I Tested 10 AI Coding Models On Real Work: Here's What Happened

I Tested 10 AI Coding Models On Real Work: Here's What Happened

look, I gotta be honest with you. I am NOT a pro reviewer. I'm just some dude who builds stuff and ships it, and lately I've been wondering which AI model actually writes the best code without making me pull my hair out. So I did what any sensible person would do - I threw 10 of these models at real coding tasks and watched what happened. Some of them floored me. Some of them made me yell at my screen.

If you're like me and you don't have time to A/B test every API on the planet, here's the whole breakdown. Buckle up.

Why I Even Bothered Doing This

Here's the thing. I'm a solo founder. I write Python for my backend, TypeScript for my frontend, and I occasionally dip into Go when something needs to be FAST. Every month I'm burning cash on AI APIs, and honestly I had no clue if I was overpaying or getting hosed.

The market right now is WILD. You've got massive models that cost an arm and a leg, you've got cheap little ones that sound great on Twitter but produce garbage, and you've got code-specialized stuff that claims to be magic. I just wanted to know which ones were actually worth my limited indie hacker budget.

So I grabbed 10 models, picked 5 tasks that mirror real work I do, and started running them through their paces. No synthetic benchmarks, no vibes-based reviews. Just... actual tasks an actual person would assign.

The 10 Models I Threw At My Problems

Here's the lineup. I'm gonna keep the prices exactly as I got them, because pricing BS is the whole reason I did this:

#	Model	Who Made It	Output $/M	What's It For
1	DeepSeek V4 Flash	DeepSeek	$0.25	General (kinda slays at code)
2	DeepSeek Coder	DeepSeek	$0.25	Code-specialized
3	Qwen3-Coder-30B	Qwen	$0.35	Code-specialized
4	DeepSeek V4 Pro	DeepSeek	$0.78	Premium general
5	DeepSeek-R1	DeepSeek	$2.50	Reasoning (the thinker)
6	Kimi K2.5	Moonshot	$3.00	Premium general
7	GLM-5	Zhipu	$1.92	Premium general
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 thing

I personally leaned a LOT on the cheaper ones because I cannot justify paying $3.00/M when I'm running batch jobs at 3am trying to hit my launch deadline. But I tested them all fairly. The premium models got their shot too.

How I Actually Tested (Not In A Lab, In My Apartment)

So no, I wasn't doing some formal academic thing with control groups and peer review. I just made a Google Sheet, picked 5 tasks that I genuinely needed help with, and ran every model on every task. Twice. To make sure I wasn't seeing things.

The 5 tasks:

1. Recursive flatten - Write a Python function that flattens a nested list of any depth
2. Async bug fix - Fix a JavaScript race condition (the classic one where console.log fires before data arrives)
3. Dijkstra in TypeScript - Yeah, the graph algorithm, with proper types
4. Go code review - Look at some Go I wrote and tell me where I screwed up
5. Express REST endpoint - Build a paginated, filtered user API

Scoring was on a 1-10 scale. I judged on whether the code actually worked, how clean it looked, whether it had docs, and how it handled weird edge cases (because if your AI can't handle empty arrays, what are we even doing here).

The Final Standings (And My Honest Take)

Before I dive deep into tasks, here's the top-level summary:

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*

OK SO. If you look at pure quality scores, DeepSeek-R1 wins at 9.4. But you're paying $2.50/M for that. Is it TEN TIMES better than the $0.25 models? No. It's like... 10% better while costing 10x more. That's a terrible deal for most indie devs.

For actual value (quality per dollar, which is what we care about), DeepSeek V4 Flash is the absolute champion. Score of 8.7 for $0.25/M gives you that 34.8 value score. It was basically right every single time and never made me feel like I was overspending.

The wild card is Ga-Standard at $0.20/M with a variable score. It's a routing model - it picks the best backend for your task. Sometimes it routed me to great models, sometimes it routed to mediocre ones. The asterisk is doing some heavy lifting there, but the price is genuinely unbeatable when it works.

Task 1: The Classic Recursive Flatten

This was the warmup. "Write a Python function to flatten a nested list recursively." Pretty much every model on earth can do this, but I was looking for elegance, type hints, edge case handling. Heres how they did:

Model	Score	My Notes
DeepSeek V4 Flash	9.0	Clean recursive solution with type hints
Qwen3-Coder-30B	9.0	Added iterative alternative + edge cases
DeepSeek Coder	8.5	Correct but verbose
Kimi K2.5	9.0	Most readable, added docstring
DeepSeek-R1	9.5	Included complexity analysis

Honestly? DeepSeek-R1 won this one because it didn't just solve it - it told me WHY it solved it that way, including Big-O analysis and showed me 2-3 different approaches. For a $2.50/M model doing that, I'm not mad at it.

But here's the thing - for THIS specific task, DeepSeek V4 Flash and Qwen3-Coder-30B were tied, and they cost a FRACTION of R1. I would default to those two for everyday utility work.

Task 2: The Async Race Condition (My Nemesis)

This is the one where every junior dev (including past me) loses their mind:

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

Model	Score	My Notes
DeepSeek V4 Flash	9.0	Clear explanation + 3 fix options
Qwen3-Coder-30B	9.0	Added error handling
DeepSeek Coder	8.5	Correct fix, minimal explanation
Qwen3-32B	8.5	Good fix, slightly verbose

This was a TIE between DeepSeek V4 Flash and Qwen3-Coder-30B. Both nailed it. Both gave me async/await rewrites AND .then() chain versions, AND callback versions. They didn't just fix it - they EDUCATED me on why it was broken.

