rarenode

Posted on Jun 27

How I Tested 10 AI Coding Models as a Bootcamp Grad

#ai #deepseek #webdev #programming

I'll be honest — three months ago I thought all AI code tools were basically the same. Then I graduated from bootcamp, started building real projects, and realized I was spending a small fortune on AI tokens while getting mixed results. So I did what any curious dev would do. I ran my own experiment.

What happened next genuinely blew my mind. I went in thinking the expensive models would crush the cheap ones. Spoiler: that's not what I found at all.

Here's the full story, complete with the weird surprises, the models that underperformed, and the one that made me do a double-take at its price tag.

Why I Even Bothered Testing These

Let me back up. When I was in bootcamp, my instructor kept saying "use AI as a tool, not a crutch." Cool, but which tool? Every YouTube ad was pushing some new model. My classmates were debating GPT vs Claude vs Gemini like it was politics. And the pricing pages looked like airplane tickets — confusing on purpose.

I was paying $3.00 per million output tokens for one model and $0.25 for another, and honestly I couldn't tell which one was giving me better code. So I grabbed ten popular models, threw the same five coding tasks at each one, and started scoring them like a maniac.

I had no idea what I'd find. But I figured if I'm confused, other bootcamp grads probably are too.

The Lineup

Here's the crew I tested. I picked a mix of cheap, mid-range, and pricey options. The prices below are all per million output tokens, which is what you pay for the actual code the model spits out.

Model	Who's Behind It	Cost per 1M Output Tokens
DeepSeek V4 Flash	DeepSeek	$0.25
DeepSeek Coder	DeepSeek	$0.25
Qwen3-Coder-30B	Qwen	$0.35
DeepSeek V4 Pro	DeepSeek	$0.78
DeepSeek-R1	DeepSeek	$2.50
Kimi K2.5	Moonshot	$3.00
GLM-5	Zhipu	$1.92
Qwen3-32B	Qwen	$0.28
Hunyuan-Turbo	Tencent	$0.57
Ga-Standard	GA Routing	$0.20

The cheapest model on the list is Ga-Standard at $0.20. The most expensive is Kimi K2.5 at $3.00. That's a 15x price difference for what might be similar code quality. Or wildly different. I didn't know yet.

How I Scored Everything

I didn't want this to be a vibes-based review. So I built a simple rubric. Each model got the same five tasks, in the same order, with the same prompts:

Flatten a nested Python list (recursion)
Fix an async/await bug in JavaScript
Implement Dijkstra's algorithm in TypeScript
Review some Go code for security issues
Build a full Express.js REST endpoint

For each one, I scored the output from 1 to 10. I looked at whether the code actually worked (ran without errors), how clean it read, whether it had comments, and whether it handled weird edge cases. Then I averaged the scores and divided by price to get a "value" number. Higher is better.

Was it scientific? Not really. Was it consistent? Mostly. Did I learn a ton? Absolutely.

The Big Surprise

Okay, here's where things got weird. I expected the cheap models to be… cheap. You know, functional but kinda janky. The expensive ones should be elegant and bulletproof. That's the whole point of paying more, right?

Wrong. At least for coding.

Look at this table. It's sorted by my "value" score (quality per dollar):

Model	Quality Score	Price	Bang for Buck
DeepSeek V4 Flash	8.7	$0.25	34.8 🏆
DeepSeek Coder	8.6	$0.25	34.4
Qwen3-32B	8.3	$0.28	29.6
Qwen3-Coder-30B	8.8	$0.35	25.1
Hunyuan-Turbo	7.5	$0.57	13.2
DeepSeek V4 Pro	9.1	$0.78	11.7
GLM-5	8.0	$1.92	4.2
DeepSeek-R1	9.4	$2.50	3.8
Kimi K2.5	9.0	$3.00	3.0
Ga-Standard	~8.5	$0.20	~42.5

I was shocked. The top-quality model (DeepSeek-R1 at 9.4) is one of the worst values because it costs $2.50/M. Meanwhile, DeepSeek V4 Flash scored 8.7 — basically the same quality — at $0.25/M. You're paying 10x more for a 0.7 point quality bump.

That's like paying $15 for a coffee that's only slightly better than the $1.50 gas station version.

The other thing that surprised me? Ga-Standard, the cheapest one at $0.20, actually scored around 8.5 because it's a smart router — it sends your prompt to whichever backend model is best for that particular task. Its score bounces around a bit depending on what you ask, but the value is unbeatable.

What I Learned Task by Task

Task 1: The Recursive Python Thing

The prompt was simple: "Write a Python function to flatten a nested list recursively."

Most models nailed this. Like, embarrassingly nailed it. DeepSeek V4 Flash gave me clean code with type hints. Qwen3-Coder-30B added an iterative alternative. Kimi K2.5 wrote the most readable version with a nice docstring.

But DeepSeek-R1 did something I didn't expect. It included Big-O complexity analysis. It said the recursive solution is O(n) where n is the total number of elements, and then it added an iterative version with the same complexity but no recursion depth risk. I was using f-strings before bootcamp and here I am learning from an AI about algorithm trade-offs. Wild.

DeepSeek-R1 got a 9.5 here. The other models clustered around 8.5 to 9.0.

Task 2: The JavaScript Race Condition

This one tripped me up in bootcamp, so I was curious what the models would say. The bug was classic:

let data = null;
fetch('/api/data').then(r => r.json()).then(d => data = d);
console.log(data); // Always logs null — race condition!

