I Tested 10 AI Coding Models and I Was Shocked by the Winner

Check this out: i Tested 10 AI Coding Models and I Was Shocked by the Winner

Okay, so I need to talk about something that completely changed how I think about AI and coding. I graduated from a coding bootcamp a few months ago, and like every new dev out there, I've been on this endless quest to find tools that actually help me ship code faster without making me want to throw my laptop out the window.

I had no idea how deep this rabbit hole went until I started testing AI coding models seriously. Like, I thought ChatGPT was the only real option for a while. Spoiler alert: it's not. Not even close.

Why I Even Started Comparing Models

Here's the thing — when you're fresh out of bootcamp, every dollar counts. I remember staring at API pricing pages at like 2 AM, trying to figure out if I should pay $3 per million tokens for "premium" AI when there's a $0.25 option sitting right there. Was the expensive one really 12 times better? I had no clue.

So I did what any obsessive bootcamp grad would do. I grabbed 10 different AI models, threw the same coding problems at all of them, and started scoring everything like a maniac. It blew my mind what I found.

The Models I Tested

Let me walk you through the lineup. These are all real models you can call through an API:

Model	Who Makes It	Cost per Million Output Tokens	What It's Built For
DeepSeek V4 Flash	DeepSeek	$0.25	General purpose, but really good at code
DeepSeek Coder	DeepSeek	$0.25	Code specialist
Qwen3-Coder-30B	Qwen	$0.35	Code specialist
DeepSeek V4 Pro	DeepSeek	$0.78	Premium general
DeepSeek-R1	DeepSeek	$2.50	Reasoning model (thinks hard about code)
Kimi K2.5	Moonshot	$3.00	Premium general
GLM-5	Zhipu	$1.92	Premium general
Qwen3-32B	Qwen	$0.28	General purpose
Hunyuan-Turbo	Tencent	$0.57	General purpose
Ga-Standard	GA Routing	$0.20	Smart router (picks the best model for you)

When I first saw that Ga-Standard costs only $0.20 per million tokens, I was shocked. Like, twenty cents? That's basically free compared to the $3.00 Kimi K2.5. But I had no idea if cheap meant garbage.

How I Actually Tested These Things

I didn't want to do some half-baked test. I picked 5 coding tasks that any bootcamp grad would recognize:

1. Flatten a nested list in Python — sounds easy, but it's a classic recursive warm-up
2. Fix a JavaScript race condition — async/await bugs that haunted my dreams during bootcamp
3. Implement Dijkstra's algorithm in TypeScript — the graph theory boss fight
4. Review Go code for security issues — pretending I'm a senior engineer for a day
5. Build a paginated REST API endpoint in Express.js — the full-stack bread and butter

Each model got scored from 1 to 10. I looked at whether the code actually worked, how clean it was, whether it had documentation, and if it handled weird edge cases. Real-world stuff.

The Results That Made Me Spit Out My Coffee

Alright, here's where it gets juicy. After running all 10 models through the gauntlet, here's how they ranked overall:

Rank	Model	Score	Price	Value (Score per Dollar)
🥇	Qwen3-Coder-30B	8.8	$0.35	25.1
🥈	DeepSeek V4 Flash	8.7	$0.25	34.8 🏆
🥉	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*

Now let me break this down because it took me a while to wrap my head around it. The "Value" column is basically how much quality you get per dollar. And that Ga-Standard number at the bottom? The asterisk means it's a smart router — it actually picks the best model for whatever task you throw at it. So its score bounces around, but the value score of 42.5 absolutely blew my mind.

I had no idea cheap could mean good.

The Cheap Models Punch Way Above Their Weight

Let me tell you about DeepSeek V4 Flash because this is the one I keep telling all my bootcamp friends about. It scored 8.7 out of 10. That's barely a hair behind models that cost 3 to 12 times more. The value score of 34.8 means you're getting insane bang for your buck.

And Qwen3-Coder-30B? That's the dedicated code model winner. It scored 8.8 — the highest of any model under a dollar. At $0.35 per million tokens, it's a steal. I remember running it on a tough problem and getting back code that looked like a senior dev wrote it. I was shocked, honestly.

The Premium Models Are Good, But Are They Worth It?

