Have you ever encountered such troubles? You want to use AI to help you write code, develop an app or a website, but you find that there are so many AI models on the market (GPT, Claude, Gemini, DeepSeek, etc.), which are dazzling and you don’t know which one to choose. You try one at random, and it seems to be sometimes very useful, but sometimes it is "stupid", answering questions irrelevantly, and even "forgetting" what you have said to it before.

For example, I used Claude 3.7 before, but I found it was not working in recent projects. Some operations that failed several times were successfully completed in one try with Gemini 2.5. From the perspective of competition, the big models of different companies will not collide with each other, but will be optimized from different angles, which means that each big model has its own strengths. So today's topic is: How to choose the right big model when programming AI?

Do you also encounter these troubles?

Imagine that you want to develop a simple "recipe query" APP.

1. 1.  Initial conception:  You ask AI (for example, you choose a model that is good at code generation) to help you plan the core functions, target users, and design style of the app. As a result, the suggestions it gives are very vague and even a bit off topic, because it may not be good at "brainstorming" and understanding business needs.
2. 2.  Write code:  I changed to a model that is said to be very "smart" but expensive, and asked it to write specific code. It did write it, but the speed was a bit slow, and for some simple repetitive codes, it felt a bit "overkill", and my wallet was "bleeding".
3. 3.  Review:  Finally, you want AI to help you check if there are any logical problems in the code of the entire project, or help you write instructions. However, AI loses focus halfway through the code because the length of content it can "remember" (i.e., the "context window") is limited, and it cannot understand the code of your entire project at once.

Do you feel that none of the models are perfect? ​​That's right! The key is that no AI model can be the best in all development links.  The best strategy is to choose the most suitable AI model at different stages of development or for different tasks.  It's like decorating a house. You need different tools to build walls, paint, and connect wires, instead of just a hammer.

Solution: How to choose AI models at different development stages?

Let’s take the development of an APP (such as the “recipe query” APP) as an example to see how to choose and use AI models at different stages:

Phase 1: Idea generation and design (clarifying “what to do”)

• Your goal:  Determine the core functions of the app (such as recipe search, classification, collection, user comments), design style, who the target users are, which pages are needed, etc.
• What AI needs to do:  AI needs to have strong logical reasoning skills  and extensive knowledge  to help you brainstorm, understand your ideas, and give structured suggestions.
• Model recommendation:
• Google Gemini 2.5 Pro:  Has powerful reasoning capabilities and a huge "memory" (context window) to understand complex ideas and needs.
• Anthropic Claude 3.7 Opus (if available and budget sufficient):  Generally regarded as having top-notch reasoning and comprehension skills.
• OpenAI o1 (GPT-4.5):  Also known for its powerful reasoning ability.
• DeepSeek R1 (671B):  Excellent performance in planning and reasoning, cost-effective.
• Cost considerations:  This stage is laying the foundation, and a good plan can avoid a lot of modifications later. It is usually worthwhile to invest in a more powerful model here, which can save you more time and cost later.

Phase 2: Hands-on coding and implementation (turning ideas into code)

• Your goal:  Implement the designed functions line by line with code to build the interface and logic of the APP.
• What AI is needed for:  AI needs to be good at understanding and generating code  , giving code suggestions, explaining the meaning of code, and fixing simple errors.
• Model recommendation:
• Anthropic Claude 3.7 Sonnet:  Considered by many developers to be excellent in terms of code generation quality and compliance, especially when paired with development tools such as Cline.
• OpenAI GPT-4o:  A strong all-rounder with strong coding abilities.
• DeepSeek V3:  Its code implementation capability is close to that of Sonnet, and it is very cost-effective and suitable for daily coding work.
• Google Gemini 2.5 Pro:  With its powerful synthesis capabilities and huge context window, it also has an advantage when dealing with complex code bases.
• Cost considerations:  For daily simple code completion or less complex modules, you can consider using more cost-effective  models, such as  Claude 3.7 Haiku  or  DeepSeek V3  . Leave more expensive and powerful models (such as Claude 3.7 Sonnet or GPT-4o) for complex, core function development.

Phase 3: Testing and fixing bugs (making the app run without errors)

• Your goal:  Find various problems (bugs) that may exist in the app, such as no response when clicking a button, incorrect data display, etc., and fix them.
• What AI is needed for:  AI is needed to understand code logic, identify possible edge cases  , and help write test code or give repair suggestions.
• Model recommendation:
• Anthropic Claude 3.7 (Sonnet or Haiku):  Sonnet is good at understanding complex logic, and Haiku may be sufficient for simple test case generation, which is fast and cheap.
• OpenAI GPT-4o (or its Mini version):  also has good code understanding and generation capabilities, and can handle testing tasks.
• Cost considerations:  Test codes usually have a fixed pattern, and a mid-tier model is often sufficient. For complex test scenarios of core functions, consider using a more powerful model.

Phase 4: Code review and release preparation (final check and improvement)

• Your goal:  Before the app goes live, review all the code as a whole to ensure that the code style is consistent, there are no obvious logical loopholes, and you may need to write some user documentation or instructions.
• What AI is needed for:  AI is needed to be able to process a large amount of code  and understand the structure of the entire project. At this time, the model's "memory" ( context window size  ) is very important. If AI can "read" all your code at once, it will be much more efficient. Some models can also understand pictures (multimodal capabilities) and can help you check UI screenshots or design drawings.
• Model recommendation:
• Google Gemini 2.5 Pro:  has the largest context window (up to 2 million tokens) currently available, making it ideal for reviewing and understanding large code bases.
• Anthropic Claude 3.7 Sonnet:  also has a larger context window (200K tokens), which is suitable for reviewing most projects.
• OpenAI GPT-4o:  The context window is also relatively large and has multimodal capabilities.
• Cost considerations: While models with large context windows are generally more expensive, they can process more information at once, saving you time  by avoiding repeated input and interpretation,  which is often worth the time saved, especially in the later review stages of the project.

Practical advice for new developers:

1. 1.  Understand the "Context Window":  This is like the "short-term memory" of AI (similar to computer memory RAM). It determines how much information AI can process at a time (your code, your questions, its answers). If your project is large or the conversation is long, exceeding this limit, the AI ​​may "forget" the previous content. Pay attention to the size of the context window provided by the model (the unit is Token, which can be roughly understood as a word or character block). For example, Gemini 2.5 Pro is up to 2 million, while Claude 3.7 Sonnet is 200,000.
2. 2.  Start with “just enough”:  You don’t have to use the most expensive and powerful model. You can first try a mid-range model with high cost performance  (such as Claude 3.7 Haiku, DeepSeek V3, Gemini Flash series), and then upgrade to a more powerful model if you find that the performance is not enough.
3. 3.  Division of labor (if the tool supports it):  Some AI programming tools (such as Cline mentioned above) allow you to set different models for "planning" and "execution". You can use a model that is good at thinking (such as Gemini 2.5 Pro, DeepSeek R1) to do planning, and then use a model that writes code quickly and well (such as Claude 3.7 Sonnet, DeepSeek V3) to write the code.
4. 4.  Try more and find your "best partner":  You can refer to the model rankings and other people's recommendations, but in the end, you still need to try it yourself to find out which model is best for you  . You can do more experiments on less important tasks or small personal projects.
5. 5.  Focus on actual results rather than pure running scores:  The benchmark score of the model is only a reference. Its performance in actual use (such as how well it works with the tools you use) is more important.
6. 6.  Don’t consider local models for now:  Although running models on your own computer sounds cost-effective, the performance and reliability of local models (especially in performing complex tasks and using tools) are currently far inferior to cloud models, which may cause you more headaches.