Before diving into specifics of various models and how to choose the one right for you, I need to address the elephant in the room. OpenAI's o3 has already changed how I use AI on a daily basis. This isn't just another small step for AI tech bros, but a huge step for the humanity. Newer models have greater reasoning capabilities, tend to be more cost-effective and do your tasks better in general.

My name is Artem and I am trying out, using and reviewing a lot of AI Models, and also developing a tool that helps you use multiple LLMs in one chatbot for a fraction of the cost, and also to compare those models online with this tool. But in this article, I will go into the topic of how to find best models for your uses and tasks.

Current Model Landscape in 2025. Comparison Table

Let me break down the current state of the major models as of June 2025, based on my real-world testing. Here are 7 models that are, in general, worth considering as of Summer 2025. Stars here reflect how well those models do certain types of tasks, and I also added some notes to each model.

Get to Understand AI Basics and Access

AI models, specifically Large Language Models (LLMs) like GPT (which stands for Generative Pre-trained Transformer), are advanced computational frameworks designed to understand, generate, and manipulate human-like text based on vast amounts of training data. They use deep learning techniques to predict and generate responses, making them powerful automatization tools.

AI models are usually categorized into private and open-source:

• Private AI models are proprietary technologies developed and owned by organizations. They use these models internally or offer them as commercial services but do not provide public access to the model's underlying code or architecture.

• Open-source AI models are made available with their source code freely accessible to the public. This openness allows developers worldwide to contribute to the model's improvement, understand its functionality, or use it in their projects.

Among the models I just listed in a spreadsheet, only Llama 4 Maverick (along with Scout) is the one released as source‑available under Meta’s new Llama 4 Community License, which is open‑weight but restricted for large‑scale commercial use.

• All the other models—OpenAI o3, o4‑mini, GPT‑4o, Claude 4 Sonnet, Gemini 2.5 Pro, and Grok 3—are fully proprietary,

• This means, no shared weights or open‑core components to most of those models.

• However, Mistral is one of the leading open-source LLM families, offering models like Mistral 7B, Mixtral (MoE), and others—all released under the Apache 2.0 license. These models are known for their strong reasoning and code capabilities & are now widely used in both research and production.

• Mistral and other top open-weight models such as LLaMA 3, Falcon, Qwen 2.5, Baichuan, and DeepSeek provide developers with powerful, flexible alternatives to proprietary systems. They let you do local deployment and full customization.

• A growing number of platforms support these models—Mistral, for example, is available on new.writingmate.ai, a GPT-style writing assistant powered by open-source LLMs.

• Open LLMs are ideal for companies seeking transparency, data privacy, and cost efficiency, and they continue to improve rapidly through global community contributions.

Access Modes: Chatbots and API Keys

In general, AI models can be accessed in several ways:

• Chatbots: These are conversational agents that interact with users in a natural, human-like manner. Chatbots powered by AI models can be found on websites, in applications, and as part of customer service operations, providing responses based on the AI's training and capabilities.

• API Keys with Pay Per Queries: Some AI models are accessible through APIs (Application Programming Interfaces), which require an API key. This setup usually involves a payment structure where users pay based on the number of queries or the amount of computational resources used. This method is commonly used by businesses and developers who need to integrate AI capabilities into their applications.

Task-Specific Performance

Different AI models can be used to solve various tasks, and their effectiveness can vary based on the task:

• Language Understanding: Text-to-text models excel in understanding and generating text, making them ideal for writing, translation, and summarization.

• Image Generation: Models like DALL-E are specifically designed for creating images from textual descriptions, useful in graphic design, art, and more.

• Custom Tasks: Some models are specifically fine-tuned or designed for niche tasks like video, medical AI tools, legal analysis, or coding – it may depend on the knowledge is incorporated into the model.

Finding the Best AI for Your Needs

The field of Artificial Intelligence is currently experiencing a boom, with new models popping up weekly. You don't need to look far for an example: just recently, Meta AI rolled out Llama 4 Scout, Maverick and Behemoth (though I don't advice to use them!), GPT-4o and other GPTs still are ok for general use both through generic chatbot and tools like Writingmate.

So all the developments make one question arise… which AI models are the best? To find the best AI, consider what tasks you need the AI to perform. Are you looking for the best AI for text generation, do you need an AI model for coding? What is the best medical AI tool? Let's highlight several resources that let to compare different AI models side-by-side and make an informed decision based on your specific requirements.

