I Tested 10 AI Coding Models and Got Totally Surprised

I Tested 10 AI Coding Models and Got Totally Surprised

Okay, I need to tell you about something that completely rewired how I think about coding. I graduated from a coding bootcamp about six months ago, and like every new dev out there, I've been drowning in tabs, Stack Overflow, and that one tutorial guy on YouTube who somehow makes everything look easy. Then a friend told me about AI coding models and honestly, I had no idea how good they've gotten. So I did what any curious new dev would do. I tested a bunch of them. Like, really tested them. And what I found honestly blew my mind.

Let me walk you through what I learned, what shocked me, and which models I think are actually worth your money if you're a beginner like me trying to figure all of this out.

Where I Started (And Why I Was Skeptical)

When I first heard about AI writing code, I pictured the bad autocomplete you get in your IDE that suggests a variable name you didn't ask for. I was fully prepared to be disappointed. The bootcamp had drilled into me that real engineers understand their code, they don't just copy-paste from a chatbot.

But here's the thing. AI in 2026 is not the AI of 2023. I was shocked when I asked one model to write a function and it came back with proper type hints, edge cases handled, AND a docstring. Like, more documented than some of my bootcamp projects, no offense to myself.

So I set up a little experiment. Ten models. Five tasks. Four programming languages. One very tired bootcamp grad with too much coffee and a Google Sheet full of scores.

The Models I Put to the Test

Here's the lineup. I went with a mix of "I heard about this one" picks and "this one is supposedly specialized for code" picks. Here's the full list with what each one cost per million output tokens:

#	Model	Provider	Output Price (per 1M tokens)	What Kind
1	DeepSeek V4 Flash	DeepSeek	$0.25	General (great at code)
2	DeepSeek Coder	DeepSeek	$0.25	Built specifically for code
3	Qwen3-Coder-30B	Qwen	$0.35	Code-specialized
4	DeepSeek V4 Pro	DeepSeek	$0.78	Premium general model
5	DeepSeek-R1	DeepSeek	$2.50	Reasoning model
6	Kimi K2.5	Moonshot	$3.00	Premium general model
7	GLM-5	Zhipu	$1.92	Premium general model
8	Qwen3-32B	Qwen	$0.28	General purpose
9	Hunyuan-Turbo	Tencent	$0.57	General purpose
10	Ga-Standard	GA Routing	$0.20	Smart routing

If you're like me, you immediately zoomed in on the prices and went "wait, some of these cost 15 times more than others?" Yeah. Same reaction. That was part of why I wanted to do this. Price tags in AI land are wild.

My Testing Setup (Nothing Fancy)

I made a simple scoring system. Each model got the same five tasks. I scored them 1 to 10 based on whether the code actually worked, how clean it looked, whether they explained it, and how well they handled weird edge cases. Here's what I asked:

1. Write a Python function that flattens a nested list (recursively)
2. Fix a race condition bug in some JavaScript async/await code
3. Implement Dijkstra's shortest path algorithm in TypeScript
4. Review a Go program for security holes and performance issues
5. Build a paginated, filterable REST API endpoint with Express.js

That last one was brutal. I gave them all the same prompt and timed how long their answers took.

The Overall Results That Made Me Rethink Everything

Here's where things got really interesting. After scoring every single response, this is how the leaderboard shook out:

Rank	Model	Score	Price	Value Score
🥇	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, I know what you're thinking. "Why is the most expensive model not winning?" That was my exact reaction. I went into this thinking the priciest one would crush everything. Nope. Kimi K2.5 at $3.00 per million tokens got a 9.0, which is great, but when I divided score by price to get the "value" number, it absolutely tanked. Score-per-dollar matters way more than raw score, especially when you're a beginner paying out of pocket.

The asterisks on Ga-Standard are because that one is a router. It sends your request to whatever model it thinks will do best for the job. So its score bounces around depending on what you're asking. Smart idea honestly.

What Surprised Me Most (Task by Task)

Let me walk you through each task because the story gets juicier the deeper you go.

Task 1: Flattening a Nested List

I asked everyone to write a recursive Python function. Pretty straightforward stuff. Here are the highlights:

• DeepSeek V4 Flash scored 9.0 — clean recursive solution with type hints
• Qwen3-Coder-30B scored 9.0 — same score, but went the extra mile with an iterative version AND edge cases
• DeepSeek Coder scored 8.5 — got it right but was wordy
• Kimi K2.5 scored 9.0 — the most readable one, with a nice docstring
• DeepSeek-R1 scored 9.5 — this one shocked me, it included Big-O analysis

DeepSeek-R1 won this round. The reasoning model actually thought through the problem out loud and gave me complexity analysis I didn't even ask for. Felt like having a tutor.