Every single model caught the bug. Every. Single. One. I had no idea AI was this reliable on JavaScript concurrency issues. The fix is to use async/await properly:

async function fetchData() {
  const response = await fetch('/api/data');
  const data = await response.json();
  console.log(data);
  return data;
}

DeepSeek V4 Flash and Qwen3-Coder-30B tied for the win on this one. They both explained the issue clearly and offered multiple ways to fix it. DeepSeek Coder gave the correct fix but with minimal explanation, which scored a bit lower because I wanted to actually understand what went wrong.

Task 3: Dijkstra's in TypeScript

This is where the reasoning models flexed. Dijkstra's shortest path is non-trivial — you need a priority queue, proper TypeScript types, and clean graph representation.

DeepSeek-R1 crushed it. 9.5 score. Perfect type safety, priority queue included, even commented the code. Qwen3-Coder-30B also did well, but R1 was just… better. More thoughtful.

If you've got a hard algorithmic problem, R1 is worth the $2.50/M price tag. For everyday CRUD work, probably not.

Task 4: Go Code Review

I gave each model some sketchy Go code with security holes and performance issues. Things like SQL injection risks, unchecked errors, and an O(n²) loop that could be O(n).

The cheaper models caught most of the issues but missed the subtle ones. The expensive ones were more thorough. Kimi K2.5 stood out here with a detailed review that I actually learned from.

But honestly? For code review, I think you want a thinking model. The difference between "you have an SQL injection" and "you have an SQL injection, here's why, here's how to fix it with parameterized queries, and here's an example in your specific codebase" is what you're paying for.

Task 5: The Full Express Endpoint

"Build a REST API endpoint with Express.js that paginates and filters users."

This was the real-world test. Can the model actually build something I'd ship?

Most did fine. A few nailed pagination defaults, query parameter validation, and error handling. Qwen3-Coder-30B was a standout — it included input sanitization, rate limiting suggestions, and even a brief explanation of the trade-offs between offset and cursor-based pagination.

I shipped parts of its output in an actual side project. I'm not ashamed to admit that.

The Models That Disappointed Me

Hunyuan-Turbo at $0.57/M scored a 7.5. That's the lowest on the list. The code worked, but it was messier than the others, and it skipped edge cases more often. For $0.57, I expected better.

GLM-5 at $1.92/M was a tough sell. It scored 8.0, which is solid, but at nearly $2 per million tokens, the value math just doesn't work. Unless you have a very specific reason to use it, I'd skip it.

And Kimi K2.5 at $3.00/M? Look, it scored a 9.0 and the code was gorgeous. But $3.00/M is a lot. For most projects, you're leaving money on the table.

The Actual Code I Use Now

After all this testing, I switched most of my workflow to DeepSeek V4 Flash for general coding and Qwen3-Coder-30B when I need specialized code work. Both are cheap and good.

Here's how I call them through Global API. It's stupidly simple:

import requests

api_key = "your-api-key-here"
url = "https://global-apis.com/v1/chat/completions"

headers = {
    "Authorization": f"Bearer {api_key}",
    "Content-Type": "application/json"
}

payload = {
    "model": "deepseek-v4-flash",
    "messages": [
        {"role": "user", "content": "Write a Python function to debounce API calls"}
    ],
    "max_tokens": 500
}

response = requests.post(url, json=payload, headers=headers)
print(response.json()["choices"][0]["message"]["content"])

That's it. You swap in whatever model you want. The pricing is the same as what I tested above, and you don't have to sign up for ten different services.

When I need the reasoning model for a tough algorithm problem:

payload = {
    "model": "deepseek-r1",
    "messages": [
        {"role": "user", "content": "Optimize this O(n²) loop to O(n) using a hash map"}
    ],
    "max_tokens": 1000
}

I keep this snippet in a scratch file and just edit the prompt. Saves me a ton of time.

The TL;DR I Wish Someone Told Me in Bootcamp

If you're like me — a bootcamp grad building projects, watching your AI bill, and trying to figure out what actually matters — here's what I'd tell you:

For 90% of coding tasks, DeepSeek V4 Flash at $0.25/M is more than enough. The quality is excellent and your wallet will thank you.
If you're building something code-heavy (like a library or framework), Qwen3-Coder-30B at $0.35/M is purpose-built for it.
For genuinely hard algorithmic or architectural questions, DeepSeek-R1 at $2.50/M is worth the splurge. Use it sparingly.
Skip the $3.00/M models for everyday work. The quality difference doesn't justify the cost.
If you don't want to think about model selection, Ga-Standard at $0.20/M routes to whatever model fits your prompt best. Set it and forget it.

What I'd Do Differently Next Time

If I were running this experiment again, I'd add more languages (Rust, probably, since everyone keeps telling me to learn it). I'd also test on bigger projects — like, give each model an existing codebase and ask it to add a feature. That would tell me way more about real-world usefulness.

But for now? This experiment saved me probably $200 a month in AI costs while keeping the same code quality. That's money I can put toward a real server instead of burning it on tokens.

Give It a Try Yourself

If you want to mess around with these models without signing up for ten different accounts, Global API lets you hit all of them through one endpoint. The pricing is the same as what I tested above, and the setup is genuinely two minutes. Check it out if you want — I just like that I don't have to manage ten API keys.

Anyway, hope this helps someone out there who's staring at a pricing page wondering which model to pick. The answer, at least for now, is: the cheap one is usually fine.

Happy coding. 🚀

DEV Community

How I Tested 10 AI Coding Models as a Bootcamp Grad

Top comments (0)