Kimi K3 on SiliconFlow: Features, Pricing, and API Access

Kimi K3 is Moonshot AI’s 2.8-trillion-parameter flagship model for long-horizon coding, knowledge work, visual analysis, and complex reasoning. It combines a sparse Mixture-of-Experts architecture with native visual understanding and an approximately 1-million-token context window.

Kimi K3 is available through SiliconFlow’s Serverless API under the model ID moonshotai/Kimi-K3. Developers can use the model through an OpenAI-compatible interface without provisioning or maintaining the large computing infrastructure required for self-hosted inference. Current support includes image input, tool calling, JSON Mode, streaming, and reasoning output.

What Is Kimi K3?

Kimi K3 is designed for tasks that extend across multiple steps, files, tools, or information sources. Its main target areas include software engineering, technical research, document analysis, visual reasoning, and automated agent workflows.

Although users may search for a “Kimi K3 coding model,” K3 is not limited to programming. It is a general flagship model that combines coding with reasoning, visual input, and knowledge work. Moonshot AI maintains a separate coding-focused model line for developers who need a more specialized coding model.

Kimi K3 Model Architecture

Kimi K3 uses a sparse Mixture-of-Experts architecture with 2.8 trillion total parameters. During computation, it effectively activates 16 of 896 experts.

Its architecture includes:

• Kimi Delta Attention: A hybrid linear attention mechanism designed to improve efficiency across long sequences.

• Attention Residuals: A method that selectively retrieves representations across model depth rather than accumulating them uniformly.

• Stable LatentMoE: A framework designed to support stable routing at a high level of expert sparsity.

Together, these components are intended to improve information flow across both sequence length and model depth. Moonshot AI reports that the architecture delivers an approximate 2.5× improvement in overall scaling efficiency compared with Kimi K2.

Self-hosting a model of this scale requires substantial infrastructure. Moonshot AI recommends supernode configurations with at least 64 accelerators for deployment. SiliconFlow removes that setup requirement by providing Kimi K3 through an immediately available Serverless endpoint.

Kimi K3 1M Context Window

Kimi K3 supports an approximately 1-million-token context window. On SiliconFlow, the context length is specified as 1049K tokens.

This context capacity is useful when an application needs to work with:

• Large software repositories

• Technical specifications and source files

• Multiple reports or research papers

• Long conversation and tool histories

• Extensive logs and operational records

• Cross-document comparison tasks

The context window is a capacity limit, not a recommendation to include every available file. Duplicate, outdated, or irrelevant content can increase cost and make important instructions harder to prioritize. Production applications should continue to use retrieval, file selection, context compression, and clear source organization.

SiliconFlow’s model specification currently displays 1049K for both context length and maximum tokens. However, request-level output limits may also depend on the current API endpoint, parameter rules, and account configuration. Developers should follow the latest API reference instead of assuming that the full displayed model maximum can be generated in one response.

Kimi K3 Multimodal Capabilities

Kimi K3 has native visual capabilities, and its SiliconFlow endpoint supports image input.

This allows developers to combine images and text for tasks such as:

• Reading charts and technical diagrams

• Reviewing interface screenshots

• Comparing a design reference with an implementation

• Analyzing figures from research papers

• Inspecting dashboards and visual reports

• Using screenshots as feedback during frontend development

Moonshot AI describes Kimi K3 as a native multimodal model that can understand text, images, and video. The current SiliconFlow model specification explicitly confirms image input. Applications should not assume that every input format available through another Kimi product is supported through the SiliconFlow API.

What Are the Main Kimi K3 Features?

Kimi K3 is built for tasks that require sustained execution rather than a single isolated answer. Its core strengths are long-horizon coding, tool-based agent work, knowledge-intensive analysis, and visual reasoning.

Long-Horizon Coding

Kimi K3 can continue working through software engineering tasks that involve investigation, implementation, testing, correction, and review.

Relevant workloads include:

• Navigating large repositories

• Tracing dependencies across files

• Planning and applying refactors

• Debugging multi-component systems

• Running and interpreting tests

• Optimizing kernels or infrastructure code

• Revising frontend code from screenshots

• Converting technical research into executable code

Moonshot AI reports that Kimi K3 can sustain extended engineering sessions, navigate large repositories, and coordinate terminal tools with limited human oversight. Its published demonstrations include GPU kernel optimization, compiler development, chip design, visual game development, and scientific computing.

