Two Chinese AI labs have become household names among developers worldwide: DeepSeek, whose models shook the industry in early 2025, and Moonshot AI, maker of the Kimi models. In 2026, both released major new systems. DeepSeek V4 arrived on 24 April 2026, and Kimi K3 followed on 16 July 2026. If you are a developer deciding which one to test first, here is a practical comparison.
The two companies
DeepSeek is based in Hangzhou and is backed by the quantitative hedge fund High-Flyer. It became famous for releasing strong open models at very low prices, starting with DeepSeek-R1 in January 2025.
Moonshot AI is based in Beijing and was founded in 2023. Its Kimi assistant built a following for handling long documents, and its Kimi K2 models earned a reputation for agentic coding.
Both labs release open-weight models, meaning developers can download and run them, and both offer low-cost APIs.
The models at a glance
DeepSeek V4 comes in two versions. According to reports on the launch, V4-Pro has 1.6 trillion total parameters with about 49 billion active per token, and V4-Flash has 284 billion total parameters with about 13 billion active. Both support up to one million tokens of input and up to 384,000 tokens of output. They offer three reasoning modes: non-thinking, high thinking and maximum thinking.
Kimi K3 is a single, larger model with 2.8 trillion parameters, a one million token context window, and support for text, image and video input. Reasoning is always on.
Both use mixture-of-experts designs, which activate only part of the model for each token to keep running costs down.
Price
This is where the difference is stark. Reported API prices at launch were:
- DeepSeek V4-Flash: about $0.14 per million input tokens and $0.28 per million output tokens.
- DeepSeek V4-Pro: about $1.74 per million input tokens and $3.48 per million output tokens.
- Kimi K3: about $3 per million fresh input tokens and $15 per million output tokens, with cached input much cheaper.
DeepSeek is the clear winner on price, especially V4-Flash, which is among the cheapest capable models available anywhere. Kimi K3 is more expensive but still well below the most expensive American frontier models.
Coding
Both companies emphasise coding. DeepSeek reported self-measured scores of 80.6 percent on SWE-bench Verified, a test of fixing real software issues, and a Codeforces competitive programming rating of 3206. Moonshot's launch tables placed Kimi K3 ahead of several rivals on long-horizon coding and reported it ranked first in a frontend coding arena.
The caution is the same for both: many launch figures were self-reported and needed independent confirmation. In practice, both are strong coding models. Your language, framework and type of task will decide which works better for you.
Agentic and browsing tasks
Kimi has built a reputation for agentic work, where the model uses tools and browses the web to complete multi-step tasks. Moonshot reported a BrowseComp score of 91.2 for Kimi K3, which is very strong. If your use case involves research agents or browsing automation, Kimi K3 deserves a close look.
DeepSeek V4 is also capable at agentic tasks, and its low price makes it attractive for agents that run many steps, since every step costs tokens.
Context and multimodality
Both offer one million token context windows, so both can handle long documents and large codebases. Kimi K3 accepts images and video as input, which helps for tasks involving screenshots, diagrams or visual content. Check DeepSeek's current documentation for its multimodal capabilities, since these have evolved across releases.
API compatibility
Switching is easy for both. DeepSeek V4's API is compatible with both OpenAI's and Anthropic's request formats. Kimi K3's API is compatible with OpenAI's software development kit. In many cases, you can test either model by changing the base URL, API key and model name in existing code.
Running them yourself
Both release open weights, but both are very large. Running V4-Pro or Kimi K3 yourself requires serious data centre hardware. V4-Flash is smaller and more practical for self-hosting on capable servers. For most developers, the API or a hosted provider is the realistic option.
Always check the licence for the specific model version before using it commercially.
Privacy and policy considerations
Both companies' own apps and APIs process data on their servers, and both follow Chinese regulations, which can affect answers on politically sensitive topics. If you handle sensitive personal or business data, consider running open weights yourself or through a provider whose data handling you trust. Some organisations and government bodies also have rules about using AI models from particular countries, so check yours.
Which should you try first?
Try DeepSeek V4-Flash first if cost is your biggest concern, if you need to process high volumes, or if you are building a startup prototype on a tight budget.
Try DeepSeek V4-Pro if you want strong coding and reasoning at a moderate price.
Try Kimi K3 first if your work centres on agents, web research, long-horizon coding or tasks involving images and video, and you can accept a higher price.
The best approach is to test both. Take 20 to 50 real examples from your work, run them through each model, and compare accuracy, speed and cost.
The bottom line
DeepSeek and Kimi are both serious options for developers in 2026. DeepSeek leads on price and offers a very cheap Flash model alongside a capable Pro model. Kimi K3 is larger, more expensive and especially strong at agentic and browsing tasks. Both are open-weight, both offer one million token context, and both are easy to test with existing code. Start with the one that matches your biggest constraint, cost or capability, and let your own results decide.
