Kimi K2.5 Thinking/Instant and Vision Tool Configuration

One-sentence takeaway

K2.5's Thinking and Instant modes are not changed by editing a text prompt alone: the official guidance gives the two modes different temperatures and requires explicitly switching between them through the API/extra_body. Third-party deployments also need to verify the chat template, reasoning_content, and provider parser.

Use cases

• Suitable tasks: text/image/video conversations, visual analysis, coding agents, search, and multi-step tool calls.

• Unsuitable tasks: mixing Thinking and Instant parameters, or deploying tool writes directly without verifying the provider.

• Applicable model versions: Kimi K2.5; official API and vLLM/SGLang/KTransformers versions may use different fields.

• Applicable clients, agents, or APIs: Kimi API, OpenAI/Anthropic-compatible APIs, Kimi Code, vLLM/SGLang.

• Recommended reasoning levels and parameters: Thinking temperature=1.0, top_p=0.95; Instant temperature=0.6, top_p=0.95; context length 256k; set max_tokens according to the task.

Ready-to-use content

import openai

client = openai.OpenAI(api_key="<KIMI_API_KEY>", base_url="<KIMI_BASE_URL>")
messages = [
    {"role": "system", "content": "You are Kimi, an AI assistant created by Moonshot AI."},
    {"role": "user", "content": "Analyze this image and list verifiable facts, anomalies, and next checks."}
]

# Thinking mode: official API
thinking = client.chat.completions.create(
    model="kimi-k2.5",
    messages=messages,
    temperature=1.0,
    top_p=0.95,
    max_tokens=8192,
    extra_body={"thinking": {"type": "enabled"}},
)

# Instant mode: official API
instant = client.chat.completions.create(
    model="kimi-k2.5",
    messages=messages,
    temperature=0.6,
    top_p=0.95,
    max_tokens=4096,
    extra_body={"thinking": {"type": "disabled"}},
)

For vLLM/SGLang, the official repository says Instant can use extra_body={"chat_template_kwargs":{"thinking":False}}; run the Kimi Vendor Verifier before deployment.

Test/workflow steps

1. Use the same input to test Thinking and Instant separately for latency, tokens, correctness, and tool calls.

2. For visual tasks, record the original images/videos, media encoding, dimensions, request fields, and output; do not record only the text.

3. Run the Vendor Verifier/KVV and multi-turn tool calls for third-party providers, and compare the output with the official API.

4. Check that multi-turn assistant messages correctly preserve reasoning_content; remove explicit null values to avoid contaminating the chat template.

5. During load testing, verify 200/429 responses, empty responses, timeouts, early EOS termination, and idempotent retries.

Original evidence and data

• The official repository gives temperature=1.0 for Thinking, temperature=0.6 for Instant, and top_p=0.95, and distinguishes the switching fields for the official API and vLLM/SGLang.

• Official model summary: MoE, 1T total parameters, 32B active parameters, 256K context, and native vision.

• Officially recommended deployment engines include vLLM, SGLang, and KTransformers; the minimum Transformers version is 4.57.1.

• Official requirements/examples show reasoning_content and remind users to handle intermediate state correctly in multi-turn thinking/tool calls.

Boundaries of use

• These parameters are official starting recommendations and are not guaranteed to be optimal for a specific provider, task, or quantized model.

• The extra_body fields may differ across APIs/engines; do not send official API fields directly to every OpenAI-compatible service.

• reasoning_content is not a user-visible explanation and should be stored/filtered according to the provider's documentation; an explicit null may be rendered as text by the template.

• Tool calls, EOS/grammar, and rate limiting are end-to-end system issues that model benchmarks cannot cover.

Source excerpt or observation (compliance-short quote only)

The official repository recommends using the Kimi Vendor Verifier to validate deployments; this is an important practice given differences among open-model providers.