These examples describe model capability, not automatic access to a development environment. Applications must still provide files, repository tools, terminal functions, sandboxes, and approval controls.

Complex Agent Tasks

Kimi K3 can act as the reasoning layer in an agent that performs several connected actions.

For example, an application may give the model access to functions for:

• Searching a knowledge base

• Querying a database

• Reading approved files

• Running calculations

• Calling internal services

• Executing code in a sandbox

The model can determine when a tool is relevant and generate the arguments for a function call. The application remains responsible for validating those arguments, running the function, returning the result, handling errors, and enforcing access controls.

SiliconFlow supports function calling through an OpenAI-compatible tools format. A model can request a function call, but it cannot independently access a browser, terminal, database, or local file system unless the application provides that capability.

End-to-End Knowledge Work

Kimi K3 is also designed for workflows that combine research, analysis, data handling, writing, and visual communication.

Possible tasks include:

• Comparing policies, contracts, or technical standards

• Reviewing scientific literature

• Extracting evidence from long reports

• Producing research summaries

• Analyzing financial or operational documents

• Building recommendations from multiple sources

• Converting research into reports and dashboards

Moonshot AI has demonstrated Kimi K3 on extended research tasks involving thousands of searches, large document collections, terminal-based data processing, concurrent subagents, and interactive visualization. These cases show the model’s ability to coordinate complex work, but important conclusions should still be checked against primary sources.

Tool Calling and JSON Mode

Kimi K3 supports both tool calling and JSON Mode on SiliconFlow.

JSON Mode can support:

• Information extraction

• Content classification

• Request routing

• Form processing

• API payload generation

• Structured report components

A request can enable JSON Mode with:

response_format={"type": "json_object"}

JSON Mode is designed to return valid JSON when configured correctly, but it does not enforce a specific JSON Schema. Structured Outputs are not currently supported for Kimi K3 on SiliconFlow.

Applications should validate required fields, data types, allowed values, and business rules before using model-generated JSON. They should also handle incomplete output when a response is truncated.

How Does Kimi K3 Perform?

Moonshot AI reports frontier-level results across coding, agent, knowledge-work, and multimodal evaluations. The company also acknowledges that the overall user experience still trails the strongest proprietary systems in some areas.

Kimi K3 Coding Performance

Kimi K3 has been evaluated on coding benchmarks such as:

• DeepSWE v1.1

• Terminal-Bench 2.1

• Program Bench

• SWE Marathon

• FrontierSWE

• PostTrain Bench

• MLS Bench Lite

• Kimi Code Bench 2.0

These evaluations cover repository modification, terminal operation, program synthesis, long-running engineering, model post-training, and machine learning systems work.

The results should be interpreted carefully. Depending on the benchmark, models were evaluated through different agent harnesses, including KimiCode, Claude Code, Codex, and Terminus. Some results also came from external leaderboards, while others were produced through Moonshot AI’s own evaluation setup.

For production decisions, benchmark results should be treated as an initial capability signal. Teams should also test Kimi K3 on their own repositories, languages, tool definitions, testing requirements, and completion criteria.

Kimi K3 Agent Performance

Agent evaluations cover tasks involving web research, automation, spreadsheets, office documents, and external tools. Moonshot AI’s published evaluation set includes BrowseComp, AutomationBench, SpreadsheetBench 2, OfficeQA Pro, MCP Atlas, GDPval-AA, and AA-Briefcase.

These tasks measure more than direct question answering. A model may need to gather evidence, operate tools, maintain task state, update files, or coordinate several actions.

Real-world agent performance also depends on the surrounding system. Clear tool descriptions, reliable APIs, permission limits, retry policies, and intermediate-result checks can be as important as the underlying model.

Kimi K3 Multimodal Performance

Kimi K3 has also been evaluated on visual perception, multimodal reasoning, chart interpretation, and tool-assisted visual tasks. Moonshot AI’s evaluation materials include MMMU-Pro, PerceptionBench, CharXiv, and ZeroBench.

