Kimi K3.1 Pro vs GPT-6 Astra: Long-Context Costs

In Moonshot’s published comparison, GPT has the higher Long-horizon coding and BenchCAD with-tools results; Kimi has the higher Terminal-Bench 4.0 and OfficeQA Pro v2 results. A larger price gap does not mean the models return interchangeable answers.

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

Additional task evidence reported by Moonshot

GPT’s reported advantage concerns long coding tasks and writing CAD code from images. Kimi’s higher OfficeQA Pro v2 result concerns answering questions from financial documents. Evaluation tools and settings differ, so these task results do not establish a universal or matched-endpoint winner.³

Later requests may resend earlier messages, files and tool results. Add the cost of each request to estimate the total conversation spend.

The uncached example with 400,000 input and 20,000 output tokens costs $1 for Kimi and $9.50 for GPT. Shortening carried-forward material may reduce input charges, but the extra summarizing or retrieval also has a cost.

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Prices compare Kimi on Novita with the OpenAI API. The code example uses the Novita Kimi endpoint.⁷²

The GPT-6 Astra rates in the first table apply to prompts with at most 272,000 input tokens. Above that threshold, the whole request uses the higher input, cache and output rates explained below. The Kimi column uses Novita’s published synchronous token rates.

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The new prefix is charged at the cache-write rate instead of the uncached-input rate and must be cache-eligible. This row uses Novita’s 5-minute write price for Kimi and OpenAI’s default write price with a minimum 30-minute cache lifetime for GPT; the retention periods differ. The warm-cache row is a later request with a successful cache read.

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.

GPT-6 Astra: the 400,000-input example includes the whole-request long-context surcharge: $20 / 1M input and $75 / 1M output tokens, giving $8 + $1.50 = $9.50.

GPT figures use OpenAI’s standard synchronous API prices. Newly cached prefix tokens use the cache-write rate instead of the uncached-input rate; later cache hits use the cached-input rate. Subscription, Batch, Flex and Fast pricing are excluded.

Kimi on Novita lists $2 / 1M tokens for a 5-minute cache write and $4 for a 1-hour write. GPT-6 Astra’s $12.5 / 1M cache-write rate corresponds to OpenAI’s default 30-minute minimum cache lifetime at the short-context rate. The cold-cache example uses Kimi’s 5-minute option and GPT’s default 30-minute option; it compares their write costs at those different durations. OpenAI’s optional 12-hour setting is outside this example.

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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.¹⁷

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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Estimate the input sent at every step, including tool results, then compare the full conversation cost. Start with a short request and a long one; a result from only the short example misses GPT’s pricing threshold.

If the assistant needs to run code, a Novita Agent Sandbox can provide the execution environment; your application controls the commands. Tool results sent back to either model become part of later input, so execution can grow a GPT conversation toward its pricing threshold. Sandbox runtime is billed separately.

Explore Novita Agent Sandbox · Build a repeatable execution environment

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The sample calls Kimi through Novita and prints its reply and usage. A GPT comparison needs a separate request to the OpenAI API.

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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