Tools to Compare AI models

Here are three tools you can try that let you compare AI models., both private and & open source. Such a tool lets you find out what model fits you the best and let you know exact parameters, benchmarks, speed, and many more. Each one lets you do that in its own unique way, so it's worth to check out three of them in any case.

1. Chatbot Arena by LMSYS

Chatbot Arena is an extremely popular independent platform for comparing various LLMs, enjoying significant authority in the AI community. Here you can not only see a detailed ranking of AI different models with filters by various parameters, but you can also compare models and rate them yourself. Currently, Chatbot Arena supports about 100 of the most popular models, and the list is constantly expanding.

2. Writingmate AI

I have to mention Writingmate here because it's genuinely changed how I work with LLMs daily. Instead of juggling multiple subscriptions and platforms, I get access to all the major models - including the latest o3 mini, Claude 4 Sonnet, Llama 4 Maverick, and everything else right in one place.

What really makes me base my work around Writingmate was the ability to:

• Test multiple models simultaneously: The side-by-side comparison feature is invaluable;

• Switch between models mid-conversation: Start with GPT-4o Mini, then escalate to o3 if needed, switch to R1 and then make visuals with Stable Diffusion. Or any other workflow which includes multiple AI, right from one web app, no installation, drain or keys;

• Generate images alongside code documentation: Particularly useful for all kinds of diagrams, graphs, visualizations and more. Now also includes new 2025 ChatGPT image generator, also traditional DALLE, Stable Diffusion and Flux1.ai;

• Access top and newest models: New releases are available almost immediately and work fast, at a fraction of a cost of their generic chatbots combined.

Writingmate has a AI Split View feature, which helps send a single query to different models and compare not only the responses but also the task execution speed, the number of tokens used, also the cost of queries. This can be useful for those who want to use models through API for their needs and projects. But remember, Writingmate does not require any API and subscriptions start at 9 dollars per month with a free option available as well. Try it out here:

https://new.writingmate.ai

Another unique aspect of Writingamate is the ability to use many models in countries that are not supported by these models. For example, you can access Claude 4 Sonnet and Opus or Claude 3.7, all popular models from Anthropic, living in Europe while the company seems to not offer such an opportunity in its own chatbot.

• With Writingmate, you can always choose the best AI model for your task, whether it's AI for medical students, or the best AI for engineers, or the best LLM for writing texts.

3. Leaderboard by Artificial Analysis

Artificial Analysis has gathered the top 100 LLMs in one table so that you can conveniently choose the best model for your tasks. Here, you can select models based on various parameters:

• Benchmarks: Chatbot Arena, MMLU, HumanEval, Index of evals, MT-Bench.

• Cost: entry, exit, average

• Speed in tokens/sec: median, P5, P25, P75, P95 (those who understand, understand).

• Context window size.

• Compatibility with the OpenAI library.

• API Provider.

• Latency: median, P5, P25, P75, P95.

Examples of top AIs in each category include:

• Benchmarks: GPT-4o

• Cost: $0.06/1M tokens Llama 3 (8B) via API groq

• Speed: 912.9 tokens/sec Llama 3 (8B) via API groq

• Latency: 0.13s Mistral 7B via API baseten

• Context window size: 1m Gemini 1.5 Pro

A Quick Look at o4 (What We Know So Far)

While o4 hasn't been officially announced yet, there are rumors circulating about OpenAI's next iteration.

• From what industry insiders are suggesting, o4 might focus more on efficiency and real-time reasoning rather than just raw performance improvements.

• In my experience, this could be exactly what we need. Sometimes the most powerful model isn't the most practical one for everyday development work.

What Actually Matters When Choosing an LLM

After working with various models over the years, I've noticed that people often get caught up in benchmark scores and marketing claims. But here's what I've found actually matters in real-world usage:

Performance Where It Counts In my opinion, raw performance metrics only tell part of the story. I've seen models that crush benchmarks but struggle with the specific type of code I write daily. For instance, o3's incredible ARC-AGI score (87.5%) is impressive, but what matters more to me is how it handles my specific Django applications or React components.

Cost vs. Value Reality Check This one hit me hard early on. I was burning through API credits like crazy before I learned to match model capabilities with task complexity. The o3 model, while incredibly capable, can cost 3-4x more than GPT-4o for similar tasks. You don't always need the most expensive model - sometimes GPT-4o Mini does the job just fine.

