Kimi K3.1 Pro vs Claude Opus 5.5: Coding and Cost

The coding results do not point to one model for every job. In Moonshot’s Terminal-Bench 4.0 table, Kimi scores 65.7% and Claude 66.4%; the CAD and financial-document tests below favor different models.

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Vendor-reported results. Evaluation settings and sources are described in the methodology.

Additional task evidence reported by Moonshot

BenchCAD tests writing CAD code from images: Claude scores 96.2% versus Kimi’s 92.7%. OfficeQA Pro v2 tests questions about financial documents: Kimi scores 64.4 versus Claude’s 62.2. Evaluation settings differ between the published results, so these are reported task results rather than a matched endpoint test.³

An application built around Claude may need changes to how it describes tools and reads model replies. That work can outweigh small token savings in a low-volume application.

For a coding assistant, compare time to a usable patch, including tests, rather than just the first streamed message. No matched endpoint timing measurement is quoted here.

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Prices compare Kimi on Novita with the Anthropic API. The code example uses the Novita Kimi endpoint.⁸²

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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.

Claude figures use Anthropic’s standard global API prices, not Fast mode, US-only inference or a Novita resale quote.

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Novita’s model pages abbreviate the Kimi limits as 1M. Its public model catalog specifies 1,048,576 tokens for both context and maximum output; those exact values are used in this table. A shared 1M label across providers does not by itself establish identical token limits.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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A practical split is to trial Kimi on one type of job before changing the whole application. Move more work only if it meets the same output requirements at a lower total cost.

For a coding comparison, an optional Novita Agent Sandbox provides a separate place to run generated code and tests. Your application controls allowed commands, and runtime charges are separate from model charges. Pure document Q&A needs no Sandbox unless the task also runs code.

Explore Novita Agent Sandbox · Build a repeatable execution environment

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This example asks Kimi on Novita for a short implementation plan. It does not call Claude or execute the plan.

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"]:
    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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