Hunyuan-Turbo here actually scored lower than I expected (didn't even crack the top performers on this task). It fixed the code but the explanation was... thin. Like, it assumed I already knew what a race condition was. Not great for someone learning.

Task 3: Dijkstra in TypeScript (Where Things Got Spicy)

OK this is where I expected the cheap models to start sweating. Dijkstra is real algorithmic work, and I wanted strict TypeScript types. No any allowed. Heres how it went:

The top performer was DeepSeek-R1 with a 9.5. It used a priority queue, it had full type safety, it even added JSDoc comments explaining what each function did. I literally copy-pasted this into my codebase with zero edits. Worth the $2.50/M for that kind of output IF I needed it once a week. I don't, so... ouch, my wallet.

DeepSeek V4 Flash came in clutch here too, scoring like an 8.8 with proper types but slightly less explanation. For most production work? I'd take it. The savings are REAL.

The code-specialized ones like Qwen3-Coder-30B absolutely devoured this task. Like, it KNEW what a Fibonacci heap was being used for. It knew when to use a binary heap vs a sorted array. That kind of domain awareness for $0.35/M is honestly criminal.

Task 4: Go Code Review (My Favorite Test)

I handed them some intentionally sketchy Go code with buffer overflow vibes, a race condition, and a goroutine leak. I wanted to see which models actually CAUGHT all three issues.

Winner here was DeepSeek V4 Pro at 9.0. It didn't just point out the bugs - it explained HOW to fix them with idiomatic Go (channels, mutexes, context cancellation). For a senior engineer's actual workflow, this was unmatched.

Kimi K2.5 also did really well here (8.5+). But again, $3.00/M for code review is gonna hurt unless you're doing critical infrastructure work.

GLM-5 surprised me here. It scored higher on this task than on the algorithm test. Suggests it's more of a "general dev" model than an "algorithmic thinking" model. Useful to know.

Task 5: The Full Express Feature (Real Production Work)

"Build a paginated, filtered user endpoint." This is the bread-and-butter stuff indie hackers do every day. I wanted to see which model could just... do the whole thing without me having to babysit it.

DeepSeek V4 Flash was my favorite here. It gave me:

• The full Express endpoint
• Error handling
• Input validation
• Even a sample test file
• And decent SQL injection protection

For $0.25/M that's INSANE. I would 100% use this as my daily driver for routine feature work.

Qwen3-Coder-30B was a hair better in code structure but cost 40% more. Honestly tied for me on this task.

The expensive models (Kimi K2.5, DeepSeek-R1) overengineered this so badly I had to actually trim stuff OUT. They added OAuth middleware, rate limiting, full logging systems. Cool, but I'm building a side project, not Netflix.

My Real-World Recommendation (The Indie Hacker Special)

After burning through actual cash and several late nights of testing, here's what I'm personally doing:

For 90% of my work: DeepSeek V4 Flash. It nails the quality bar for like 12% of the cost of premium models. I am DEEPLY suspicious of anything more expensive for routine coding tasks.

For dedicated coding work: Qwen3-Coder-30B. It's slightly more expensive but it ACTUALLY knows code idioms across multiple languages. When I'm switching between Python, JS, and Go in the same session, this thing keeps up.

For hard algorithms & system design: DeepSeek-R1. Yes it's $2.50/M. But for the once-a-week "I need to write a distributed lock from scratch" moment, it pays for itself in time saved.

For experimenters: Ga-Standard. That $0.20/M pricing is hard to argue with, but you gotta be ok with variability. Sometimes it's brilliant, sometimes... not. Use when you're prototyping and don't care about consistency.

Avoid Hunyuan-Turbo for now. Sorry Tencent, but it just didn't impress me. Maybe future versions will be better.

How I Actually Wired This Up

Real talk, here's what my setup looks like. I use Global API because their unified endpoint means I don't have to juggle a million API keys and SDKs. Pretty much plug-and-play:

import requests

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

def chat_with_model(model, prompt, temperature=0.2):
    headers = {
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json"
    }

    payload = {
        "model": model,
        "messages": [{"role": "user", "content": prompt}],
        "temperature": temperature,
        "max_tokens": 2000
    }

    response = requests.post(
        f"{BASE_URL}/chat/completions",
        headers=headers,
        json=payload
    )
    return response.json()

result = chat_with_model(
    "deepseek-v4-flash",
    "Write a Python function that debounces a webhook handler. Include type hints and handle edge cases."
)
print(result["choices"][0]["message"]["content"])

Here's another one for code review that I run in CI:

def review_code(code_snippet, language="python"):
    prompt = f"""Review this {language} code for security issues, 
    performance problems, and bugs. Be specific and concise.

    Code:
    ```
{% endraw %}
{language}
    {code_snippet}
{% raw %}

    ```"""

    return chat_with_model("qwen3-coder-30b", prompt)

# run on PRs
review = review_code(my_pull_request_diff, "go")
print(review["choices"][0]["message"]["content"])

Honestly, having ONE endpoint across all these models changed my workflow. I can A/B test outputs in the same