Consistency and Reliability What I've learned is that consistency often beats peak performance. A model that gives me good results 95% of the time is more valuable than one that gives me amazing results 70% of the time and confusing output the rest.

Real-World Testing Methodology I've Developed

Over the past couple of years, I've developed a systematic approach to testing models:

1. Standardized Test Cases I maintain a set of 20 coding challenges ranging from simple functions to complex system design problems. Each new model gets tested against these same challenges.

2. Time-to-Solution Tracking I measure not just accuracy but how quickly I can get to a working solution, including iterations and refinements.

3. Cost per Task Analysis I track actual costs for comparable tasks across different models, which has revealed some surprising insights about value.

4. Context Retention Testing For longer coding sessions, I test how well models maintain context over extended conversations.

The Current LLM Landscape: What I've Actually Used

Let me walk you through the models I've spent real time with, incorporating insights from recent developments:

OpenAI o3: The Reasoning Powerhouse

The o3 model represents a significant leap forward, especially for complex reasoning tasks. In my testing, it excels at:

• Complex Algorithm Design: When I need to implement sophisticated algorithms or optimize existing ones, o3 consistently provides more elegant solutions.

• System Architecture: Its reasoning capabilities shine when discussing microservices design or database optimization strategies.

• Code Review and Debugging: The model can identify subtle bugs and architectural issues that other models miss.

Real example: When I asked o3 to help optimize a slow database query, it not only suggested index improvements but also identified that the underlying data model could be restructured for better performance. Other models typically only addressed the immediate query optimization.

However, the cost is substantial. In my experience, it's best reserved for genuinely complex problems where the superior reasoning justifies the expense.

GPT-4o and GPT-4o Mini: My Go-To Workhorses

These private models (don't let "OpenAI" name fool you!) still remain my daily drivers, and for good reason:

GPT-4o: Still my heavy lifter for most coding tasks. While o3 might be more capable, GPT-4o offers the right balance of performance and cost for 80% of my work.

GPT-4o Mini: This has become my secret weapon for routine tasks. Recent updates have improved its coding capabilities significantly, and for debugging, documentation, and simple feature implementation, it's hard to beat the value proposition.

Claude 4 Sonnet: The Thoughtful Alternative

Claude 4 Sonnet has impressed me consistently with its approach to problem-solving. There's something about how it structures responses that makes complex solutions easier to understand and implement.

What sets Claude apart in my experience:

• Code Explanation: Exceptional at breaking down complex code for team discussions;

• Best Practices: Consistently suggests more maintainable coding approaches;

• Security Awareness: Seems to be better than most models at identifying potential security issues.

You can check out more detailed comparisons between Claude models in our Claude 4 Sonnet vs Opus analysis.

Llama 4 Maverick: The Open Source Champion

The latest Llama iteration has closed the gap significantly with commercial models. While it doesn't match o3's reasoning capabilities, it's surprisingly competent for most coding tasks. Many reviews of this model are surprisingly negative and my experience with it was also not that great. Not the worst model ever, but comparing to o3 or new Claude, this just doesn't work so well with my tasks and needs.

Where Llama 4 Maverick seems to work well enough:

• Custom fine-tuning possibilities: For specialized domains or coding styles

• Privacy-first scenarios: When code cannot leave your infrastructure

• Cost-effective scaling: For high-volume, lower-complexity tasks

A Quick Word on Vibe Coding

Before we dive deeper, let me touch on something that's become pretty popular lately - vibe coding. This is essentially coding based on intuition and flow rather than rigid planning. It's become especially relevant with AI assistance because these tools can help explore ideas rapidly.

Best Practices for Vibe Coding:

• Start with clear but flexible goals

• Use AI to explore different approaches quickly

• Don't be afraid to iterate and pivot based on what feels right

• Document your discoveries as you go

• Set aside time later for proper architecture review

Concerns to Watch Out For:

• Code quality can suffer without proper structure

• Technical debt accumulates quickly when following intuition over planning

• Integration challenges when merging different exploratory approaches

• Security and performance considerations might be overlooked

In my experience, AI models work great for that fashionable vibe coding. You see, they can quickly generate multiple approaches. But you still need to apply good engineering judgment afterward, no matter what LLM you use to vibe code ;)

Cost Considerations of Various LLM

Let's talk money because this matters for most of us. The pricing landscape has become more complex when high-capability models like o3 were finally shipped.

