Kimi K3: Complete Specifications, Pricing, API Access & Use Cases (2026)

Kimi K3 is Moonshot AI’s multimodal reasoning model for long-horizon coding, agent workflows and complex knowledge work. It supports a one-million-token context window, visual inputs and tool calling. As per the Gate.AI model card, Kimi K3 costs $3 per million input tokens and $15 per million output tokens.

What Is Kimi K3?

Kimi K3 is a multimodal large language model developed by Moonshot AI. The model is designed for tasks that require sustained reasoning across large codebases, documents, images, videos and tool results rather than isolated question-and-answer interactions.

Moonshot AI identifies Kimi K3 as a 2.8-trillion-parameter model with a mixture-of-experts architecture. Its official API platform highlights a one-million-token context window and tool-calling support. These characteristics position Kimi K3 as a broad agent and knowledge-work model rather than a coding-only assistant.

Kimi K3 differs from Kimi K2.6 and Kimi K2.7 Code primarily in scope. Kimi K2.6 is a general multimodal model with a smaller context window, while Kimi K2.7 Code is more narrowly optimized for software-engineering workflows. Kimi K3 is intended to combine large-scale context, coding, vision and general reasoning within one model.

What Are Kimi K3’s Key Specifications and Pricing?

Moonshot AI’s official model documentation confirms the one-million-token context window and positions Kimi K3 for long-running coding and knowledge-work tasks.

The Gate.AI prices above are presented as per the Gate.AI listing and model card. Pricing should be rechecked on the live Gate.AI model marketplace before deployment because model rates and availability can change.

For example, a request containing 500,000 uncached input tokens and producing 50,000 output tokens would have the following estimated cost:

• Input: 0.5 × $3 = $1.50
• Output: 0.05 × $15 = $0.75
• Estimated total: $2.25

This calculation excludes account-specific discounts, routing adjustments and cache eligibility.

What Can Kimi K3 Do That Makes It Useful in Production?

Kimi K3 is most useful when a workflow requires the model to retain substantial evidence while repeatedly taking actions. A coding agent could inspect repository files, architecture documents, screenshots and test logs, propose changes, call development tools and interpret the resulting output.

The large context window can also support document-intensive research. Instead of splitting a substantial policy library or technical repository into many disconnected prompts, a team may be able to preserve more of the material within a shared working context. However, a larger context does not guarantee better answers; retrieval design, instruction quality and validation still matter.

Tool calling makes Kimi K3 relevant to agent systems. The model can select a defined tool, receive its result and continue reasoning from the updated state. This is useful for debugging, structured research, repository navigation and workflow automation.

A practical selection rule is to use Kimi K3 when the task combines long context, mixed media and multiple tool interactions. A smaller model may be more economical for short extraction, classification or routine chat.

What Are Kimi K3’s Supported Modalities?

Moonshot AI documents native visual understanding and identifies Kimi K3 as supporting image and video inputs. Its documented outputs remain text-oriented, including code, structured responses and tool calls. Kimi K3 should not be treated as an image- or video-generation model.

Where Does Kimi K3 Fall Short?

Kimi K3 may be unnecessarily expensive for short or predictable tasks. Based on the Gate.AI model-card price, output tokens cost five times more than uncached input tokens. Long reasoning responses or large code outputs can therefore become the main cost driver.

A one-million-token limit also creates operational challenges. Large prompts take longer to transfer and process, and irrelevant context may reduce answer quality. Teams should not load every available file merely because the model can accept it.

Kimi K3’s recent release is another limitation. Independent evidence about latency, reliability, language consistency and performance under sustained production traffic remains limited. Provider benchmark claims should not replace testing with representative internal workloads.

Finally, Gate.AI’s general OpenAI-compatible route is documented, but every provider-specific Kimi K3 parameter may not be exposed identically through the gateway. Advanced reasoning, visual-input and tool settings should be verified before production use.

What Is Kimi K3 Best Used For?

Kimi K3 is best suited to:

• large-repository analysis and multi-file code migration;
• debugging workflows involving code, screenshots and logs;
• technical research across extensive source collections;
• multimodal interface or design review;
• long-running agents that call tools and evaluate results.

Choose Kimi K3 when maintaining context across many interdependent inputs is more important than minimizing cost or latency.

Consider Kimi K2.7 Code when the workload is specifically centered on coding agents. Kimi K2.6 may be sufficient for general multimodal tasks that do not require Kimi K3’s one-million-token context.

How Does Kimi K3 Compare to Kimi K2.6 and Kimi K2.7 Code?

Kimi K3 is the strongest fit when context scale and cross-domain agent work are essential. Kimi K2.7 Code may offer a more focused development experience, while Kimi K2.6 may suit less complex multimodal applications.

Teams comparing models across providers can also review Claude Sonnet 4.6 and GPT-5.5, focusing on documented context, pricing, modalities, tool support and deployment requirements rather than seeking a universal winner.

How Do I Access Kimi K3 Through Gate.AI?

Gate.AI documents an OpenAI-compatible API at https://api.gate.ai/openai/v1, using bearer-token authentication and the /chat/completions endpoint. Model IDs use a provider/model format and should be copied from the live marketplace.

As per the Gate.AI model card, the Kimi K3 identifier is MoonshotAI/kimi-k3.

Python

import os
from openai import OpenAI
client = OpenAI(
    api_key=os.environ["GATEAI_API_KEY"],
    base_url="https://api.gate.ai/openai/v1",
)
response = client.chat.completions.create(
    model="MoonshotAI/kimi-k3",
    messages=[
        {
            "role": "user",
            "content": "Identify the main reliability risks in this workflow."
        }
    ],
)
print(response.choices[0].message.content)

curl

curl "https://api.gate.ai/openai/v1/chat/completions" \
  -H "Authorization: Bearer $GATEAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "MoonshotAI/kimi-k3",
    "messages": [
      {
        "role": "user",
        "content": "Explain when a one-million-token context is useful."
      }
    ]
  }'

The base URL and general request format are documented by Gate.AI. Confirm the model ID and any Kimi-specific multimodal or reasoning parameters in the live console before production deployment.

FAQs

Does Kimi K3 support one million tokens?

Yes. Moonshot AI documents a one-million-token context window for Kimi K3.

Is Kimi K3 multimodal?

Yes. It supports text, image and video inputs, but standalone image, audio or video generation is not documented.

How much does Kimi K3 cost on Gate.AI?

As per the Gate.AI model card, pricing is $3 per million input tokens, $15 per million output tokens and $0.30 per million cache-read tokens.

Is Kimi K3 designed for coding?

Yes, but it is broader than a coding-only model. Its target workloads include long-horizon coding, multimodal analysis, tool use and end-to-end knowledge work.

Can Kimi K3 call external tools?

Kimi K3 supports tool calling, but the application must define and execute the external tools before returning their results to the model.