I Ran 10 AI Coding Models Through Real Tasks Heres What Actually Won

I gotta say, i Ran 10 AI Coding Models Through Real Tasks Heres What Actually Won

Okay so heres the thing. I built my last three products almost entirely with AI-generated code, and honestly, I gotta say — the quality jump between what I was using in 2024 and whats available now in 2026 is kinda absurd. Like, night and day.

But heres what bugs me. Every blog post I read about "best coding AI" feels like it was written by someone whos never actually shipped a damn thing. They benchmark stuff on toy problems, show you a LeetCode score, and call it a day. Meanwhile I'm over here at 1am trying to figure out if I should pay $2.50/M tokens for DeepSeek-R1 or just stick with whatever gives me the best bang for my buck.

So I did something probably stupid. I tested TEN models on real coding tasks. Not "write fizzbuzz" — actual work. Python, JavaScript, TypeScript, Go. From "flatten this list" all the way to "build me a REST endpoint with pagination." I'm gonna walk you through what I found, what I use now, and yeah — where you can grab all this stuff without selling a kidney.

Lets get into it.

Why I Even Bothered Doing This

Look, I'm a solo founder. I don't have time to A/B test AI models for two weeks. I should be shipping. But after burning through like $400 in API credits last quarter trying different models, I realized I was being an idiot. You can't just guess.

So I blocked off two weekends, made myself a big spreadsheet, and ran every model through the same gauntlet. Same prompts. Same evaluation rubric. No vibes-based judging — actual scoring on correctness, code quality, documentation, and whether it handles the weird edge cases I throw at it.

The rubric was simple: 1 to 10. Anything below a 7 means I wouldn't ship it to production. Anything above 9 means I'd actually pay extra for it.

The Lineup (And What They Cost Me)

Heres who showed up to fight. I'm gonna keep the prices exactly as I paid them because honestly, pricing is the whole point of this exercise:

Model	Provider	Output $/M	Vibe
DeepSeek V4 Flash	DeepSeek	$0.25	The people's champ
DeepSeek Coder	DeepSeek	$0.25	Code specialist
Qwen3-Coder-30B	Qwen	$0.35	Dedicated code beast
DeepSeek V4 Pro	DeepSeek	$0.78	Flash but smarter
DeepSeek-R1	DeepSeek	$2.50	The thinker
Kimi K2.5	Moonshot	$3.00	Premium everything
GLM-5	Zhipu	$1.92	Underdog
Qwen3-32B	Qwen	$0.28	General workhorse
Hunyuan-Turbo	Tencent	$0.57	Big tech energy
Ga-Standard	GA Routing	$0.20	The wildcard

Ten models. Prices ranging from 20 cents to three bucks per million output tokens. That's a 15x spread. If you're paying the high end and getting the same quality as the low end, you're lighting money on fire.

How I Tested Them

I gave each model the same five tasks. Nothing fancy, just real things I'd actually need to build:

1. The recursive flatten thing — "Write a Python function to flatten a nested list recursively"
2. Bug hunting — Fix a race condition in some async/await JavaScript (classic)
3. Dijkstra's algorithm — In TypeScript, with proper types
4. Code review — Tear apart some Go code for security and perf issues
5. Build something real — A paginated, filtered REST API endpoint in Express.js

I scored each one based on: did it actually work? Was the code readable? Did it document itself? And most importantly — did it handle the weird edge cases that always come up in production?

Pretty simple rubric. Pretty painful to grade. Some of these models generated LONG responses.

The Headline Results (Spoiler Alert)

Alright, before I bore you with task-by-task stuff, heres the TL;DR I wish someone had handed me two months ago:

Qwen3-Coder-30B wins on raw quality (8.8/10) at $0.35/M. DeepSeek V4 Flash wins on value (8.7/10 at $0.25/M = 34.8 value score). DeepSeek-R1 is the heavyweight champ at 9.4/10 but at $2.50/M, you're paying 10x for a 0.7-point quality bump.

Full breakdown:

Rank	Model	Score	Price	Value
🥇	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*

Ga-Standard is the wildcard because it routes to whatever model is best for the task, so the score bounces around. The asterisk is doing some heavy lifting here but honestly that 42.5 value score made me raise my eyebrows.

Task #1: Flatten The Damn List

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

Every model got this right. Like, literally every one. The question became HOW they got it right.

