What is Moonshot Kimi K3? Kimi K3 vs DeepSeek V4: Features, Pricing and API Guide
The open-source AI market is developing faster than ever. Only a few months after DeepSeek introduced DeepSeek V4, Moonshot AI released Kimi K3, a massive multimodal model designed for coding, reasoning and complex knowledge work.
Although some users refer to the model as “Moonshot Kimi 3,” its official name is Kimi K3. It represents the latest generation of Moonshot AI’s Kimi model family and introduces major improvements in model scale, visual understanding, long-context processing and autonomous agent workflows.
At first glance, Kimi K3 and DeepSeek V4 appear remarkably similar. Both offer a one-million-token context window, support AI agent development and target advanced coding and reasoning tasks. However, their architectures, pricing strategies and ideal applications are significantly different.
This Kimi K3 vs DeepSeek V4 comparison explains how the two models work, where each one performs best and how developers can access them through an AI API.
What Is Kimi K3?
Kimi K3 is Moonshot AI’s flagship open model for long-horizon coding, multimodal reasoning and end-to-end knowledge work.
Released in July 2026, the model contains approximately 2.8 trillion total parameters. Moonshot AI describes it as the first open model in the three-trillion-parameter class. Despite its enormous overall size, K3 uses a sparse Mixture-of-Experts architecture that activates only a selection of experts while processing each token.
This approach allows the model to benefit from a very large knowledge capacity without activating every parameter for every request.
Native Image and Video Understanding
One of the most important Kimi K3 features is its native visual understanding.
Instead of processing only written prompts, the Kimi K3 API can accept text, images and video inputs. The model can inspect screenshots, understand visual layouts and use visual feedback while completing a task.
For example, an AI coding agent powered by K3 could generate a website, examine a screenshot of the resulting page, identify layout problems and then revise the code. This combination of coding and visual reasoning makes the model particularly suitable for:
- Front-end website development
- User interface design
- Game development
- Computer-aided design workflows
- Presentation generation
- Image and video analysis
- Visual document processing
Rather than functioning only as a chatbot, K3 is intended to operate as part of a broader production workflow.
Long-Horizon Coding and Knowledge Work
Complex software projects often require an AI model to maintain context across hundreds of files, understand dependencies and continue working through multiple stages.
Kimi K3 addresses this challenge with a context window of up to one million tokens. It can work with large codebases, lengthy documents, research materials and extended tool-calling histories. Automatic context caching can also reduce the cost of repeatedly sending the same long prompt or reference material.
In addition, the model always operates with reasoning enabled. Developers can control the reasoning level through low, high or maximum reasoning-effort settings, depending on the complexity of the task.
What Is DeepSeek V4?
DeepSeek V4 is DeepSeek’s latest open model family for reasoning, coding, long-context processing and AI agent applications.
The official release is currently called DeepSeek V4 Preview. It became available in April 2026 through the DeepSeek website, API platform and open model weights.
Instead of offering only one model, the V4 family is divided into two versions.
DeepSeek V4 Pro
DeepSeek-V4-Pro is the more powerful option. It contains approximately 1.6 trillion total parameters and activates 49 billion parameters during inference.
Its primary strengths include difficult mathematical reasoning, software engineering, world knowledge and agentic coding. According to DeepSeek, V4 Pro is designed to compete with leading closed-source AI models while remaining available as an open model.
For developers building advanced coding agents, research systems or reasoning-intensive applications, the Pro version is the more appropriate choice.
DeepSeek V4 Flash
DeepSeek-V4-Flash focuses on speed and affordability.
It contains 284 billion total parameters and activates approximately 13 billion parameters. While smaller than V4 Pro, its reasoning performance is designed to remain competitive on routine tasks and relatively simple agent workflows.
Flash is therefore better suited to high-volume applications such as customer support, document classification, content generation, data extraction and SaaS features that must process large numbers of requests.
Kimi K3 vs DeepSeek V4: Key Specifications
| Feature | Kimi K3 | DeepSeek V4 Pro | DeepSeek V4 Flash |
|---|---|---|---|
| Developer | Moonshot AI | DeepSeek | DeepSeek |
| Total parameters | 2.8 trillion | 1.6 trillion | 284 billion |
| Active architecture | 16 of 896 experts | 49 billion active parameters | 13 billion active parameters |
| Context window | 1 million tokens | 1 million tokens | 1 million tokens |
| Maximum output | Up to 1 million tokens through configuration | Up to 384,000 tokens | Up to 384,000 tokens |
| Image input | Supported | Not emphasized as a native V4 capability | Not emphasized as a native V4 capability |
| Video input | Supported | Not emphasized as a native V4 capability | Not emphasized as a native V4 capability |
| Reasoning mode | Always enabled; adjustable effort | Thinking and non-thinking | Thinking and non-thinking |
| Tool calling | Supported | Supported | Supported |
| Main advantage | Multimodal agents and complex projects | Advanced reasoning and coding | Speed and low API cost |
How Is Kimi K3 Different From DeepSeek V4?
Multimodal Capabilities
The clearest difference is that Kimi K3 is a natively visual model.
Its ability to understand images and videos gives it a broader range of possible applications. A single workflow can combine written instructions, screenshots, design references, code and video content without requiring a separate vision model.
By comparison, the official DeepSeek V4 release focuses mainly on text, reasoning, coding, long context and tool use. This does not make V4 less capable overall, but it does mean that developers may need an additional vision model when building applications that rely heavily on visual inputs.
Consequently, K3 has a structural advantage in website creation, interface testing, presentations, games and other visually intensive workflows.
Reasoning and Coding Strategy
Both model families are designed for AI coding agents, yet they approach reasoning differently.