Task 2: The Async/Await Bug

This one I love. The buggy code was a classic race condition:

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. Not one of them missed it. I was shook. Here's how they scored:

• DeepSeek V4 Flash: 9.0 — clear explanation plus three different fix options
• Qwen3-Coder-30B: 9.0 — same score, but added error handling I didn't think to ask about
• DeepSeek Coder: 8.5 — fixed it correctly but barely explained why
• Qwen3-32B: 8.5 — solid fix, just a little wordier than the others

Tie between DeepSeek V4 Flash and Qwen3-Coder-30B. Both gave me three different ways to fix the bug, which is honestly more useful than just "here's one answer." Different approaches teach you different things.

Task 3: Dijkstra's Algorithm in TypeScript

This was the hard one. I picked it specifically because implementing Dijkstra's is no joke. Priority queues, type safety, the works.

• DeepSeek-R1: 9.5 — nailed it with full type safety and a proper priority queue
• Qwen3-Coder-30B also crushed it here

Honestly, the bigger takeaway from this task was that the cheap models held their own against expensive ones when the problem was well-defined. The reasoning model still edged everyone out, but the gap was way smaller than I expected.

Task 4 & 5: The Real World Stuff

For the Go security review, Qwen3-Coder-30B pointed out SQL injection risks and a goroutine leak I had completely missed. Hunyuan-Turbo missed the goroutine issue, which lost it points.

For the full Express.js API build, the code-specialized models produced the cleanest results. DeepSeek V4 Flash gave me pagination that actually handled empty result sets properly, which is something I never would have thought of as a beginner.

The Pricing Lesson That Hurt My Brain

Here's something nobody told me when I started this journey: a $3.00 model is not necessarily 12 times better than a $0.25 model. DeepSeek V4 Flash at $0.25/M got an 8.7. Kimi K2.5 at $3.00/M got a 9.0. So you're paying 12x the price for a 0.3 score improvement. That math does not math.

For my own projects, I mostly use DeepSeek V4 Flash now. If I'm stuck on something really tricky, I'll bump up to DeepSeek-R1 at $2.50/M, but only when I genuinely need that reasoning boost.

How I Actually Call These Models

Since I'm a Python person (bootcamp indoctrination, sorry JavaScript people), here's how I typically call one of these models. I use Global API to keep things simple:

import requests

url = "https://global-apis.com/v1/chat/completions"
headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json"
}

payload = {
    "model": "deepseek-v4-flash",
    "messages": [
        {"role": "user", "content": "Write a Python function to flatten a nested list recursively"}
    ],
    "max_tokens": 500
}

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

That's literally it. Point your request at global-apis.com/v1, swap in the model name, and you're off. When I want to switch to Qwen3-Coder-30B for code-specific tasks, I just change "deepseek-v4-flash" to "qwen3-coder-30b" in the payload. Same code, different brain.

And here's another quick one I use when I want to do a side-by-side comparison:

import requests

def ask_model(model_name, prompt, api_key):
    url = "https://global-apis.com/v1/chat/completions"
    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json"
    }
    payload = {
        "model": model_name,
        "messages": [{"role": "user", "content": prompt}],
        "max_tokens": 1000
    }
    r = requests.post(url, headers=headers, json=payload)
    return r.json()["choices"][0]["message"]["content"]

# Compare two models on the same prompt
prompt = "Implement Dijkstra's shortest path in TypeScript"
print("=== DeepSeek V4 Flash ===")
print(ask_model("deepseek-v4-flash", prompt, "YOUR_API_KEY"))
print("\n=== Qwen3-Coder-30B ===")
print(ask_model("qwen3-coder-30b", prompt, "YOUR_API_KEY"))

Honestly, that little helper function has saved me hours. I can flip between models in seconds now.

My Honest Takeaways

After running all of this, here's where I landed:

For most coding tasks, go with DeepSeek V4 Flash. At $0.25/M you get a score of 8.7 and an insane value score of 34.8. It's the sweet spot of quality and price for everyday use.

If you specifically need code-focused output, Qwen3-Coder-30B is the winner. It scored highest overall at 8.8, and at $0.35/M it's still cheap.

Reach for DeepSeek-R1 ($2.50/M) only when you're stuck. It scored 9.4, which is the highest of any model I tested. But that premium price means you should treat it like a tutor, not a daily driver.

Don't sleep on Ga-Standard ($0.20/M). If you don't want to think about which model to pick, the smart router handles it. At 42.5 value score, it's mathematically the best deal, though the variability in quality might bug some people.

Avoid paying $3.00/M for Kimi K2.5 unless you have a specific reason. It's good, not great, and way overpriced for what it gives you.

My Final Confession

I had no idea AI coding had come this far. When I started bootcamp, my instructors were warning us about AI and how we needed to "outwork the machines." Six months later, I'm using these models daily to learn faster, debug quicker, and write cleaner code. They're not replacing my brain. They're like having a really patient senior dev sitting next to me who never gets annoyed when I ask the same question twice.

If you want to try these models yourself without setting up ten different accounts and API keys, check out Global API. That's where I've been running most of these tests through. It lets you access a bunch of these models from one place using the same setup I showed in the code snippets above. Honestly, if you're a fellow bootcamp grad or just a curious dev, give