Model	Score	What Happened
DeepSeek V4 Flash	9.0	Clean, type hints, just worked
Qwen3-Coder-30B	9.0	Also gave me an iterative alternative and edge cases
DeepSeek Coder	8.5	Right answer, kinda verbose
Kimi K2.5	9.0	Most readable, proper docstring
DeepSeek-R1	9.5	Included Big-O analysis AND multiple approaches

DeepSeek-R1 won this one. Its output literally had a section titled "Time Complexity: O(n) where n is total number of elements" and then gave me FOUR different ways to write it. Recursive, iterative, using stack, using generator. That's the kind of thinking that justifies the premium price tag for hard problems.

But here's the thing — for a FLATTEN function? I don't need four implementations. I need one that works. DeepSeek V4 Flash gave me a perfect one in like 12 lines. Done. Ship it.

Task #2: The Classic Async Race Condition

I dropped this gem on every model:

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 correctly identified the bug. Not one of them missed it. So scoring came down to how they fixed it.

Model	Score	The Fix
DeepSeek V4 Flash	9.0	Clear explanation + 3 fix options
Qwen3-Coder-30B	9.0	Plus error handling
DeepSeek Coder	8.5	Right fix, minimal explanation
Qwen3-32B	8.5	Solid fix, slightly chatty

This was a tie between DeepSeek V4 Flash and Qwen3-Coder-30B. Both gave me working fixes, both explained why the original was broken, and both gave me options (async/await vs .then chains vs wrapper functions).

Honestly, this is the task where the price difference REALLY matters. Both winners cost me less than $0.40/M output. Kimi K2.5 would have charged me $3.00/M for... the same answer. No thanks.

Task #3: Dijkstra's Algorithm (TypeScript Edition)

This is where things got spicy. TypeScript + algorithms + proper typing = where most models fall apart.

DeepSeek-R1 absolutely demolished this one with a 9.5/10. It gave me a priority queue implementation with full type safety, generic constraints, the whole nine yards. It even explained WHY certain type choices were made.

I gotta say, watching R1 "think" through the algorithm in real-time was kind of mesmerizing. It would output paragraphs of reasoning before writing any code. For an algorithm task like this, that thinking is gold.

But again — $2.50/M. For a Dijkstra implementation I'm gonna use once and stick in a utils file? Hard to justify. For a critical, hard-to-get-right algorithm that runs in production? Suddenly the price feels reasonable.

The Models That Surprised Me

Ga-Standard

This thing is interesting. It doesn't have its own model — it's a routing layer that picks the best model for each task. Sometimes it sends stuff to DeepSeek V4 Flash, sometimes to Qwen3-Coder-30B, sometimes to other models entirely.

That asterisk on the value score (42.5) is because the score is genuinely variable. Some tasks it crushed. Some tasks it felt average. But at $0.20/M, even an "average" answer is a great deal.

Honestly, for indie hackers who just want to stop thinking about which model to use, this is kinda the dream. Set it once, never look back.

GLM-5

I had low expectations for GLM-5 because honestly I hadn't heard much about it. It came in at 8.0/10, which sounds fine until you see the price tag ($1.92/M). For that price, I expect 9+. So it underperformed for me.

But — and heres the thing — it had the most interesting error messages of any model. When it got stuff wrong, it was honest about being uncertain. That's weirdly valuable for a debugging assistant.

Hunyuan-Turbo

The biggest disappointment. Tencent makes good stuff usually, but Hunyuan-Turbo came in at 7.5/10 with a price of $0.57/M. For context, DeepSeek V4 Flash scored 8.7 at $0.25/M. You're paying more for worse code. No thanks.

The Models That Earned My Money

DeepSeek V4 Flash — My Daily Driver

For 90% of my coding tasks, this is what I use now. It's fast, cheap ($0.25/M), and good enough that I rarely have to rewrite anything. The score of 8.7 is genuinely impressive at that price point.

If you're starting out and just need something that works, this is the answer.

Qwen3-Coder-30B — When I Need Code-Specific Smarts

This thing is a code specialist and you can tell. When I asked it for a REST API endpoint with pagination and filtering, it gave me a production-ready Express.js implementation with proper error handling, validation, and even JSDoc comments.

At $0.35/M, it's still super affordable. I use this when I'm building new features and want code I can ship with minimal edits.

DeepSeek-R1 — The Big Guns

For