Kimi K3.1 Pro Review: Pricing, Benchmarks, Open Weights and Licence

Last updated: 9 November 2026

Kimi K3.1 Pro at a glance

If you are new to Kimi, see how to use Kimi.

What Moonshot claims about performance

Moonshot published a long comparison table against leading models. One analysis counted 37 rows with scores for both Kimi K3.1 Pro and Claude Opus 5.5, with Kimi ahead on 16. Highlights from Moonshot’s figures:

Important caveats: Kimi was run at maximum reasoning effort, many comparison scores came from other labs’ reports or leaderboards, and no independent results on the released weights had been published at the time of writing. Outside testers were not yet convinced it matches Opus in everyday use. Treat these as promising claims, not settled facts.

Pricing compared

OpenRouter listed Kimi K3.1 Pro at about half the price of Claude Opus 5.5 when checked on 4 November 2026. NVIDIA’s API catalogue also lists a free prototype endpoint with rate limits of 40 requests per minute and 10,000 per day, useful for testing. See Mistral Large 4, GPT-6.1 Terra and LLM API pricing comparison.

The licence

Kimi K3.1 Pro uses its own licence, not a standard open-source one. It grants broad rights to use, copy, modify, distribute and sell, with conditions:

• Large model-as-a-service hosts with more than $20 million in revenue (including affiliates) in any 12 months must sign a separate agreement with Moonshot before commercial use.
• Very large products, with more than 100 million monthly active users or more than $20 million monthly revenue, must display the Kimi name prominently in the interface.
• Internal use is exempt from both conditions.

For almost all businesses, these conditions will not apply. Large platforms should read the licence carefully.

Running it yourself

The weights are about 1.56 TB across 96 files. Moonshot recommends vLLM, SGLang and TokenSpeed for inference but does not state hardware requirements. One third-party estimate suggested eight NVIDIA B300 GPUs, a setup costing hundreds of thousands of dollars. For most teams, the API, OpenRouter or a cloud host is far more practical. See how to run an LLM locally for models that fit smaller hardware.

Safety and data considerations

• Cyber data removed. Moonshot says it removed cyber-related data from both training stages for the open-weight release while keeping general coding data.
• Data location. Using Moonshot’s API sends data to Moonshot’s servers. Self-hosting or a third-party host in your region gives more control.
• Company policy. Some organisations restrict AI services based in certain countries. Check your policy and clients’ requirements. See AI data privacy for business.

For a related discussion, see is DeepSeek safe.

Best uses for Kimi K3.1 Pro

• Coding agents through Kimi Code or other agent tools that accept OpenAI-compatible APIs.
• Long document analysis using the 1M token context.
• Financial and business research, one of Moonshot’s stated focus areas.
• 3D and design-adjacent workflows, another stated focus.
• Cost-sensitive high-volume tasks where a frontier-class model is needed but closed-model prices are too high.

How to test it fairly

1. Pick 20 to 50 real tasks from your work, not benchmark questions.
2. Run them on Kimi K3.1 Pro and your current model with the same prompts.
3. Score results blind if possible.
4. Track cost per task, including reasoning tokens.
5. Check latency at your expected volume.
6. Decide based on quality per dollar, not leaderboard rank.

How to call Kimi K3.1 Pro from your code

Because Moonshot offers an OpenAI-compatible endpoint, many existing tools and scripts can switch to Kimi by changing the base URL, API key and model name. The exact model identifier and base URL are listed in Moonshot’s and OpenRouter’s documentation; check them before you deploy. A typical pattern in Python looks like this:

from openai import OpenAI

client = OpenAI(
    base_url="YOUR_PROVIDER_BASE_URL",   # Moonshot or OpenRouter endpoint
    api_key="YOUR_API_KEY",
)

response = client.chat.completions.create(
    model="KIMI_K3_1_PRO_MODEL_ID",       # copy from the provider's model list
    messages=[
        {"role": "system", "content": "You are a careful financial research assistant."},
        {"role": "user", "content": "Summarise the key risks in the attached annual report."},
    ],
)
print(response.choices[0].message.content)

Keep your API key out of source code, set a spending limit with your provider, and log token usage per request so you can compare costs with your current model.

Where to access it: options compared

Working with a 1M token context window

A one million token window can hold several long reports or a large codebase in a single request. That is powerful, but it is easy to waste money. Practical tips:

• Only send what the task needs. Long inputs cost more on every request.
• Put instructions at the start and repeat the key question at the end of very long prompts, so the model does not lose the thread.
• Ask for structured output, such as a table or JSON, when you will process the answer in code.
• Use retrieval for very large collections. For thousands of documents, search first and send only the relevant ones. See what is RAG.
• Check for missed details in long-document answers by asking the model to quote the passages it relied on.

See context window explained.

Open-weight model checklist for businesses

Before adopting Kimi K3.1 Pro or any open-weight model in a business process, work through these questions:

1. Quality: does it match or beat your current model on your own test set?
2. Cost: what is the cost per completed task, including reasoning tokens and retries?
3. Latency: is it fast enough at your expected volume?
4. Data: where will prompts and outputs be processed and stored?
5. Licence: do any conditions apply to your use?
6. Support: who will you call when something breaks?
7. Exit plan: can you switch models easily if needed?

Kimi K3.1 Pro vs closed frontier models: when to choose which

Many teams end up using several models: an open model for volume work and a closed model for the hardest steps. See model routing explained.

Frequently asked questions