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Different model families have different defaults and constraints for Chat Completions API parameters.

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Parameter Comparison

When temperature is close to 0, n can only be 1. Otherwise, the API returns invalid_request_error.

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Model Parameter Differences

When switching models, you need to look beyond the model field — models differ in which request parameters they support and what defaults they use:
“Fixed” means the parameter cannot be modified: passing any other value returns an error, so do not pass it explicitly.

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thinking

thinking is a K2.x-only request parameter:
  • kimi-k2.6: supports {"type": "enabled"} (default), {"type": "disabled"}, and {"type": "enabled", "keep": "all"}.
  • kimi-k2.7-code: thinking is on by default and only {"type": "enabled", "keep": "all"} is accepted; any other configuration returns an error. When switching from kimi-k2.6, you must pass back the historical reasoning_content in messages as required by Preserved Thinking.
See Thinking Mode.

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reasoning_effort

K3 always reasons with Preserved Thinking enabled. Configure its reasoning effort with the top-level reasoning_effort request field, which supports "low", "high", and "max" (default "max"). See Reasoning Effort.
Switching levels invalidates prefix-cache hits. Decide on the effort level before the conversation starts and avoid switching it mid-session.

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tool_choice

kimi-k3 supports auto / none / required. kimi-k2.6 and kimi-k2.7-code do not support required and return an error if it is passed. See Tool Choice.

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temperature

  • kimi-k2.6: fixed at 1.0 in thinking mode and 0.6 in non-thinking mode; other values return an error.
  • kimi-k2.7-code: fixed at 1.0; other values return an error.
  • kimi-k3: fixed at 1.0; other values return an error.
Do not pass temperature explicitly when calling these models. kimi-k2.7-code-highspeed is the same model as kimi-k2.7-code with identical parameter constraints; only the output speed differs.

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FAQ

Switching from kimi-k2.6 to kimi-k3 — do I need to change my code? Replace model with kimi-k3 and remove the K2.x thinking configuration. To set the reasoning effort explicitly, use top-level reasoning_effort. In multi-turn conversations and tool calls, pass the complete assistant message returned by the API back to messages as-is, including any reasoning_content. Switching from kimi-k2.7-code to kimi-k3 — do I need to change my code? Replace model and continue passing complete assistant messages back as-is. To set the reasoning effort explicitly, use top-level reasoning_effort. My code uses OpenAI’s reasoning_effort — do I need to change it for kimi-k3? No. K3 supports top-level reasoning_effort with "low", "high", and "max" as accepted values and "max" as the default. Can I use tool_choice: "required" on kimi-k2.6 or kimi-k2.7-code? No. These models do not support required and return an error if it is passed; only kimi-k3 supports it.

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Kimi K2.7 Code series — thinking Parameter

The kimi-k2.7-code series includes kimi-k2.7-code and its high-speed variant kimi-k2.7-code-highspeed; the two are the same model with identical parameter constraints (including the table above and the thinking behavior) and differ only in output speed (referred to collectively as kimi-k2.7-code below). kimi-k2.7-code is code-focused, and all parameter constraints except thinking are identical to kimi-k2.6. Unlike kimi-k2.6, its thinking is always on and cannot be disabled (passing {"type": "disabled"} errors), and Preserved Thinking is always on (thinking.keep is treated as "all" whether omitted or set to "all"; any other invalid value errors). So you do not need to pass the thinking parameter — just switch the model, and the model always emits reasoning_content. For details, see Using Thinking Mode.

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Kimi K2.6 — thinking Parameter

Kimi K2.6 supports the thinking parameter to control whether deep thinking is enabled. Accepts {"type": "enabled"} or {"type": "disabled"}. Since the OpenAI SDK doesn’t have a native thinking parameter, use extra_body: