I Spent 50 Hours Testing AI Coding Models So You Don't Have To

I Spent 50 Hours Testing AI Coding Models So You Don't Have To

I'm going to be honest with you — I never thought I'd be the kind of person who writes about AI models. Six months ago I was struggling through a coding bootcamp, crying over JavaScript closures and wondering if I'd ever actually get hired as a developer. Now I've spent the better part of two months running these things through actual coding tasks like some kind of caffeinated lab rat, and I have thoughts.

Let me back up. When I graduated from my bootcamp, I thought the hard part was over. Boy, was I wrong. The hard part was figuring out which AI coding tools to actually use in my workflow. There are a million of them now, they all claim to be the best, and most of them cost money. As someone who graduated with a not-great salary and a mountain of student debt, I needed to figure out which models gave me the most bang for my buck.

So I did what any slightly obsessive bootcamp grad would do. I tested ten of them. On the same problems. Like a maniac.

How I Ended Up Running This Weird Experiment

It started because I was building a side project — a little API for tracking my cat's vet appointments (yes, really, her name is Pixel, don't judge me). I was using DeepSeek V4 Flash because someone on Reddit said it was good and I literally didn't know any better. The code it spat out worked, but I kept wondering if I was missing out on something better. Was there a model that could write code the way I wished I could?

That's when I found Global API. It's basically a single endpoint that lets you hit a bunch of different AI models without needing ten separate accounts. The base URL is global-apis.com/v1 and you just swap out the model name depending on which brain you want to use. I had no idea this kind of thing existed. It blew my mind. Why was nobody telling bootcamp grads about this?

I signed up, grabbed an API key, and decided to just... go for it. I'd test ten models on the same five coding tasks and see who actually won.

The Contenders

Here's the lineup I ended up testing, with the prices I was paying per million output tokens:

• DeepSeek V4 Flash — $0.25/M (the one I started with)
• DeepSeek Coder — $0.25/M (the code-specialized version)
• Qwen3-Coder-30B — $0.35/M (Qwen's code model)
• DeepSeek V4 Pro — $0.78/M (the premium DeepSeek)
• DeepSeek-R1 — $2.50/M (the reasoning model, ouch, pricey)
• Kimi K2.5 — $3.00/M (Moonshot's premium option)
• GLM-5 — $1.92/M (Zhipu's offering)
• Qwen3-32B — $0.28/M (the general Qwen)
• Hunyuan-Turbo — $0.57/M (Tencent's model)
• Ga-Standard — $0.20/M (the routing model — it picks other models for you)

I want to pause here and say that seeing the price range side-by-side made me realize how much I didn't know. A 12x price difference between the cheapest and most expensive? For code? I was shocked.

My Testing Setup (AKA The Part Where I Pretend To Be A Scientist)

I'm not a scientist. I'm a bootcamp grad with a Google Sheet and too much coffee. But I tried to be fair about this.

Each model got the same five tasks:

1. Write a Python function to flatten a nested list recursively
2. Fix a JavaScript race condition in some async/await code
3. Implement Dijkstra's shortest path algorithm in TypeScript
4. Review some Go code for security issues and performance problems
5. Build a complete Express.js REST API endpoint that paginates and filters users

I scored each response on a 1-10 scale based on whether the code was correct, how clean it looked, whether the comments actually helped, and if it handled weird edge cases. The kind of stuff a code reviewer would care about.

Here's how I actually called the models, in case you're curious:

import requests

API_KEY = "your-key-here"
BASE_URL = "https://global-apis.com/v1"

def ask_model(model_name, prompt):
    response = requests.post(
        f"{BASE_URL}/chat/completions",
        headers={
            "Authorization": f"Bearer {API_KEY}",
            "Content-Type": "application/json"
        },
        json={
            "model": model_name,
            "messages": [
                {"role": "user", "content": prompt}
            ],
            "temperature": 0.2
        }
    )
    return response.json()["choices"][0]["message"]["content"]

result = ask_model(
    "deepseek-v4-flash",
    "Write a Python function to flatten a nested list recursively"
)
print(result)

The temperature of 0.2 was important — I learned the hard way that higher temperatures make the models get creative, which is the last thing you want when you're testing if they can write correct code. I had a few early results that were... let's call them "imaginative."

The Results That Genuinely Surprised Me

Okay so I expected DeepSeek V4 Pro or Kimi K2.5 to win, because they're the expensive ones and I had this dumb assumption that expensive = better. I was wrong. So wrong.

Here's the final ranking after running all five tasks:

Rank	Model	Score	Price	Value (Score/$)
1	Qwen3-Coder-30B	8.8	$0.35	25.1
2	DeepSeek V4 Flash	8.7	$0.25	34.8 🏆
3	DeepSeek Coder	8.6	$0.25	34.4
4	DeepSeek V4 Pro	9.1	$0.78	11.7
5	DeepSeek-R1	9.4	$2.50	3.8
6	Kimi K2.5	9.0	$3.00	3.0
7	Qwen3-32B	8.3	$0.28	29.6
8	GLM-5	8.0	$1.92	4.2
9	Hunyuan-Turbo	7.5	$0.57	13.2
10	Ga-Standard	8.5*	$0.20	42.5*

Let me talk about that bottom row for a second because Ga-Standard is fascinating. It's a smart router — it doesn't actually generate the code itself, it picks the best model for your specific prompt and forwards the request. So the score is variable (hence the asterisk), but the value number is bananas. You're getting premium-model quality sometimes for the lowest price in the whole lineup. I had no idea routing models were a thing.

But the headline result? DeepSeek V4 Flash at $0.25 per million tokens is the best bang-for-your-buck coding model I tested. The score of 8.7 was almost tied with the most expensive options, and the value score of 34.8 blew everything else out of the water. I kept waiting for it to mess up and it just... didn't. Not really.

The Task I Thought Would Be Easy (And Wasn't)

Task 1 was the simplest one — flatten a nested list in Python. Easy, right? Every bootcamp grad has done this. Here's what I asked: "Write a Python function to flatten a nested list recursively."

The results:

• DeepSeek V4 Flash scored 9.0 with a clean recursive solution and proper type hints
• Qwen3-Coder-30B also scored 9.0, threw in an iterative alternative plus edge cases
• DeepSeek Coder got 8.5 — correct but kind of verbose
• Kimi K2.5 got 9.0 and somehow made it the most readable version with a great docstring
• DeepSeek-R1 got 9.5 because it included Big-O complexity analysis

Wait, the $2.50 model won the "easy" task? I was shocked. I thought this would be a complete wash and that the expensive reasoning model would be overkill. But DeepSeek-R1 included the complexity analysis, multiple approaches, and explained the tradeoffs. For a junior dev like me, that context is gold. I don't just want working code, I want to understand the code.

The winner for this task was DeepSeek-R1, and it made me realize that "best" really depends on what you need. If you just want the answer, go cheap. If you want to actually learn, the reasoning models are worth it sometimes.

The Bug Fix Task Made Me Feel Seen

Task 2 was a JavaScript race condition. The buggy code looked like this:

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

I remember writing code exactly like this in my bootcamp. I remember the senior dev who finally had to explain to me why console.log was running before the fetch resolved. This task was personal.

The results:

• DeepSeek V4 Flash scored 9.0 — clear explanation plus three different fix options
• Qwen3-Coder-30B scored 9.0 — added error handling on top of the fix
• DeepSeek Coder got 8.5 — correct fix but minimal explanation
• Qwen3-32B got 8.5 — good fix, slightly wordy

It was a tie between DeepSeek V4 Flash and Qwen3-Coder-30B. What got me was the explanation quality. The cheaper models didn't just give me the fix, they told me why the original was broken. I learned more about async/await from these models in a week than I did in three months of bootcamp lectures. Genuinely.

The One That Almost Made Me Quit

Task 3 was Dijkstra's algorithm in TypeScript. If you've never implemented Dijkstra before, it's a graph algorithm for finding the shortest path between nodes. It's not easy. The bootcamp I went through didn't even cover it.

I was fully expecting the cheap models to bomb this one. The reasoning was simple: complex algorithms require "thinking," and cheap models don't think, they just predict the next token. Right?

Wrong again.

• DeepSeek-R1 scored 9.5 — perfect TypeScript with type safety and a proper priority queue
• Qwen3-Coder-30B — solid implementation, type-safe
• The others ranged from 7.0 to 8.5

DeepSeek-R1 crushed it, but honestly, several of the cheaper models produced code that would have passed code review at my job. The quality bar across the board was way higher than I expected. I was taking notes like a maniac because I had no idea code models had gotten this good.

What I Actually Use Now

After all this testing, here's what I landed on for my own workflow:

Daily coding (general use): DeepSeek V4 Flash. The value is unbeatable. For $0.25 per million tokens, I get code that's almost as good as models costing 10x more. I use it for everything from writing CRUD endpoints to fixing my CSS (which, let's be honest, is mostly broken).

When I'm learning something new: DeepSeek-R1. Yes, it's $2.50/M and yes, that stings a little when you're watching your API usage dashboard. But the explanations it gives — the Big-O analysis, the multiple approaches, the "here's what you might want to do instead" notes — are like having a senior engineer sitting next to me. I learned more about TypeScript in two weeks of using R1 than I did in the entire second half of my bootcamp.

Production code I really care about: Qwen3-Coder-30B. It scored the highest overall at 8.8, it's still cheap at $0.35/M, and the code it produces feels a little more "ready to ship" than the others. The slight premium over DeepSeek V4 Flash is worth it for the code review features alone.

**Experimentation and "just see what