Its visual capabilities are particularly relevant when images are part of a broader workflow. An agent may inspect a screenshot, update code, run the application, and review the next visual result.

Visual output still requires verification when a task depends on small labels, exact measurements, fine visual distinctions, or safety-critical decisions.

Kimi K3 Pricing and Availability on SiliconFlow

Kimi K3 is available through SiliconFlow’s Serverless API. Developers can test it without setting up a dedicated model deployment and pay according to token usage.

Kimi K3 API Pricing

Current Kimi K3 pricing is:

For example, a request using 100,000 standard input tokens and generating 20,000 output tokens would have the following estimated cost:

Input:

100,000 ÷ 1,000,000 × $3.00 = $0.30

Output:

20,000 ÷ 1,000,000 × $15.00 = $0.30

Estimated total:

$0.60

This example only illustrates the pricing formula. Actual cost depends on cache eligibility, input length, image inputs, reasoning output, completion length, and the number of calls required to finish the workflow.

An agent may make several model and tool calls for one user request. Cost monitoring should therefore operate at the task or session level rather than only at the individual API-request level.

Kimi K3 Context and Output Limits

The available context can include:

• System instructions

• User and assistant messages

• Source documents

• Tool requests

• Tool results

• Image representations

• Generated output

Applications should set max_tokens according to the expected answer rather than the model’s full theoretical capacity. Short code reviews may need only a few thousand tokens, while long reports may need more.

For large repositories or document collections, retrieving relevant sections is often more efficient than repeatedly sending the full corpus.

Kimi K3 Supported API Features

These limits affect application design. For example, developers should combine JSON Mode with application-side validation rather than depending on schema enforcement.

How to Use the Kimi K3 API on SiliconFlow

SiliconFlow provides an OpenAI-compatible API. Applications already using the OpenAI Python SDK can connect by updating the API key, base URL, and model ID.

Create a SiliconFlow API Key

Create or sign in to a SiliconFlow account, open the API Keys page, and generate a new key.

Store it in an environment variable rather than placing it directly in source code:

export SILICONFLOW_API_KEY="your_api_key"

Do not commit API keys to public repositories or expose them in frontend applications.

Call Kimi K3 With the OpenAI SDK

Install or update the OpenAI Python package:

pip install --upgrade openai

Then configure the client with the SiliconFlow base URL:

The required configuration values are:

Base URL: https://api.siliconflow.com/v1

Model ID: moonshotai/Kimi-K3

SiliconFlow provides unified access to more than 200 language and multimodal models. Teams can evaluate different models without building a separate provider integration for each one, although model-specific parameters and history requirements still need to be handled individually.

Configure Streaming and Reasoning

Streaming allows an application to display content as it is generated:

SiliconFlow reasoning-model responses may contain reasoning_content alongside the final content. Because Kimi K3 is sensitive to reasoning-history handling, long-running agents should use a framework with verified Kimi K3 compatibility rather than assuming that the conversation state of another reasoning model can be reused unchanged.

At launch, Kimi K3 uses maximum thinking effort by default. Moonshot AI has announced lower reasoning-effort options for later updates, but developers should not assume that parameters available through another Kimi endpoint are already supported through SiliconFlow.

Best Use Cases for Kimi K3

Kimi K3 is most suitable when a task genuinely requires extended execution, large amounts of context, visual reasoning, or complex tool coordination.

Large Codebase Analysis

Use Kimi K3 when a task requires reasoning across many connected files or maintaining progress through an extended engineering workflow.

Provide:

• A repository map

• Relevant directories

• Available test commands

• Clear acceptance criteria

• Tool permissions

• Rules for human approval

A smaller model may be more efficient for isolated syntax changes, short code explanations, or simple formatting tasks.

Long-Context Document Processing

Kimi K3 can support the comparison of large reports, contracts, technical standards, policies, or research collections.

For more reliable results, instruct the model to:

• Identify the source for major findings

• Separate evidence from inference

• Flag contradictory information

• Note missing evidence

• Preserve important terminology

Retrieval remains useful when only a small portion of a large collection is relevant to each question.