My Updated Cost Strategy (2025):

• GPT-4o Mini for routine tasks (70% of my work): Documentation, simple debugging, code reviews

• GPT-4o for standard development (25% of my work): Feature development, moderate complexity problems

• o3 for complex challenges (5% of my work): System design, optimization, complex debugging

This approach has actually improved my cost efficiency compared to 2024, even despite having access to more expensive models. The key is being strategic about when to use each tool.

Real Cost Example: A recent project required building a complex data processing pipeline. Using o3 for the initial architecture design cost about $15 but saved me probably 4-6 hours of work. Then I used GPT-4o for implementation details and GPT-4o Mini for documentation. Total AI cost: ~$25. Time saved: ~8 hours.

Specialized Use Cases: What Works Where

Through extensive testing, I've found certain models excel in specific areas:

Complex System Design: o3 > Claude 4 Sonnet > GPT-4o Web Development: GPT-4o > GPT-4o Mini > Claude 4 Sonnet Data Science: Claude 4 Sonnet > o3 > GPT-4o Mobile Development: GPT-4o > Claude 4 Sonnet > GPT-4o Mini DevOps and Infrastructure: GPT-4o > Llama 4 Maverick > GPT-4o Mini Code Documentation: Claude 4 Sonnet > GPT-4o > GPT-4o Mini

The Future: What I'm Watching

Based on what I'm seeing in the field, here are the trends I'm keeping an eye on:

• Reasoning-First Models: o3 seems to be leading a trend toward models that prioritize deep reasoning over quick responses. This could dramatically change how we approach complex problems.

• Specialized Coding Models: We're starting to see models trained specifically for coding tasks. These could be game-changers for developers.

• Cost Optimization: As capabilities increase, I also expect more sophisticated pricing models. I believe models can charge based on actual complexity rather than flat rates. But when you use Writingmate, you just pay subscription that starts at 9 dollars, no API needed and use of multiple model simplified as much as possible.

• Real-time Collaboration: Models tat can directly interact with development environments & finally participate in real-time coding sessions almost as human collaborator does.

My Honest Recommendations on Choosing AI

After all this experimentation and the recent developments, here's what I'd recommend:

If you're just starting with AI coding: Begin with GPT-4o through Writingmate. It's forgiving, cost-effective, and will give you a good sense of capabilities. Also works on a free tier & adds more features in comparison to OpenAI generic chatbot.

If you're a professional developer: Get access to multiple models (Claude 4, o3 mini, Mistral, Llama etc.) through Writingmate. The side-by-side comparison feature alone is worth it in my opinion and experience.

If you're working on complex systems: Budget for o3 usage on your most challenging problems. The reasoning capabilities are genuinely transformative for certain tasks.

If you're cost-conscious: Stick with the GPT family for most work and use higher-tier models strategically. You can also adjust parameters or fine-tune to find the ideal price-effectiveness ratio for you. This is quite advanced but also can be worth it.

Making the Right Choice for Your Situation

In my experience, the "best" LLM in 2025 is the one that fits your specific workflow, budget, and technical requirements. Don't get caught up in the hype around any particular model. I will recommend to, instead, focus on what actually solves your problems.

The rapid evolution we've seen in 2024 and early 2025 (particularly with o3) shows this landscape will keep changing. That's why I recommend platforms like Writingmate that give you simple access to multiple models and don't lock you into a single option.

Final Thoughts

After using AI for coding for a couple of years, after witnessing the dramatic improvements of 2024-2025, I'm sure we're experiencing a genuine inflection point now. That gap between human reasoning and AI capabilities is narrowing even faster than I expected.

The key is staying flexible, testing regularly with your specific use cases & not being afraid to switch approaches when something isn't working. Models like o3 still show us where we're heading, but tools like GPT-4o and Claude 4 Sonnet are also quite practical for day-to-day work and for general tasks. Look back at a spreadsheet at the beginning to see top use cases for each model.

Remember, these tools are meant to augment your skills, not replace your judgment. The best results come from understanding both the capabilities and limitations of each model and applying them thoughtfully to real problems. Remember to try Writingmate if you would like an all-in one AI tool that is both affordable and has huge capabilities and 300+ AI models including all the top and recent ones.

For more detailed articles on AI and development, visit our blog that we make with a love of technology, people, and their needs. We also have a comprehensive guide on which LLM to use for coding if you want to dive deeper into coding-specific applications.