Grok 4 is everywhere right now, and there’s a lot of hype about it being the best model out there. Elon Musk said that Grok-4 is a "PhD-level in everything" across multiple disciplines, and the benchmarks seem to back up at least some of the claims. On Artificial Analysis's Intelligence Index, it's sitting at 73, which puts it ahead of o3 (70), Gemini 2.5 Pro (70), and Claude 4 Opus (64).
Grok 4 is impressive. It's setting new records on reasoning benchmarks and outperforming established models. But it’s not the only model winning benchmarks. We're seeing a fundamental shift in how AI models approach problems. We’re at a phase of AI development where hype cycles are loud, everywhere, and don’t give the full picture.
As I explore AI Coding Assistants, one of the things that can help developers get the most out of AI coding experience is understanding the models and what they’re best at. The Artificial Analysis Report is a great way to get a better understanding of the best uses of each model.
What Makes Reasoning Models Different
Traditional AI models are prediction engines. They're good at generating the next likely token based on patterns they've seen before. But reasoning models like o1, Claude 4 Sonnet Thinking, and DeepSeek R1 work fundamentally differently.
Instead of immediately outputting an answer, they spend time in a thinking phase where they work through problems step-by-step, considering multiple approaches, and refining their understanding before responding. The performance data from the report shows this:
- Standard models: 0.4-1.6 seconds to first token
- Reasoning models: 25-115 seconds to first token
That delay is the feature. During those seconds, the model is doing what you would do, thinking through the problem systematically.
Let’s think about this another way: Reasoning models are like chess grandmasters.
Traditional AI models are like speed chess players. They see the board, recognize a familiar pattern, and make a move in under a second. It’s fast, impressive, and often good enough.
Reasoning models are like the grandmasters in a tournament match. They don’t move right away. They pause. They visualize multiple scenarios. They think 10 moves ahead. That silence is where the real strategy happens.
That long pause before the first token is where the grandmaster is calculating. Despite what it may feel like, it’s not lag.
The Intelligence Breakthrough
Looking at the Artificial Analysis Intelligence Index, we can see the top performers and how the Reasoning models perform versus the Traditional models in terms of intelligence.
Reasoning Models:
- Grok 4: 73 (top performer)
- o3-pro: 71
- Gemini 2.5 Pro: 70
Traditional Models:
- GPT-4.1: 53
- DeepSeek V3: 53
- Llama 4Maverick: 51
The best reasoning models score 20-30 points higher on intelligence benchmarks than traditional models. This means dramatically better performance on complex coding tasks.
If we continue with the chess analogy, the traditional models are fast players in a local chess club who have seen thousands of games, recognize common openings, and can make solid moves in seconds. The reasoning models have a stronger performance because they can visualize the board 20 moves ahead, and adapt their strategy mid-game. That's like the difference between a strong amateur and a world champion.
Where Reasoning Models Excel
Reasoning models do more than complete development tasks. They can identify issues, explain decisions, and make suggestions. This goes back to the idea of Which Code Assistant Actually Helps Developers Grow?. It’s not just about the coding assistant being most effective. It’s about the model as well. When it comes down to it, reasoning models are better teachers because they can explain their thinking process. Here's some of the ways they’re successful:
- Complex Debugging: Traditional models might help you recognize syntax errors or suggest quick fixes. Reasoning models can trace through complex logic flows, identify subtle bugs, and explain why they occur.
- Architecture Decisions: Reasoning models can weigh architectural trade-offs systematically rather than just suggesting the most common pattern.
- Code Review and Refactoring: Reasoning models can identify bugs, design issues, performance problems, and maintainability concerns that traditional models miss.
In three different evaluations—LiveCodeBench, SciCode, and HumanEval—the top models include Grok 4, o4-mini (high), Gemini 2.5 Pro, and o3.
The Performance Trade-offs
Even grandmasters play slowly. They analyze, visualize, and weigh every move and that takes time. Reasoning models are no different. Their intelligence shows up in their deliberation, not in their speed. And that’s why traditional models aren’t going away, and they don’t need to.
You don’t always need a grandmaster. Sometimes you need a blitz player who makes quick, reactive moves that are good enough to get through your to-do list.
Output Speed (tokens/second):
- Gemini 2.5 Flash: 360 tokens/sec
- Grok 3 miniReasoning: 210 tokens/sec
- o3: 171 tokens/sec
For autocomplete and quick edits, speed is probably more important than having the ability to do complex problem-solving.
The Cost Reality
Cost is always a necessary consideration, and one of the tradeoffs. Is it good enough for the cost? (Output cost)
- o3-pro: $80/million tokens
- Claude 4 Opus Thinking: $75/million tokens
- DeepSeek R1: $2.19/million tokens
- GPT-4o: $10/million tokens
- Grok 4: $15/million tokens
Using o3-pro for every AI interaction would kind of be like hiring a senior architect for every coding task. But for the problems that could take hours of your time, the cost might be justified.
How Reasoning Impacts Development Workflows
Developing new patterns around reasoning models can help you work smarter and more efficiently. Understanding models can help you to set up your AI Coding Assistant to be more effective and cost-effective.
Continue's multi-model model blocks or GitHub Copilot's model settings allow you to become more powerful. Different tasks need different types of intelligence. Your AI Assistant is only as good as the way you configure it. I like to think of the approach as organizing a team of models.
For Autocomplete: Speed matters. Use a lightweight, fast model like Gemini 2.5 Flash or Grok 3 mini for instant suggestions. These models excel at boilerplate and repetitive patterns without slowing you down.
For Editing: Go for balance. Models like Claude 4 Sonnet or o3 give accuracy at a reasonable cost and speed and work great for refactoring or small changes.
For Deep Reasoning: Configure your chat to use heavyweights like Grok4, o4-mini, Claude 4, or DeepSeek R1. Think of these like your chess grandmasters for debugging, architectural trade-offs, and complex logic analysis.
Reasoning models aren't magic. They do have limitations:
- They're Not Always Right
- They're Slower for Everything
- They're More Expensive
- They Can Overthink Simple Problems
The Future of AI-Assisted Development
As these models get faster and cheaper, they'll reshape how we approach complex development challenges. We're moving from "AI that writes code" to "AI that thinks about code." For experienced developers, reasoning models become force multipliers. For junior developers, they can become mentors.