Kimi K3.1 Pro vs Highspeed: Pricing and Use Cases

Both Novita endpoints list a 1M context window and tool calling. They accept text, image and video inputs and return text.

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With 100,000 input and 10,000 output tokens and no cache, standard costs $0.30 and Highspeed $0.60. The extra $0.30 pays for itself after 36 seconds of productive time saved if that time is valued at $30 per hour. This is a calculated break-even example, not a measured speed result.

Count the time a person actually waits for a usable answer, as well as tool and test time. For reasoning models, the first visible answer token and the first reasoning chunk are different events.

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The cold-cache example uses the published 5-minute write rates. The new prefix is charged once as a cache write and must be cache-eligible. The warm-cache example assumes a later request successfully reads an existing entry.

Each row fixes the input and output amounts to show token charges. Cache-read estimates require a successful hit. Tools, Sandbox runtime, storage, networking and taxes are separate; negotiated discounts and batch processing are excluded.

For both Kimi endpoints, Novita’s public catalog lists 1,048,576 tokens for context and maximum output; the model pages abbreviate these limits as 1M.¹⁸

Context and maximum-output values are the providers’ published limits, not a promise that both can be fully consumed in one request. Apply the endpoint’s total-context and generation rules.

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Use the same input, requested output length and pass/fail checks for both tiers. Paying more makes sense when shorter waits help the person or process finish sooner; waiting overlapped with other work is not automatically time saved.

When interactive debugging needs commands or tests, an optional Novita Agent Sandbox can run them for either tier. Keep that runtime the same so faster model responses are not confused with faster test execution. The application controls tool permissions; Sandbox runtime is billed separately from model tokens.

Explore Novita Agent Sandbox · Build a repeatable execution environment

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This example sends one planning prompt to each Novita model ID, then prints the stop reason and usage. It shows how to switch tiers; it does not run the generated plan or measure their speed.

Generation is capped at 1,024 tokens, including any reasoning tokens counted by the endpoint. For an empty answer or a completion stopped by the token limit, the code prints a notice and does not automatically request another completion. Review usage before raising the limit.

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["NOVITA_API_KEY"],
    base_url="https://api.novita.ai/openai/v1",
)

for model in ["moonshotai/kimi-k3.1-pro","moonshotai/kimi-k3.1-pro-highspeed"]:
    reply = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": "Review a repository bug fix: list the implementation steps and acceptance tests."}],
        max_tokens=1024,
    )
    choice = reply.choices[0]
    content = choice.message.content or ""
    print(model, "finish_reason:", choice.finish_reason)
    print("usage:", reply.usage)
    if choice.finish_reason == "length" or not content.strip():
        print("No complete answer. Inspect usage and adjust the generation budget before another request.")
    else:
        print(content)

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