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moonshotai/kimi-k2-instruct

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Specifications

Context window131K tokens
Max output—
API typechat
AddedJul 11, 2025
Model IDnovita/moonshotai/kimi-k2-instruct
Data retentionYes
Used for trainingUnknown
Provider location🇺🇸 US
Privacy policyNovita AI Privacy Policy →

Benchmarks

Released 2025-07
SWE-Bench Verifiedcoding
65.8%

Resolving real GitHub issues from 12 popular Python repositories.

GPQA Diamondreasoning
70.0%

Graduate-level physics, chemistry & biology questions designed to resist Googling.

MMLU Proknowledge
82.3%

Massive Multitask Language Understanding across 57 academic subjects.

Scores are sourced from official model cards, Artificial Analysis, and public leaderboards. Benchmarks measure specific skills and do not capture every aspect of model quality — always test on your own workload.

Pricing

Input / 1M
$0.57
Output / 1M
$2.30
Cache write
—
Cache read
—
Estimated cost
100K input + 10K output$0.0800
1M input + 100K output$0.80
10M input + 1M output$8.00

Requesty charges exactly what the upstream provider charges — no markup, no per-request fees. Prompt caching and smart routing can reduce effective cost by 30-80%.

Quickstart

Drop-in compatible with the OpenAI SDK. Change the base URL, swap in your Requesty API key, and set the model to novita/moonshotai/kimi-k2-instruct.

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from openai import OpenAI

client = OpenAI(
    api_key="YOUR_REQUESTY_API_KEY",
    base_url="https://router.requesty.ai/v1",
)

response = client.chat.completions.create(
    model="novita/moonshotai/kimi-k2-instruct",
    messages=[
        {"role": "user", "content": "Explain quantum computing in one paragraph."},
    ],
)

print(response.choices[0].message.content)

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Frequently asked questions

How much does moonshotai/kimi-k2-instruct cost?
moonshotai/kimi-k2-instruct is priced at $0.57 per million input tokens and $2.30 per million output tokens when accessed via Requesty. Requesty charges exactly what the upstream provider charges — we don't add markup.
What is the context window of moonshotai/kimi-k2-instruct?
moonshotai/kimi-k2-instruct has a context window of 131K tokens. That's roughly 175 words of input you can fit in a single prompt.
How does moonshotai/kimi-k2-instruct perform on benchmarks?
moonshotai/kimi-k2-instruct scores 89.9% on HumanEval, 89.2% on MATH, 82.3% on MMLU Pro. See the full benchmark chart above for results across MMLU Pro, GPQA Diamond, SWE-Bench Verified, HumanEval, MATH, AIME, MMMU, and LiveBench.
What can moonshotai/kimi-k2-instruct do?
moonshotai/kimi-k2-instruct supports tool calling. You can call it through any OpenAI-compatible client by pointing base_url to Requesty.
How do I use moonshotai/kimi-k2-instruct with the OpenAI SDK?
Install the OpenAI SDK, set base_url to "https://router.requesty.ai/v1", set your API key to your Requesty key, and set the model to "novita/moonshotai/kimi-k2-instruct". The Quickstart above shows Python, JavaScript and cURL snippets.

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