Every Kimi K3 API request uses reasoning. Developers can adjust how much reasoning effort the model applies, but they cannot fully disable the thinking process.
DeepSeek V4 provides more flexibility by supporting both thinking and non-thinking modes. A difficult programming problem can be routed to thinking mode, while a simple classification or formatting request can use non-thinking mode for a faster response.
This distinction matters when controlling AI token consumption. Reasoning models can generate additional tokens internally or in their reasoning output, increasing latency and cost. Applications with mixed workloads may therefore benefit from DeepSeek’s dual-mode design.
API Pricing
Price is arguably DeepSeek V4’s strongest advantage.
At the time of writing, the official Kimi K3 API costs $3.00 per million uncached input tokens and $15.00 per million output tokens. Cache-hit input costs $0.30 per million tokens.
DeepSeek V4 is substantially less expensive:
| Model | Cache-Hit Input | Cache-Miss Input | Output |
| Kimi K3 | $0.30 | $3.00 | $15.00 |
| DeepSeek V4 Pro | $0.003625 | $0.435 | $0.87 |
| DeepSeek V4 Flash | $0.0028 | $0.14 | $0.28 |
Prices are calculated per one million AI tokens. DeepSeek’s official API documentation also notes that rates may change, so developers should verify current pricing before estimating production costs.
For large-scale text generation, customer service or document processing, the difference can become substantial. K3’s premium may still be justified when native visual understanding or stronger long-horizon execution reduces the need for additional models and repeated human intervention.
API Compatibility and Integration
Both providers offer APIs that can be integrated into external applications.
The Kimi K3 API uses an OpenAI-compatible chat-completions format. Developers can connect through the OpenAI SDK by changing the API base URL, API key and model name.
DeepSeek V4 supports both OpenAI Chat Completions and Anthropic-compatible API interfaces. Existing applications can generally migrate by updating the base URL and selecting either deepseek-v4-pro or deepseek-v4-flash.
As a result, neither model requires developers to rebuild an entire application from the beginning.
Which Model Is Better for Your Application?
There is no universal winner in the Kimi K3 vs DeepSeek V4 comparison.
Kimi K3 is likely to be the better option when a workflow combines coding with screenshots, images, videos or other visual information. It is also attractive for complex projects that require a model to plan, use tools, review its own output and continue working over an extended period.
DeepSeek V4 Pro is more appropriate when the priority is advanced reasoning or coding at a significantly lower token price. Its one-million-token context window also makes it suitable for large repositories, extensive knowledge bases and long technical documents. Meanwhile, V4 Flash provides an economical solution for high-frequency API requests. It may not be the first choice for the most demanding reasoning problems, but its lower cost can be extremely valuable for applications processing millions of AI tokens each day.
In practice, many businesses should avoid depending entirely on one model.
Access Kimi K3 and DeepSeek V4 Through the CostRouter API
Managing separate model providers creates unnecessary operational work. Each platform may require a different account, API key, billing balance, rate limit and integration process.
CostRouter simplifies this process through a unified multi-model API. With one CostRouter API key and a compatible API endpoint, developers can connect their applications to models such as Kimi K3, DeepSeek V4, GPT, Claude, Gemini, Grok and other leading AI models. Switching models does not require rebuilding the entire application.
This approach provides several practical benefits:
- One API key for multiple AI model providers
- Faster model switching and testing
- Centralized AI token usage records
- Simplified billing and cost management
- Flexible routing for different types of requests
- Reduced dependence on a single model provider
- Backup model options when one provider is unavailable
For instance, visual development tasks could be routed to Kimi K3, difficult text reasoning could use DeepSeek V4 Pro, and high-volume routine requests could be sent to V4 Flash.
A multi-model API strategy allows businesses to select the right balance of quality, speed and price for every request rather than forcing one AI model to handle every workload.
Frequently Asked Questions
Is Moonshot Kimi 3 the Same as Kimi K3?
Yes. Some users search for “Moonshot Kimi 3” or “Kimi 3,” but the model’s official name is Kimi K3. It was developed by Moonshot AI as the successor to the earlier Kimi model generations.
Does Kimi K3 Support Images and Videos?
Yes. The Kimi K3 API supports native visual understanding and can process both image and video inputs. This capability is especially useful for multimodal AI agents, visual coding and content-analysis workflows.
Is DeepSeek V4 Cheaper Than Kimi K3?
Based on the providers’ official list prices in July 2026, both DeepSeek V4 Pro and V4 Flash are considerably cheaper per million tokens than Kimi K3. Nevertheless, the final cost of an application also depends on prompt length, output length, cache-hit rates, reasoning usage and the number of model calls.
Can I Use Kimi K3 and DeepSeek V4 With One API Key?
A multi-model AI API provider such as CostRouter can provide unified access to different model families through one account and API interface. This makes it easier to compare models, control AI token costs and change the model used by an existing application.
Final Thoughts
Kimi K3 and DeepSeek V4 represent two different directions in the development of open AI models. Moonshot AI is pursuing a large-scale, natively multimodal system capable of handling visual information, extended coding projects and complex end-to-end knowledge work. DeepSeek, in contrast, emphasizes efficient reasoning, flexible model sizes and extremely competitive API pricing.
For visual and multimodal agent workflows, Kimi K3 offers capabilities that DeepSeek V4 does not currently emphasize. For affordable coding, reasoning and high-volume text processing, DeepSeek V4 remains difficult to ignore. Rather than treating the decision as a permanent choice, developers can use a multi-model API to evaluate both models in real workloads. The most effective AI infrastructure is not necessarily built around the largest model—it is built around selecting the right model, at the right price, for each individual task.