Here's where I have to be real with you. DeepSeek-R1 scored a 9.4. That's the highest raw score in the entire test. And Kimi K2.5 wasn't far behind at 9.0. These are genuinely the best models for coding.

But when I looked at the value score — 3.8 for DeepSeek-R1 and 3.0 for Kimi K2.5 — I almost choked. You're paying $2.50 to $3.00 per million tokens for maybe an extra 0.7 points of quality.

Is that worth it? Maybe. If you're working on a really hard algorithmic problem and the reasoning model can solve it in one shot when others would need 10 tries, then yeah, the $2.50 is worth it. DeepSeek-R1 basically thinks through the problem step by step, like a human working through it on a whiteboard. For certain tasks, that's unbeatable.

But for everyday bootcamp-level coding? I had no idea I could save so much money.

The Tasks Where I Learned the Most

The Python Recursive Warm-Up

For the "flatten a nested list" task, DeepSeek V4 Flash and Qwen3-Coder-30B both scored 9.0. Kimi K2.5 also got 9.0, and it added a really clean docstring that I actually learned from. But the standout was DeepSeek-R1 at 9.5 — it included Big-O complexity analysis and multiple solution approaches. I had no idea I could get a free algorithms lesson alongside my code.

The JavaScript Race Condition Fix

This was the task that I personally struggled with during bootcamp. 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!

Every model I tested correctly spotted the issue. Like, 100% of them caught it. That was reassuring. DeepSeek V4 Flash gave me three different fix options with clear explanations. Qwen3-Coder-30B added error handling on top of the fix. Both tied for the win at 9.0. Honestly, for $0.25 and $0.35 respectively, that level of debugging help is unreal.

The Dijkstra Boss Fight

When I asked for Dijkstra's algorithm in TypeScript, this is where things got interesting. DeepSeek-R1 absolutely crushed it with a 9.5 — perfect type safety, used a priority queue properly, the works. The premium models flexed their muscles on this one. But I want you to remember: that 9.5 cost me $2.50 per million tokens.

The Tool That Actually Made My Life Easier

Okay, real talk time. After running all these tests, I needed a way to actually call these models without setting up 10 different accounts and API keys. That's when I found Global API. It's basically a unified gateway where you can access all these models through one endpoint.

Here's what a basic Python call looks like:

import requests

response = requests.post(
    "https://global-apis.com/v1/chat/completions",
    headers={
        "Authorization": "Bearer YOUR_API_KEY",
        "Content-Type": "application/json"
    },
    json={
        "model": "deepseek-v4-flash",
        "messages": [
            {
                "role": "user",
                "content": "Write a Python function to find the longest palindromic substring"
            }
        ],
        "max_tokens": 500
    }
)

result = response.json()
print(result["choices"][0]["message"]["content"])

And here's how I switch to the premium reasoning model when I need it:

import requests

# Use DeepSeek-R1 for those really tough algorithmic problems
response = requests.post(
    "https://global-apis.com/v1/chat/completions",
    headers={
        "Authorization": "Bearer YOUR_API_KEY",
        "Content-Type": "application/json"
    },
    json={
        "model": "deepseek-r1",
        "messages": [
            {
                "role": "user",
                "content": "Implement a red-black tree insertion with all rotation cases"
            }
        ],
        "max_tokens": 2000
    }
)

print(response.json()["choices"][0]["message"]["content"])

The fact that I can flip between a $0.25 model and a $2.50 model using the exact same URL pattern still feels like magic to me. I had no idea this was even possible.

What I Actually Use Day to Day Now

After all this testing, here's my honest workflow as a bootcamp grad:

• For boilerplate, simple functions, and quick scripts: DeepSeek V4 Flash at $0.25. It nails it almost every time.
• When I'm writing production code that needs to be solid: Qwen3-Coder-30B at $0.35. The dedicated code specialist vibe really shows.
• For those brain-bending algorithmic puzzles: DeepSeek-R1 at $2.50. Worth every penny when it solves in one shot what would take me three hours.
• When I genuinely don't know what I need: Ga-Standard at $0.20. The smart router picks something good, and the price is ridiculous.

I used to just default to whatever expensive model had the most Twitter hype. Now I'm spending like 80% less on API costs and getting comparable or better results. It blew my mind when I saw my first month's bill after switching.

The Thing That Surprised Me the Most

The biggest takeaway from all this? Raw score isn't everything