AI Agents and Automated Workflows

Kimi K3 can support coding agents, research assistants, document-review systems, data-analysis workflows, and internal automation.

Production agents should define:

• Allowed tools

• Maximum execution steps

• Token and cost budgets

• Data-access boundaries

• Timeouts and retry limits

• Actions requiring human approval

• Conditions that require the agent to stop

The model can reason about actions and request tools, while the surrounding application remains responsible for execution, permissions, and business rules.

Multimodal Research and Analysis

Image input makes Kimi K3 useful for workflows involving charts, screenshots, dashboards, diagrams, and visually rendered documents.

A workflow may ask the model to inspect a figure, retrieve supporting text, call a calculation tool, and return a structured explanation. Important findings should remain traceable to the original image and source material.

Kimi K3 Limitations and Development Considerations

Reasoning History Management

Kimi K3 was trained with preserved thinking history. Moonshot AI warns that generation quality may become unstable when an agent framework fails to return the required historical thinking content.

For long sessions:

• Use a framework tested with Kimi K3.

• Preserve model state according to current API guidance.

• Avoid reconstructing the session from displayed answers alone.

• Monitor context growth.

• Restart the session deliberately when history becomes unreliable.

Do not copy a reasoning-history implementation from another provider without confirming that it is compatible with the SiliconFlow endpoint.

Model Switching and Session Compatibility

Avoid switching an active session from another reasoning model to Kimi K3 while retaining model-specific reasoning traces.

A safer process is to:

• Extract verified facts and completed actions.

• Remove internal reasoning history from the previous model.

• Open a new Kimi K3 session.

• Provide a concise summary of the current task state.

• Attach the source material needed to continue.

SiliconFlow’s unified API reduces provider-level integration work, but the internal states of different models are not automatically interchangeable.

Controlling Excessive Proactiveness

Moonshot AI notes that Kimi K3 may make unexpected decisions when it encounters minor obstacles or ambiguous instructions. This behavior is related to its training focus on long and difficult tasks. Applications with strict operational boundaries should therefore define permitted actions, approval requirements, stopping conditions, and rules for handling missing information. They should also state whether the model may modify files, call external systems, or make assumptions beyond the supplied evidence. These limits can be included in the system prompt, agent policy, or project-level instructions such as AGENTS.md.

Cost and Latency Considerations

Long prompts, reasoning output, image input, and repeated tool calls can increase both response time and total cost. Production workflows should control context size, output length, tool-call frequency, caching, and execution budgets based on the complexity of each task. A multi-model architecture can route repository-wide analysis, extended research, and complex agent work to Kimi K3 while assigning classification, extraction, and routine formatting to smaller models. This approach helps balance capability, latency, and cost across the application.

Start Using Kimi K3 on SiliconFlow

Kimi K3 is available on SiliconFlow under the model ID:

moonshotai/Kimi-K3

Create a SiliconFlow API key, test the model with your own code, documents, images, and tools, and measure its quality, latency, and cost under realistic workload conditions before moving the application into production.

Common Questions About Kimi K3 on SiliconFlow

Q1. Does Kimi K3 Have the Same Rate Limits for Every Account?

No. Kimi K3 usage limits may vary by account, model, and current service configuration. Check the model details or dashboard before setting concurrency targets, and add request queuing, exponential backoff, and retry limits to handle temporary throttling.

Q2. Can Kimi K3 Work With Third-Party Coding Agents?

Yes. Coding tools that accept an OpenAI-compatible provider can connect using SiliconFlow’s base URL, API key, and model ID. Confirm that the selected tool supports the context, image, and output settings required by your Kimi K3 workflow.

Q3. Does Cached Input Pricing Apply to Every Repeated Prompt?

No. Repeating similar text does not mean every input token will receive the cached rate. Cache billing applies only when the request qualifies for caching, so review actual billing records instead of estimating savings from prompt similarity alone.

Q4. Should Production Applications Prepare for Kimi K3 Service Changes?

Yes. SiliconFlow may adjust model availability and service capabilities over time. Production systems should monitor announcements, test important workflows after updates, handle model availability errors, and maintain a fallback path instead of assuming the current endpoint will remain unchanged indefinitely.