle tool execution, check permissions, and return results; when using hosted tool workflows, progress should also be confirmed based on execution events rather than assuming an operation succeeded solely from text generated by the model.
Model information · Updated: 2026-10-01. For call parameters and billing rules, see the API and pricing sections.
Start with clear goals and judge whether it suits your work based on real results.
This is only a basic call example. See the full docs for more parameters and advanced usage.
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